system

Abstract

The system according to this embodiment aims to streamline the process of questioning and responding to customers when electrical appliances malfunction. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a specification unit, a proposal unit, and an acquisition unit. The reception unit receives inquiries from users. The analysis unit analyzes the content of the inquiries received by the reception unit. The specification unit identifies the cause of the failure based on the results of the analysis by the analysis unit. The proposal unit proposes an appropriate solution based on the cause identified by the specification unit. The acquisition unit automatically acquires and analyzes relevant data based on the solution proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes time and effort to conduct a consultation and response in case of a failure of an electrical appliance, and it is not efficient.

[0005] The system according to the embodiment aims to improve the efficiency of consultation and response in case of a failure of an electrical appliance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, a proposal unit, and an acquisition unit. The reception unit receives inquiries from users. The analysis unit analyzes the content of the inquiries received by the reception unit. The specification unit identifies the cause of the failure based on the results of the analysis by the analysis unit. The proposal unit proposes an appropriate solution based on the cause identified by the specification unit. The acquisition unit automatically acquires and analyzes relevant data based on the solution proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the consultation and response process when electrical appliances malfunction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is a system that takes over the task of questioning and responding to inquiries regarding malfunctions of electrical appliances. This system is intended for use in call centers of electronic equipment manufacturers. Conventionally, questioning and responding to inquiries regarding electrical appliances required specialized knowledge and experience, and the information published on publicly available websites was difficult for users to understand. In the present invention, the AI ​​agent learns knowledge and failure history equivalent to that of a quality control officer, and automatically acquires and analyzes relevant data via an API to take over the task of questioning and responding. For example, when a user inquires about a malfunction of an electrical appliance, the AI ​​agent analyzes the content of the user's inquiry and identifies the cause of the malfunction. Based on the knowledge and failure history equivalent to that of a quality control officer, the AI ​​agent proposes an appropriate response method. Furthermore, the AI ​​agent automatically acquires and analyzes relevant data via an API to provide more accurate information. This mechanism allows users to easily receive questioning and support regarding electrical appliance malfunctions without specialized knowledge. It also reduces the burden on call center staff and enables more efficient responses. This allows the AI ​​agent service to efficiently analyze user inquiries, identify the cause of failures, propose appropriate solutions, and automatically acquire and analyze relevant data.

[0029] The AI ​​agent service according to this embodiment comprises a reception unit, an analysis unit, a identification unit, a proposal unit, and an acquisition unit. The reception unit receives inquiries from users. User inquiries include, but are not limited to, technical problems, product questions, and service inquiries. For example, the reception unit receives the content of inquiries made by users through a web form. The reception unit can also receive inquiries made by telephone or email. Furthermore, the reception unit can also receive inquiries in real time using a chatbot. For example, the reception unit automatically analyzes the content entered by the user in a web form and classifies the inquiry. In the case of telephone inquiries, it uses speech recognition technology to convert the content into text and analyzes it. For email inquiries, it uses natural language processing technology to analyze and classify the content. The chatbot collects and analyzes the inquiry content through dialogue with the user. The analysis unit analyzes the inquiry content received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis unit uses text analysis technology to extract keywords from the inquiry content and identify areas where failures may occur. The analysis unit can also use data mining technology to analyze past inquiry data and identify similar failure cases. Furthermore, the analysis unit can use machine learning algorithms to classify the inquiry content and estimate the cause of the failure. The identification unit identifies the cause of the failure based on the results analyzed by the analysis unit. The cause of the failure may include, but is not limited to, hardware failure, software bugs, or user error. For example, the identification unit identifies areas with a high probability of failure based on keywords extracted by the analysis unit. The identification unit can also use data mining technology to refer to past failure data and identify the cause of the failure. Furthermore, the identification unit can estimate the cause of the failure using machine learning algorithms. The proposal unit proposes appropriate countermeasures based on the cause identified by the identification unit.The response methods include, but are not limited to, repair procedures, software updates, and user instructions. For example, the suggestion unit proposes repair procedures depending on the cause of the malfunction. The suggestion unit can also propose update procedures if the cause is a software bug. Furthermore, the suggestion unit can also provide instructions on the correct operation method if the cause is a user error. The acquisition unit automatically acquires and analyzes relevant data based on the response methods proposed by the suggestion unit. The relevant data includes, but is not limited to, log data, sensor data, and user operation history. For example, the acquisition unit acquires and analyzes log data via an API. The acquisition unit can also collect and analyze sensor data in real time. Furthermore, the acquisition unit can refer to the user's operation history and collect data to identify the cause of the malfunction. As a result, the AI ​​agent service according to the embodiment can efficiently analyze user inquiries, identify the cause of malfunctions, propose appropriate response methods, and automatically acquire and analyze relevant data.

[0030] The reception department receives inquiries from users. These inquiries include, but are not limited to, technical issues, product questions, and service inquiries. For example, the reception department receives inquiries submitted by users via web forms. It can also receive inquiries made by telephone or email. Furthermore, the reception department can receive inquiries in real time using a chatbot. For example, the reception department automatically analyzes the content entered by users in web forms and categorizes the inquiries. For telephone inquiries, it uses speech recognition technology to transcribe and analyze the content. For email inquiries, it uses natural language processing technology to analyze and categorize the content. The chatbot collects and analyzes inquiry content through interaction with users. The reception department provides multiple channels to enhance user convenience when making inquiries. For example, the web form is designed with input fields that allow users to easily enter their inquiries and ensures that all necessary information is collected. For telephone inquiries, speech recognition technology is used to accurately transcribe the user's speech and send it to the analysis department. For email inquiries, natural language processing technology is used to extract important keywords and phrases from the email body and quickly classify the inquiry content. The chatbot collects and analyzes inquiry content in real time through interaction with the user. The chatbot not only responds immediately to the user's questions and provides necessary information, but also automatically classifies the inquiry content and sends it to the analysis department. This allows the reception department to efficiently receive inquiries from users and quickly hand them over to the analysis department. Furthermore, the reception department can manage the user's inquiry history and refer to past inquiries to provide faster and more appropriate responses. For example, if a similar inquiry has been made in the past, the history can be referenced to quickly suggest a solution. As a result, the reception department can respond to user inquiries quickly and accurately, improving user satisfaction.

