System

Abstract

An object of a system according to an embodiment is to improve the efficiency of cooperation between a customer and an engineer and to reduce man-hours and labor costs.SOLUTION: A system includes a reception part, a hearing part, a segmentation part, and a provision part. The reception unit receives an inquiry from a customer. The hearing unit hears a confirmation item based on the inquiry content received by the reception unit. The division unit divides the inquiry content on the basis of the content heard by the hearing unit. The providing unit provides information to the engineer based on the content separated by the separating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there were issues such as misunderstandings between customers and engineers and repeated interviews, which increased the amount of work required.

[0005] The system according to the embodiment aims to improve the efficiency of collaboration between customers and engineers and reduce labor hours and personnel costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a hearing unit, a classification unit, and a providing unit. The reception unit receives customer inquiries. The hearing unit asks for confirmation based on the inquiry content received by the reception unit. The classification unit classifies the inquiry content based on the content heard by the hearing unit. The providing unit provides information to an engineer based on the content classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of collaboration between customers and engineers, and reduce man-hours and labor costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a chatbot to efficiently process customer inquiries and maximize the efficiency of collaboration with engineers. In this system, the chatbot accepts customer inquiries, automatically asks for confirmation based on the customer's description, categorizes the inquiry, and provides the necessary information to the engineer. This system streamlines collaboration between customers and engineers, reducing labor costs and manpower. The system also provides 24-hour support and supports various languages. For example, the system accepts a customer's inquiry. For example, a customer may inquire about an issue such as "I can't connect to the Internet." This inquiry is entered into the chatbot. The chatbot then automatically asks for confirmation based on the customer's description. For example, it asks questions such as "Is the router turned on?" or "Is an error message displayed?" This allows the system to grasp the details of the inquiry. The chatbot then categorizes the inquiry. For example, if the router is not turned on, it instructs the user to turn it on. If an error message is displayed, the system infers the suspected problem based on the error message. Finally, the chatbot provides the necessary information to the engineer, who then responds. For example, the system provides the content of error messages and information about the customer's environment to the engineer, allowing the engineer to respond quickly. This enables the system to automatically correct long-form questions and essay tests in Japanese language.

[0029] A chatbot system according to an embodiment includes a reception unit, a hearing unit, a classification unit, and a provision unit. The reception unit receives customer inquiries. Customer inquiries include, but are not limited to, technical issues, product questions, and support requests. The reception unit receives customer inquiries using, for example, a chatbot. The reception unit is available 24 hours a day and can support various languages. For example, support for multiple languages, such as English, Japanese, and Chinese, can reduce night shift labor hours due to time differences. The hearing unit asks questions to confirm based on the inquiries received by the reception unit. The questions to confirm include, but are not limited to, required information and types of questions. For example, the hearing unit creates a list of questions to present to the customer. The hearing unit can also use AI to ask questions to confirm based on the customer's report. For example, the hearing unit asks questions such as, "Is the router turned on?" or "Is an error message displayed?" The isolation unit isolates the inquiry based on the information gathered by the hearing unit. Isolation includes, for example, the type of problem, priority, and scope of impact, but is not limited to these examples. The isolation unit, for example, estimates the suspected malfunction location. The isolation unit can also isolate the inquiry using AI. For example, the isolation unit identifies the suspected malfunction location based on the content of an error message or an analysis of a log file. The providing unit provides information to an engineer based on the information isolated by the isolation unit. The provided information includes, for example, an error message, a log file, and system configuration information, but is not limited to these examples. The providing unit provides, for example, the content of an error message and information about the customer's environment to the engineer. The providing unit can also provide information to the engineer using AI. For example, the providing unit inputs the content of an error message and information about the customer's environment into AI, and provides the engineer with the results of the AI's analysis. This allows the chatbot system according to the embodiment to efficiently process customer inquiries and maximize the efficiency of collaboration with engineers.

[0030] The hearing unit can present the confirmation items to the customer in a list. Examples of listing include, but are not limited to, the order of items, importance, and display format. The hearing unit can present the confirmation items to the customer in a list. For example, the hearing unit can display the confirmation items in list format to allow the customer to select an item. The hearing unit can also sort and display the confirmation items in order of importance. For example, the hearing unit can display important confirmation items at the top so that the customer can respond to them first. The hearing unit can also change the display format of the confirmation items. For example, the hearing unit can not only display the confirmation items in text format, but also visually present them using images or videos. Listing the confirmation items improves the efficiency of the hearing. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without AI. For example, the hearing unit can input the confirmation items into AI and present the list created by the AI ​​to the customer.

[0031] The isolation unit can identify a suspected malfunction location. Examples of suspected malfunction locations include, but are not limited to, analysis of error messages and log files. The isolation unit can identify a suspected malfunction location based on the content of an error message. For example, the isolation unit can analyze the content of an error message and infer a suspected malfunction location based on a specific error code. The isolation unit can also identify a suspected malfunction location based on analysis of a log file. For example, the isolation unit can analyze the content of a log file and detect a specific error message or abnormal operation. The isolation unit can also identify a suspected malfunction location based on system configuration information. For example, the isolation unit can analyze the system configuration information and identify a specific hardware or software problem. This can infer a suspected malfunction location, thereby accelerating problem resolution. Some or all of the above-described processing in the isolation unit can be performed using, for example, AI, or can be performed without AI. For example, the isolation unit can input the content of an error message or a log file into AI and identify a suspected malfunction location based on the AI's analysis results.

[0032] The providing unit can provide the content of an error message and information about the customer's environment to the engineer. The information to be provided includes, but is not limited to, error messages, log files, and system configuration information. The providing unit, for example, provides the content of an error message to the engineer. For example, the providing unit transmits the content of an error message to the engineer in text format. The providing unit can also provide the content of a log file to the engineer. For example, the providing unit transmits the content of a log file to the engineer in text format. The providing unit can also provide the system configuration information to the engineer. For example, the providing unit transmits the system configuration information to the engineer in text format. This makes it possible to provide the engineer with necessary information so that they can respond quickly. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the content of an error message or a log file into AI and provide the engineer with the results of the AI's analysis.

[0033] The reception department is capable of providing 24-hour support. 24-hour support includes, but is not limited to, a shift system and the use of support tools. The reception department is capable of providing 24-hour support, for example. For example, the reception department may establish a shift system and accept customer inquiries 24 hours a day. The reception department can also provide 24-hour support using support tools. For example, the reception department may use a chatbot to accept customer inquiries 24 hours a day. This enables 24-hour support and reduces the amount of work required for night shifts due to time differences. Some or all of the above-described processing in the reception department may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input data from a chatbot into AI, which can then accept customer inquiries 24 hours a day.

[0034] The reception unit can support multiple languages. Supportable languages ​​include, but are not limited to, English, Japanese, and Chinese, for example. The reception unit can support multiple languages, for example. For example, the reception unit can support multiple languages, such as English, Japanese, and Chinese. The reception unit can also support multiple languages ​​using AI. For example, the reception unit uses AI to accept customer inquiries in multiple languages. This enables multilingual support and promotes international use. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI, for example. For example, the reception unit can accept customer inquiries in multiple languages ​​using AI.

[0035] The reception unit can analyze past inquiry history and select an appropriate reception method. For example, the reception unit can automatically display inquiries that the user has frequently made in the past as candidates. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. This makes it possible to efficiently receive inquiries by utilizing the past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past inquiry history into AI and select a reception method based on the results of the AI ​​analysis.

[0036] The reception unit can perform filtering based on the user's current situation or areas of interest when receiving an inquiry. For example, when the user inputs their current situation, the reception unit automatically filters and displays related inquiry content. The reception