[0031] The analysis department analyzes the content of inquiries received by the reception department. Analysis is performed using methods such as text analysis, data mining, and machine learning algorithms, but is not limited to these examples. For instance, the analysis department may use text analysis techniques to extract keywords from inquiries and identify potential fault locations. It can also use data mining techniques to analyze past inquiry data and identify similar failure cases. Furthermore, it can use machine learning algorithms to classify inquiries and estimate the cause of failures. The analysis department utilizes AI technology to quickly and accurately analyze received inquiries. For example, it uses text analysis techniques to extract important keywords and phrases from inquiries and use them to identify potential fault locations. By using data mining techniques to analyze past inquiry data and identify similar failure cases, it can quickly find solutions. By using machine learning algorithms to classify inquiries and estimate the cause of failures, it can propose more accurate solutions. The analysis department combines these technologies to analyze inquiries from multiple perspectives and identify the cause of failures. For example, based on keywords extracted using text analysis technology, past inquiry data is analyzed using data mining technology to identify similar failure cases. Furthermore, by classifying the inquiry content and estimating the cause of the failure using machine learning algorithms, more accurate solutions can be proposed. The analysis unit utilizes these technologies to quickly and accurately analyze received inquiries and identify the cause of the failure. As a result, the analysis unit can respond quickly and accurately to user inquiries and improve user satisfaction.

[0032] The identification unit identifies the cause of the failure based on the results of the analysis unit. The causes of failure include, but are not limited to, hardware failures, software bugs, and user errors. For example, the identification unit identifies areas with a high probability of failure based on keywords extracted by the analysis unit. The identification unit can also identify the cause of failure by referring to past failure data using data mining techniques. Furthermore, the identification unit can estimate the cause of failure using machine learning algorithms. The identification unit quickly and accurately identifies the cause of the failure based on the results of the analysis unit. For example, it identifies areas with a high probability of failure based on keywords extracted by the analysis unit. By referring to past failure data using data mining techniques and identifying similar failure cases, the cause of failure can be quickly identified. By estimating the cause of failure using machine learning algorithms, more accurate countermeasures can be proposed. The identification unit utilizes these technologies to identify the cause of failure from multiple perspectives. For example, it analyzes past failure data using data mining techniques based on keywords extracted by the analysis unit and identifies similar failure cases. Furthermore, by estimating the cause of failure using machine learning algorithms, more accurate countermeasures can be proposed. By utilizing these technologies, the specialized unit can quickly and accurately identify the cause of a malfunction and respond promptly and accurately to user inquiries. This allows the specialized unit to respond quickly and accurately to user inquiries, thereby improving user satisfaction.

[0033] The proposal department proposes appropriate solutions based on the cause identified by the specific department. These solutions include, but are not limited to, repair procedures, software updates, and user instructions. For example, the proposal department may propose repair procedures depending on the cause of the malfunction. It may also propose update procedures if the cause is a software bug. Furthermore, it may provide instructions on the correct operation method if the cause is user error. The proposal department quickly and accurately proposes appropriate solutions based on the cause identified by the specific department. For example, if the cause of the malfunction is a hardware failure, it proposes repair procedures. If the cause is a software bug, it proposes update procedures. If the cause is user error, it provides instructions on the correct operation method. By quickly and accurately proposing these solutions, the proposal department can quickly resolve user problems. When proposing these solutions, the proposal department can customize them according to the user's skill level and situation. For example, it can provide detailed procedures for novice users and concise procedures for advanced users. The proposal department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to quickly and accurately resolve user problems and improve user satisfaction.

[0034] The acquisition unit automatically acquires and analyzes relevant data based on the proposed solution by the proposal unit. Relevant data includes, but is not limited to, log data, sensor data, and user operation history. For example, the acquisition unit acquires and analyzes log data via an API. The acquisition unit can also collect and analyze sensor data in real time. Furthermore, the acquisition unit can refer to user operation history to collect data for identifying the cause of a malfunction. The acquisition unit quickly and accurately acquires and analyzes relevant data based on the proposed solution by the proposal unit. For example, by acquiring and analyzing log data via an API, it collects data for identifying the cause of a malfunction. By collecting and analyzing sensor data in real time, it grasps the progress of a malfunction. By referring to user operation history and collecting data for identifying the cause of a malfunction, it can propose a more accurate solution. By quickly and accurately acquiring and analyzing this data, the acquisition unit can quickly resolve user problems. When acquiring this data, the acquisition unit can take measures to ensure the accuracy and reliability of the data. For example, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions become possible. Furthermore, the data acquisition unit centrally manages the collected data and can collaborate with other systems and departments as needed. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall performance of the system.

[0035] The reception desk can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the type of inquiry the user has frequently made in the past. The reception desk can also prioritize suggesting inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest inquiry methods to be used during specific time periods based on the user's past inquiry history. This allows the reception desk to select the most suitable reception method by analyzing the user's past inquiry history. Past inquiry history includes, but is not limited to, the content of the inquiry, the date and time, and the outcome of the response. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past inquiry history data into a generating AI and have the generating AI select the most suitable reception method.

[0036] The reception unit can filter inquiries based on the user's current usage and areas of interest. For example, the reception unit can automatically retrieve information about the electrical appliances the user is currently using and prioritize displaying relevant inquiries. The reception unit can also filter and display relevant inquiries based on the user's areas of interest. Furthermore, the reception unit can suggest the most appropriate inquiry method based on the user's current usage. This allows for the priority display of relevant inquiries by filtering based on the user's current usage and areas of interest. Current usage includes, but is not limited to, the status of the devices and applications being used. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user usage data into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. The reception desk can also suggest the most appropriate inquiry method based on the user's geographical location information. Furthermore, if the user is on the move, the reception desk can prioritize receiving relevant inquiries based on their current location. This allows for the prioritization of highly relevant inquiries by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0038] The reception desk can analyze a user's social media activity when receiving an inquiry and accept relevant inquiries. For example, the reception desk can analyze a user's current interests from their social media activity and prioritize accepting relevant inquiries. The reception desk can also suggest the most appropriate inquiry method based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and filter and display relevant inquiries. This allows for the prioritization of relevant inquiries by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, follower count, and engagement. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant inquiries.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, the analysis unit will perform a detailed analysis for inquiries of high importance. It can also perform a simplified analysis for inquiries of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry content. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of the inquiry content. The importance of the inquiry content includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input inquiry importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply the optimal analysis algorithm depending on the type of electrical appliance. It can also apply different analysis algorithms depending on the type of failure. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the content of the inquiry. By applying different analysis algorithms depending on the category of the inquiry, more appropriate analysis results can be provided. The categories of inquiries include, but are not limited to, technical problems, service-related questions, and product-related inquiries. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry category data into a generating AI and have the generating AI select an analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the inquiry. For example, the analysis unit will prioritize the analysis of urgent inquiries. The analysis unit can also perform analysis on regular inquiries with normal priority. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date of the inquiry. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission date of the inquiry. The submission date includes, but is not limited to, the submission date and time, or the time elapsed since submission. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant queries. It can also analyze less relevant queries in the normal order. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of queries. By adjusting the order of analysis based on the relevance of queries, it is possible to provide more appropriate analysis results. The relevance of queries includes, but is not limited to, similarity of content, scope of impact, and user attributes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The identification unit can improve the accuracy of its identification by referring to past failure data when identifying the cause of a failure. For example, the identification unit can select the optimal identification method based on past failure data. The identification unit can also improve the accuracy of its identification by referring to past failure data. Furthermore, the identification unit can improve the accuracy of its identification by analyzing past failure data. This allows for improved accuracy of identification by referring to past failure data. Past failure data includes, but is not limited to, the type of failure, frequency of occurrence, and response results. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past failure data into a generating AI and have the generating AI perform the improvement of its identification accuracy.

[0044] The identification unit can perform the identification of the cause of a failure by considering the attribute information of the person submitting the inquiry. For example, the identification unit can select the optimal identification method based on the age and gender of the person submitting the inquiry. The identification unit can also improve the accuracy of the identification based on the usage status of the person submitting the inquiry. Furthermore, the identification unit can analyze the attribute information of the person submitting the inquiry to improve the accuracy of the identification. Thus, by considering the attribute information of the person submitting the inquiry, the accuracy of the identification can be improved. The attribute information of the submitter includes, but is not limited to, age, gender, occupation, and device used. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the attribute information of the submitter into a generating AI and have the generating AI perform the improvement of the accuracy of the identification.

[0045] The identification unit can perform fault identification while considering the geographical distribution of inquiries. For example, the identification unit can prioritize identifying fault causes occurring in a specific region. The identification unit can also select the optimal identification method based on the geographical distribution. Furthermore, the identification unit can analyze the geographical distribution to improve the accuracy of identification. This improves the accuracy of identification by considering the geographical distribution of inquiries. Geographical distribution includes, but is not limited to, the number of inquiries by region, geographical clusters, etc. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0046] The identification unit can improve the accuracy of its identification of the cause of failure by referring to relevant literature. For example, the identification unit can select the optimal identification method based on the relevant literature. The identification unit can also improve the accuracy of its identification by referring to relevant literature. Furthermore, the identification unit can also improve the accuracy of its identification by analyzing the relevant literature. Thus, the accuracy of identification can be improved by referring to relevant literature. Relevant literature includes, but is not limited to, technical papers, patent documents, and industry reports. Some or all of the above processing in the identification unit may be performed using, for example, AI, or not using AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0047] The proposal unit can adjust the level of detail of its proposals based on the severity of the failure cause. For example, the proposal unit can provide detailed proposals for high-severity failure causes, and simplified proposals for low-severity failure causes. Furthermore, the proposal unit can determine the priority of proposals according to the severity of the failure cause. This allows for the provision of more appropriate proposal results by adjusting the level of detail of proposals based on the severity of the failure cause. The severity of a failure cause includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input failure cause severity data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the failure cause when making a proposal. For example, the proposal unit can apply the optimal proposal algorithm depending on the type of electrical appliance. It can also apply different proposal algorithms depending on the type of failure. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the content of the inquiry. By applying different proposal algorithms depending on the category of the failure cause, it is possible to provide more appropriate proposal results. The categories of failure causes include, but are not limited to, hardware failures, software bugs, and user errors. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input failure cause category data into a generating AI and have the generating AI select a proposal algorithm.

[0049] The proposal department can determine the priority of proposals based on the timing of failure cause submissions. For example, the proposal department will prioritize proposals for urgent failure causes. It can also prioritize proposals for normal failure causes. Furthermore, the proposal department can adjust the priority of proposals according to the timing of failure cause submissions. This allows for the provision of more appropriate proposal results by determining the priority of proposals based on the timing of failure cause submissions. The submission timing includes, but is not limited to, the submission date and time, and the time elapsed since submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input failure cause submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the failure causes. For example, the proposal unit can prioritize proposals for highly relevant failure causes. It can also propose solutions in the normal order for less relevant failure causes. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the failure causes. By adjusting the order of proposals based on the relevance of the failure causes, it is possible to provide more appropriate proposal results. The relevance of failure causes includes, but is not limited to, similarity of content, scope of impact, and user attributes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input failure cause relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0051] The acquisition unit can improve the accuracy of acquisition by referring to past data when acquiring relevant data. For example, the acquisition unit can select the optimal data acquisition method based on past data. The acquisition unit can also improve the accuracy of acquisition by referring to past data. Furthermore, the acquisition unit can analyze past data to improve the accuracy of acquisition. In this way, the accuracy of acquisition can be improved by referring to past data. Past data includes, but is not limited to, past inquiry history, failure data, and user operation history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input past data into a generating AI and have the generating AI perform the task of improving the accuracy of acquisition.

[0052] The acquisition unit can acquire relevant data while considering the attribute information of the inquiry submitter. For example, the acquisition unit can select the optimal data acquisition method based on the inquiry submitter's age and gender. The acquisition unit can also improve the accuracy of acquisition based on the inquiry submitter's usage. Furthermore, the acquisition unit can analyze the inquiry submitter's attribute information to improve the accuracy of acquisition. Thus, by considering the inquiry submitter's attribute information, the accuracy of acquisition can be improved. The submitter's attribute information includes, but is not limited to, age, gender, occupation, and device used. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the submitter's attribute information into a generating AI and have the generating AI perform the improvement of acquisition accuracy.

[0053] The data acquisition unit can acquire relevant data while considering the geographical distribution of queries. For example, the data acquisition unit can prioritize the acquisition of data occurring in a specific region. The data acquisition unit can also select the optimal data acquisition method based on the geographical distribution. Furthermore, the data acquisition unit can analyze the geographical distribution to improve the accuracy of the acquisition. This improves the accuracy of the acquisition by considering the geographical distribution of queries. Geographical distribution includes, but is not limited to, the number of queries by region and geographical clusters. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the acquisition.

[0054] The acquisition unit can improve the accuracy of data acquisition by referring to relevant literature when acquiring relevant data. For example, the acquisition unit can select the optimal data acquisition method based on relevant literature. The acquisition unit can also improve the accuracy of data acquisition by referring to relevant literature. Furthermore, the acquisition unit can analyze relevant literature to improve the accuracy of data acquisition. Thus, the accuracy of data acquisition can be improved by referring to relevant literature. Relevant literature includes, but is not limited to, technical papers, patent documents, and industry reports. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of data acquisition.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, it can automatically display as suggestions the types of inquiries the user has frequently made in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest inquiry methods to be used during specific time periods based on the user's past inquiry history. In this way, the reception department can select the most suitable reception method by analyzing the user's past inquiry history. Past inquiry history includes, but is not limited to, the content of inquiries, the date and time, and the outcome of the response. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past inquiry history data into a generating AI and have the generating AI select the most suitable reception method.

[0057] The reception unit can filter inquiries based on the user's current usage and areas of interest. For example, it can automatically retrieve information about the electrical appliances the user is currently using and prioritize displaying relevant inquiries. It can also filter and display relevant inquiries based on the user's areas of interest. Furthermore, it can suggest the most appropriate inquiry method based on the user's current usage. This allows for the priority display of relevant inquiries by filtering based on the user's current usage and areas of interest. Current usage includes, but is not limited to, the status of the devices and applications being used. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user usage data into a generating AI and have the generating AI perform the filtering.

[0058] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize inquiries related to that region. It can also suggest the most appropriate inquiry method based on the user's geographical location. Furthermore, if a user is on the move, it can prioritize inquiries that are relevant based on their current location. In this way, by considering the user's geographical location, it is possible to prioritize receiving inquiries that are highly relevant. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, it can perform a detailed analysis on high-importance inquiries and a simplified analysis on low-importance inquiries. Furthermore, it can determine the priority of the analysis according to the importance of the inquiry content. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the inquiry content. The importance of the inquiry content includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0060] The identification unit can improve the accuracy of its identification process by referring to past failure data when identifying the cause of a failure. For example, it can select the optimal identification method based on past failure data. It can also improve the accuracy of its identification process by referring to past failure data. Furthermore, it can analyze past failure data to improve the accuracy of its identification process. In this way, the accuracy of its identification process can be improved by referring to past failure data. Past failure data includes, but is not limited to, the type of failure, the frequency of occurrence, and the results of the countermeasures taken. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past failure data into a generating AI and have the generating AI perform the task of improving the accuracy of its identification process.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives inquiries from users. These inquiries include technical issues, product questions, and service inquiries. The reception desk can receive inquiries via web forms, telephone, email, and chatbots. For example, it can automatically analyze the content entered in web forms and categorize the inquiries. Telephone inquiries are converted to text using speech recognition technology and then analyzed. Email inquiries are analyzed and categorized using natural language processing technology. The chatbot collects and analyzes inquiry content through interaction with the user. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis techniques can be used to extract keywords from the inquiry and identify areas where malfunctions may occur. Data mining techniques can also be used to analyze past inquiry data and identify similar failure cases. Machine learning algorithms can also be used to classify the inquiry and estimate the cause of the failure. Step 3: The identification unit identifies the cause of the failure based on the results analyzed by the analysis unit. The causes of failure include hardware failure, software bugs, and user error. For example, based on keywords extracted by the analysis unit, the unit identifies areas that are likely to fail. It is also possible to identify the cause of failure by referring to past failure data using data mining techniques. It is also possible to estimate the cause of failure using machine learning algorithms. Step 4: The proposing department proposes appropriate countermeasures based on the cause identified by the specific department. These countermeasures may include repair procedures, software updates, and user instructions. For example, depending on the cause of the malfunction, a repair procedure may be proposed. If the cause is a software bug, an update procedure may also be proposed. If the cause is user error, instructions on the correct operation method may also be provided. Step 5: The acquisition unit automatically acquires and analyzes relevant data based on the proposed solution proposed by the proposal unit. Relevant data includes log data, sensor data, and user operation history. For example, log data can be acquired and analyzed via an API. Sensor data can also be collected and analyzed in real time. User operation history can also be referenced to collect data for identifying the cause of a malfunction.

[0063] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system that takes over the task of questioning and responding to inquiries regarding malfunctions of electrical appliances. This system is intended for use in call centers of electronic equipment manufacturers. Conventionally, questioning and responding to inquiries regarding electrical appliances required specialized knowledge and experience, and the information published on publicly available websites was difficult for users to understand. In the present invention, the AI ​​agent learns knowledge and failure history equivalent to that of a quality control officer, and automatically acquires and analyzes relevant data via an API to take over the task of questioning and responding. For example, when a user inquires about a malfunction of an electrical appliance, the AI ​​agent analyzes the content of the user's inquiry and identifies the cause of the malfunction. Based on the knowledge and failure history equivalent to that of a quality control officer, the AI ​​agent proposes an appropriate response method. Furthermore, the AI ​​agent automatically acquires and analyzes relevant data via an API to provide more accurate information. This mechanism allows users to easily receive questioning and support regarding electrical appliance malfunctions without specialized knowledge. It also reduces the burden on call center staff and enables more efficient responses. This allows the AI ​​agent service to efficiently analyze user inquiries, identify the cause of failures, propose appropriate solutions, and automatically acquire and analyze relevant data.

[0064] The AI ​​agent service according to this embodiment comprises a reception unit, an analysis unit, a identification unit, a proposal unit, and an acquisition unit. The reception unit receives inquiries from users. User inquiries include, but are not limited to, technical problems, product questions, and service inquiries. For example, the reception unit receives the content of inquiries made by users through a web form. The reception unit can also receive inquiries made by telephone or email. Furthermore, the reception unit can also receive inquiries in real time using a chatbot. For example, the reception unit automatically analyzes the content entered by the user in a web form and classifies the inquiry. In the case of telephone inquiries, it uses speech recognition technology to convert the content into text and analyzes it. For email inquiries, it uses natural language processing technology to analyze and classify the content. The chatbot collects and analyzes the inquiry content through dialogue with the user. The analysis unit analyzes the inquiry content received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis unit uses text analysis technology to extract keywords from the inquiry content and identify areas where failures may occur. The analysis unit can also use data mining technology to analyze past inquiry data and identify similar failure cases. Furthermore, the analysis unit can use machine learning algorithms to classify the inquiry content and estimate the cause of the failure. The identification unit identifies the cause of the failure based on the results analyzed by the analysis unit. The cause of the failure may include, but is not limited to, hardware failure, software bugs, or user error. For example, the identification unit identifies areas with a high probability of failure based on keywords extracted by the analysis unit. The identification unit can also use data mining technology to refer to past failure data and identify the cause of the failure. Furthermore, the identification unit can estimate the cause of the failure using machine learning algorithms. The proposal unit proposes appropriate countermeasures based on the cause identified by the identification unit.The response methods include, but are not limited to, repair procedures, software updates, and user instructions. For example, the suggestion unit proposes repair procedures depending on the cause of the malfunction. The suggestion unit can also propose update procedures if the cause is a software bug. Furthermore, the suggestion unit can also provide instructions on the correct operation method if the cause is a user error. The acquisition unit automatically acquires and analyzes relevant data based on the response methods proposed by the suggestion unit. The relevant data includes, but is not limited to, log data, sensor data, and user operation history. For example, the acquisition unit acquires and analyzes log data via an API. The acquisition unit can also collect and analyze sensor data in real time. Furthermore, the acquisition unit can refer to the user's operation history and collect data to identify the cause of the malfunction. As a result, the AI ​​agent service according to the embodiment can efficiently analyze user inquiries, identify the cause of malfunctions, propose appropriate response methods, and automatically acquire and analyze relevant data.

[0065] The reception department receives inquiries from users. These inquiries include, but are not limited to, technical issues, product questions, and service inquiries. For example, the reception department receives inquiries submitted by users via web forms. It can also receive inquiries made by telephone or email. Furthermore, the reception department can receive inquiries in real time using a chatbot. For example, the reception department automatically analyzes the content entered by users in web forms and categorizes the inquiries. For telephone inquiries, it uses speech recognition technology to transcribe and analyze the content. For email inquiries, it uses natural language processing technology to analyze and categorize the content. The chatbot collects and analyzes inquiry content through interaction with users. The reception department provides multiple channels to enhance user convenience when making inquiries. For example, the web form is designed with input fields that allow users to easily enter their inquiries and ensures that all necessary information is collected. For telephone inquiries, speech recognition technology is used to accurately transcribe the user's speech and send it to the analysis department. For email inquiries, natural language processing technology is used to extract important keywords and phrases from the email body and quickly classify the inquiry content. The chatbot collects and analyzes inquiry content in real time through interaction with the user. The chatbot not only responds immediately to the user's questions and provides necessary information, but also automatically classifies the inquiry content and sends it to the analysis department. This allows the reception department to efficiently receive inquiries from users and quickly hand them over to the analysis department. Furthermore, the reception department can manage the user's inquiry history and refer to past inquiries to provide faster and more appropriate responses. For example, if a similar inquiry has been made in the past, the history can be referenced to quickly suggest a solution. As a result, the reception department can respond to user inquiries quickly and accurately, improving user satisfaction.

[0066] The analysis department analyzes the content of inquiries received by the reception department. Analysis is performed using methods such as text analysis, data mining, and machine learning algorithms, but is not limited to these examples. For instance, the analysis department may use text analysis techniques to extract keywords from inquiries and identify potential fault locations. It can also use data mining techniques to analyze past inquiry data and identify similar failure cases. Furthermore, it can use machine learning algorithms to classify inquiries and estimate the cause of failures. The analysis department utilizes AI technology to quickly and accurately analyze received inquiries. For example, it uses text analysis techniques to extract important keywords and phrases from inquiries and use them to identify potential fault locations. By using data mining techniques to analyze past inquiry data and identify similar failure cases, it can quickly find solutions. By using machine learning algorithms to classify inquiries and estimate the cause of failures, it can propose more accurate solutions. The analysis department combines these technologies to analyze inquiries from multiple perspectives and identify the cause of failures. For example, based on keywords extracted using text analysis technology, past inquiry data is analyzed using data mining technology to identify similar failure cases. Furthermore, by classifying the inquiry content and estimating the cause of the failure using machine learning algorithms, more accurate solutions can be proposed. The analysis unit utilizes these technologies to quickly and accurately analyze received inquiries and identify the cause of the failure. As a result, the analysis unit can respond quickly and accurately to user inquiries and improve user satisfaction.

[0067] The identification unit identifies the cause of the failure based on the results of the analysis unit. The causes of failure include, but are not limited to, hardware failures, software bugs, and user errors. For example, the identification unit identifies areas with a high probability of failure based on keywords extracted by the analysis unit. The identification unit can also identify the cause of failure by referring to past failure data using data mining techniques. Furthermore, the identification unit can estimate the cause of failure using machine learning algorithms. The identification unit quickly and accurately identifies the cause of the failure based on the results of the analysis unit. For example, it identifies areas with a high probability of failure based on keywords extracted by the analysis unit. By referring to past failure data using data mining techniques and identifying similar failure cases, the cause of failure can be quickly identified. By estimating the cause of failure using machine learning algorithms, more accurate countermeasures can be proposed. The identification unit utilizes these technologies to identify the cause of failure from multiple perspectives. For example, it analyzes past failure data using data mining techniques based on keywords extracted by the analysis unit and identifies similar failure cases. Furthermore, by estimating the cause of failure using machine learning algorithms, more accurate countermeasures can be proposed. By utilizing these technologies, the specialized unit can quickly and accurately identify the cause of a malfunction and respond promptly and accurately to user inquiries. This allows the specialized unit to respond quickly and accurately to user inquiries, thereby improving user satisfaction.

[0068] The proposal department proposes appropriate solutions based on the cause identified by the specific department. These solutions include, but are not limited to, repair procedures, software updates, and user instructions. For example, the proposal department may propose repair procedures depending on the cause of the malfunction. It may also propose update procedures if the cause is a software bug. Furthermore, it may provide instructions on the correct operation method if the cause is user error. The proposal department quickly and accurately proposes appropriate solutions based on the cause identified by the specific department. For example, if the cause of the malfunction is a hardware failure, it proposes repair procedures. If the cause is a software bug, it proposes update procedures. If the cause is user error, it provides instructions on the correct operation method. By quickly and accurately proposing these solutions, the proposal department can quickly resolve user problems. When proposing these solutions, the proposal department can customize them according to the user's skill level and situation. For example, it can provide detailed procedures for novice users and concise procedures for advanced users. The proposal department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to quickly and accurately resolve user problems and improve user satisfaction.

[0069] The acquisition unit automatically acquires and analyzes relevant data based on the proposed solution by the proposal unit. Relevant data includes, but is not limited to, log data, sensor data, and user operation history. For example, the acquisition unit acquires and analyzes log data via an API. The acquisition unit can also collect and analyze sensor data in real time. Furthermore, the acquisition unit can refer to user operation history to collect data for identifying the cause of a malfunction. The acquisition unit quickly and accurately acquires and analyzes relevant data based on the proposed solution by the proposal unit. For example, by acquiring and analyzing log data via an API, it collects data for identifying the cause of a malfunction. By collecting and analyzing sensor data in real time, it grasps the progress of a malfunction. By referring to user operation history and collecting data for identifying the cause of a malfunction, it can propose a more accurate solution. By quickly and accurately acquiring and analyzing this data, the acquisition unit can quickly resolve user problems. When acquiring this data, the acquisition unit can take measures to ensure the accuracy and reliability of the data. For example, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions become possible. Furthermore, the data acquisition unit centrally manages the collected data and can collaborate with other systems and departments as needed. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall performance of the system.

[0070] The reception desk can estimate the user's emotions and adjust the way inquiries are handled based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and handle the inquiry quickly. This allows for more appropriate responses by adjusting the way inquiries are handled according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the type of inquiry the user has frequently made in the past. The reception desk can also prioritize suggesting inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest inquiry methods to be used during specific time periods based on the user's past inquiry history. This allows the reception desk to select the most suitable reception method by analyzing the user's past inquiry history. Past inquiry history includes, but is not limited to, the content of the inquiry, the date and time, and the outcome of the response. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past inquiry history data into a generating AI and have the generating AI select the most suitable reception method.

[0072] The reception unit can filter inquiries based on the user's current usage and areas of interest. For example, the reception unit can automatically retrieve information about the electrical appliances the user is currently using and prioritize displaying relevant inquiries. The reception unit can also filter and display relevant inquiries based on the user's areas of interest. Furthermore, the reception unit can suggest the most appropriate inquiry method based on the user's current usage. This allows for the priority display of relevant inquiries by filtering based on the user's current usage and areas of interest. Current usage includes, but is not limited to, the status of the devices and applications being used. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user usage data into a generating AI and have the generating AI perform the filtering.

[0073] The reception desk can estimate the user's emotions and determine the priority of inquiries based on the estimated emotions. For example, if the user has an urgent inquiry, the reception desk will prioritize it. If the user is relaxed, the reception desk can also prioritize it at the normal priority level. Furthermore, if the user is stressed, the reception desk can respond quickly. This allows for more appropriate responses by prioritizing inquiries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. The reception desk can also suggest the most appropriate inquiry method based on the user's geographical location information. Furthermore, if the user is on the move, the reception desk can prioritize receiving relevant inquiries based on their current location. This allows for the prioritization of highly relevant inquiries by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0075] The reception desk can analyze a user's social media activity when receiving an inquiry and accept relevant inquiries. For example, the reception desk can analyze a user's current interests from their social media activity and prioritize accepting relevant inquiries. The reception desk can also suggest the most appropriate inquiry method based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and filter and display relevant inquiries. This allows for the prioritization of relevant inquiries by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, follower count, and engagement. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant inquiries.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, the analysis unit will perform a detailed analysis for inquiries of high importance. It can also perform a simplified analysis for inquiries of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry content. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of the inquiry content. The importance of the inquiry content includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input inquiry importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply the optimal analysis algorithm depending on the type of electrical appliance. It can also apply different analysis algorithms depending on the type of failure. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the content of the inquiry. By applying different analysis algorithms depending on the category of the inquiry, more appropriate analysis results can be provided. The categories of inquiries include, but are not limited to, technical problems, service-related questions, and product-related inquiries. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry category data into a generating AI and have the generating AI select an analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can determine the priority of analysis based on the submission date of the inquiry. For example, the analysis unit will prioritize the analysis of urgent inquiries. The analysis unit can also perform analysis on regular inquiries with normal priority. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date of the inquiry. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission date of the inquiry. The submission date includes, but is not limited to, the submission date and time, or the time elapsed since submission. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant queries. It can also analyze less relevant queries in the normal order. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of queries. By adjusting the order of analysis based on the relevance of queries, it is possible to provide more appropriate analysis results. The relevance of queries includes, but is not limited to, similarity of content, scope of impact, and user attributes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0082] The identification unit can estimate the user's emotions and adjust the method for identifying the cause of failure based on the estimated user emotions. For example, if the user is tense, the identification unit can provide a simple and easily understandable identification method. If the user is relaxed, the identification unit can also provide a more detailed identification method. Furthermore, if the user is in a hurry, the identification unit can provide a concise identification method. By adjusting the method for identifying the cause of failure according to the user's emotions, more appropriate identification results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not using AI. For example, the identification unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0083] The identification unit can improve the accuracy of its identification by referring to past failure data when identifying the cause of a failure. For example, the identification unit can select the optimal identification method based on past failure data. The identification unit can also improve the accuracy of its identification by referring to past failure data. Furthermore, the identification unit can improve the accuracy of its identification by analyzing past failure data. This allows for improved accuracy of identification by referring to past failure data. Past failure data includes, but is not limited to, the type of failure, frequency of occurrence, and response results. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past failure data into a generating AI and have the generating AI perform the improvement of its identification accuracy.

[0084] The identification unit can perform the identification of the cause of a failure by considering the attribute information of the person submitting the inquiry. For example, the identification unit can select the optimal identification method based on the age and gender of the person submitting the inquiry. The identification unit can also improve the accuracy of the identification based on the usage status of the person submitting the inquiry. Furthermore, the identification unit can analyze the attribute information of the person submitting the inquiry to improve the accuracy of the identification. Thus, by considering the attribute information of the person submitting the inquiry, the accuracy of the identification can be improved. The attribute information of the submitter includes, but is not limited to, age, gender, occupation, and device used. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the attribute information of the submitter into a generating AI and have the generating AI perform the improvement of the accuracy of the identification.

[0085] The identification unit can estimate the user's emotions and adjust the display method of the identified cause of failure based on the estimated user emotions. For example, if the user is tense, the identification unit can provide a simple and highly visible display method. If the user is relaxed, the identification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a concise display method. By adjusting the display method of the cause of failure according to the user's emotions, more appropriate display results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0086] The identification unit can perform fault identification while considering the geographical distribution of inquiries. For example, the identification unit can prioritize identifying fault causes occurring in a specific region. The identification unit can also select the optimal identification method based on the geographical distribution. Furthermore, the identification unit can analyze the geographical distribution to improve the accuracy of identification. This improves the accuracy of identification by considering the geographical distribution of inquiries. Geographical distribution includes, but is not limited to, the number of inquiries by region, geographical clusters, etc. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0087] The identification unit can improve the accuracy of its identification of the cause of failure by referring to relevant literature. For example, the identification unit can select the optimal identification method based on the relevant literature. The identification unit can also improve the accuracy of its identification by referring to relevant literature. Furthermore, the identification unit can also improve the accuracy of its identification by analyzing the relevant literature. Thus, the accuracy of identification can be improved by referring to relevant literature. Relevant literature includes, but is not limited to, technical papers, patent documents, and industry reports. Some or all of the above processing in the identification unit may be performed using, for example, AI, or not using AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0088] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, it can also provide a more detailed suggestion. Furthermore, if the user is in a hurry, it can provide a concise suggestion. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0089] The proposal unit can adjust the level of detail of its proposals based on the severity of the failure cause. For example, the proposal unit can provide detailed proposals for high-severity failure causes, and simplified proposals for low-severity failure causes. Furthermore, the proposal unit can determine the priority of proposals according to the severity of the failure cause. This allows for the provision of more appropriate proposal results by adjusting the level of detail of proposals based on the severity of the failure cause. The severity of a failure cause includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input failure cause severity data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0090] The proposal unit can apply different proposal algorithms depending on the category of the failure cause when making a proposal. For example, the proposal unit can apply the optimal proposal algorithm depending on the type of electrical appliance. It can also apply different proposal algorithms depending on the type of failure. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the content of the inquiry. By applying different proposal algorithms depending on the category of the failure cause, it is possible to provide more appropriate proposal results. The categories of failure causes include, but are not limited to, hardware failures, software bugs, and user errors. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input failure cause category data into a generating AI and have the generating AI select a proposal algorithm.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0092] The proposal department can determine the priority of proposals based on the timing of failure cause submissions. For example, the proposal department will prioritize proposals for urgent failure causes. It can also prioritize proposals for normal failure causes. Furthermore, the proposal department can adjust the priority of proposals according to the timing of failure cause submissions. This allows for the provision of more appropriate proposal results by determining the priority of proposals based on the timing of failure cause submissions. The submission timing includes, but is not limited to, the submission date and time, and the time elapsed since submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input failure cause submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0093] The proposal unit can adjust the order of proposals based on the relevance of the failure causes. For example, the proposal unit can prioritize proposals for highly relevant failure causes. It can also propose solutions in the normal order for less relevant failure causes. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the failure causes. By adjusting the order of proposals based on the relevance of the failure causes, it is possible to provide more appropriate proposal results. The relevance of failure causes includes, but is not limited to, similarity of content, scope of impact, and user attributes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input failure cause relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0094] The data acquisition unit can estimate the user's emotions and adjust the method of acquiring relevant data based on the estimated user emotions. For example, if the user is nervous, the data acquisition unit can provide a simple and highly visible data acquisition method. It can also provide a detailed data acquisition method if the user is relaxed. Furthermore, if the user is in a hurry, the data acquisition unit can provide a concise data acquisition method. This allows for more appropriate data acquisition by adjusting the data acquisition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0095] The acquisition unit can improve the accuracy of acquisition by referring to past data when acquiring relevant data. For example, the acquisition unit can select the optimal data acquisition method based on past data. The acquisition unit can also improve the accuracy of acquisition by referring to past data. Furthermore, the acquisition unit can analyze past data to improve the accuracy of acquisition. In this way, the accuracy of acquisition can be improved by referring to past data. Past data includes, but is not limited to, past inquiry history, failure data, and user operation history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input past data into a generating AI and have the generating AI perform the task of improving the accuracy of acquisition.

[0096] The acquisition unit can acquire relevant data while considering the attribute information of the inquiry submitter. For example, the acquisition unit can select the optimal data acquisition method based on the inquiry submitter's age and gender. The acquisition unit can also improve the accuracy of acquisition based on the inquiry submitter's usage. Furthermore, the acquisition unit can analyze the inquiry submitter's attribute information to improve the accuracy of acquisition. Thus, by considering the inquiry submitter's attribute information, the accuracy of acquisition can be improved. The submitter's attribute information includes, but is not limited to, age, gender, occupation, and device used. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the submitter's attribute information into a generating AI and have the generating AI perform the improvement of acquisition accuracy.

[0097] The data acquisition unit can estimate the user's emotions and adjust the display method of the acquired data based on the estimated user emotions. For example, if the user is nervous, the data acquisition unit can provide a simple and highly visible display method. If the user is relaxed, the data acquisition unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data acquisition unit can provide a concise display method. By adjusting the display method of the acquired data according to the user's emotions, more appropriate display results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0098] The data acquisition unit can acquire relevant data while considering the geographical distribution of queries. For example, the data acquisition unit can prioritize the acquisition of data occurring in a specific region. The data acquisition unit can also select the optimal data acquisition method based on the geographical distribution. Furthermore, the data acquisition unit can analyze the geographical distribution to improve the accuracy of the acquisition. This improves the accuracy of the acquisition by considering the geographical distribution of queries. Geographical distribution includes, but is not limited to, the number of queries by region and geographical clusters. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the acquisition.

[0099] The acquisition unit can improve the accuracy of data acquisition by referring to relevant literature when acquiring relevant data. For example, the acquisition unit can select the optimal data acquisition method based on relevant literature. The acquisition unit can also improve the accuracy of data acquisition by referring to relevant literature. Furthermore, the acquisition unit can analyze relevant literature to improve the accuracy of data acquisition. Thus, the accuracy of data acquisition can be improved by referring to relevant literature. Relevant literature includes, but is not limited to, technical papers, patent documents, and industry reports. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of data acquisition.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and adjust the way inquiries are handled based on those emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly handle the inquiry. This allows for more appropriate responses by adjusting the way inquiries are handled according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0103] The identification unit can estimate the user's emotions and adjust the method for identifying the cause of failure based on the estimated user emotions. For example, if the user is tense, it can provide a simple and highly visible identification method. If the user is relaxed, it can provide a more detailed identification method. Furthermore, if the user is in a hurry, it can provide a concise identification method. By adjusting the method for identifying the cause of failure according to the user's emotions, more appropriate identification results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide a simple and easily understandable suggestion. If the user is relaxed, it can provide a more detailed suggestion. Furthermore, if the user is in a hurry, it can provide a concise suggestion. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0105] The data acquisition unit can estimate the user's emotions and adjust the method of acquiring relevant data based on the estimated user emotions. For example, if the user is nervous, it can provide a simple and highly visible data acquisition method. If the user is relaxed, it can provide a detailed data acquisition method. Furthermore, if the user is in a hurry, it can provide a concise data acquisition method. By adjusting the data acquisition method according to the user's emotions, more appropriate data acquisition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0106] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, it can automatically display as suggestions the types of inquiries the user has frequently made in the past. It can also prioritize suggesting inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest inquiry methods to be used during specific time periods based on the user's past inquiry history. In this way, the reception department can select the most suitable reception method by analyzing the user's past inquiry history. Past inquiry history includes, but is not limited to, the content of inquiries, the date and time, and the outcome of the response. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past inquiry history data into a generating AI and have the generating AI select the most suitable reception method.

[0107] The reception unit can filter inquiries based on the user's current usage and areas of interest. For example, it can automatically retrieve information about the electrical appliances the user is currently using and prioritize displaying relevant inquiries. It can also filter and display relevant inquiries based on the user's areas of interest. Furthermore, it can suggest the most appropriate inquiry method based on the user's current usage. This allows for the priority display of relevant inquiries by filtering based on the user's current usage and areas of interest. Current usage includes, but is not limited to, the status of the devices and applications being used. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user usage data into a generating AI and have the generating AI perform the filtering.

[0108] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize inquiries related to that region. It can also suggest the most appropriate inquiry method based on the user's geographical location. Furthermore, if a user is on the move, it can prioritize inquiries that are relevant based on their current location. In this way, by considering the user's geographical location, it is possible to prioritize receiving inquiries that are highly relevant. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, it can perform a detailed analysis on high-importance inquiries and a simplified analysis on low-importance inquiries. Furthermore, it can determine the priority of the analysis according to the importance of the inquiry content. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the inquiry content. The importance of the inquiry content includes, but is not limited to, the scope of impact, urgency, and user sentiment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inquiry importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0110] The identification unit can improve the accuracy of its identification process by referring to past failure data when identifying the cause of a failure. For example, it can select the optimal identification method based on past failure data. It can also improve the accuracy of its identification process by referring to past failure data. Furthermore, it can analyze past failure data to improve the accuracy of its identification process. In this way, the accuracy of its identification process can be improved by referring to past failure data. Past failure data includes, but is not limited to, the type of failure, the frequency of occurrence, and the results of the countermeasures taken. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past failure data into a generating AI and have the generating AI perform the task of improving the accuracy of its identification process.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives inquiries from users. These inquiries include technical issues, product questions, and service inquiries. The reception desk can receive inquiries via web forms, telephone, email, and chatbots. For example, it can automatically analyze the content entered in web forms and categorize the inquiries. Telephone inquiries are converted to text using speech recognition technology and then analyzed. Email inquiries are analyzed and categorized using natural language processing technology. The chatbot collects and analyzes inquiry content through interaction with the user. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis techniques can be used to extract keywords from the inquiry and identify areas where malfunctions may occur. Data mining techniques can also be used to analyze past inquiry data and identify similar failure cases. Machine learning algorithms can also be used to classify the inquiry and estimate the cause of the failure. Step 3: The identification unit identifies the cause of the failure based on the results analyzed by the analysis unit. The causes of failure include hardware failure, software bugs, and user error. For example, based on keywords extracted by the analysis unit, the unit identifies areas that are likely to fail. It is also possible to identify the cause of failure by referring to past failure data using data mining techniques. It is also possible to estimate the cause of failure using machine learning algorithms. Step 4: The proposing department proposes appropriate countermeasures based on the cause identified by the specific department. These countermeasures may include repair procedures, software updates, and user instructions. For example, depending on the cause of the malfunction, a repair procedure may be proposed. If the cause is a software bug, an update procedure may also be proposed. If the cause is user error, instructions on the correct operation method may also be provided. Step 5: The acquisition unit automatically acquires and analyzes relevant data based on the proposed solution proposed by the proposal unit. Relevant data includes log data, sensor data, and user operation history. For example, log data can be acquired and analyzed via an API. Sensor data can also be collected and analyzed in real time. User operation history can also be referenced to collect data for identifying the cause of a malfunction.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, proposal unit, and acquisition unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries from the user. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the content of the inquiry. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies the cause of the failure. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes an appropriate solution. The acquisition unit is implemented by the control unit 46A of the smart device 14 and automatically acquires and analyzes the relevant data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, proposal unit, and acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives inquiries from the user. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the content of the inquiry. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies the cause of the failure. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes an appropriate solution. The acquisition unit is implemented by the control unit 46A of the smart glasses 214 and automatically acquires and analyzes the relevant data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, proposal unit, and acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries from the user. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the content of the inquiry. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies the cause of the failure. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes an appropriate response method. The acquisition unit is implemented by the control unit 46A of the headset terminal 314 and automatically acquires and analyzes the relevant data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, proposal unit, and acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries from the user. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the content of the inquiry. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies the cause of the failure. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes an appropriate response method. The acquisition unit is implemented by the control unit 46A of the robot 414 and automatically acquires and analyzes the relevant data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk that handles inquiries from users, An analysis unit analyzes the content of inquiries received by the aforementioned reception unit, An identification unit that identifies the cause of the failure based on the results of the analysis performed by the aforementioned analysis unit, A proposal unit that proposes an appropriate countermeasure based on the cause identified by the aforementioned identification unit, The system includes an acquisition unit that automatically acquires and analyzes relevant data based on the proposed solution by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts how inquiries are handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving an inquiry, filtering is performed based on the user's current usage and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inquiries to be handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving inquiries, the system prioritizes inquiries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving an inquiry, the system analyzes the user's social media activity and selects the most relevant inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the query category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the queries. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, The system estimates the user's emotions and adjusts the method for identifying the cause of the malfunction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When identifying the cause of a failure, past failure data is used to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying the cause of a malfunction, the attribute information of the person who submitted the inquiry will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, It estimates the user's emotions and adjusts how the identified cause of failure is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying the cause of a malfunction, the geographical distribution of inquiries should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying the cause of a failure, refer to relevant literature to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the severity of the cause of the failure. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the failure cause. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of submission of the failure cause. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the causes of failures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The acquisition unit is, We estimate the user's emotions and adjust how relevant data is acquired based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring related data, historical data is referenced to improve the accuracy of the acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The acquisition unit is, When retrieving related data, the attribute information of the person submitting the inquiry will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The acquisition unit is, It estimates the user's emotions and adjusts how the acquired data is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The acquisition unit is, When retrieving related data, the geographical distribution of the query is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The acquisition unit is, When acquiring related data, we refer to relevant literature to improve the accuracy of the acquisition. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles inquiries from users, An analysis unit that analyzes the content of inquiries received by the aforementioned reception unit, An identification unit that identifies the cause of the failure based on the results of the analysis performed by the aforementioned analysis unit, A proposal unit that proposes an appropriate countermeasure based on the cause identified by the aforementioned identification unit, The system includes an acquisition unit that automatically acquires and analyzes relevant data based on the proposed response method by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is We estimate the user's emotions and adjust the way we handle inquiries based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable contact method. The system according to feature 1.

4. The aforementioned reception unit is When receiving an inquiry, filtering is performed based on the user's current usage and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inquiries to be received based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving inquiries, the system prioritizes inquiries that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving an inquiry, the system analyzes the user's social media activity and selects the most relevant inquiry. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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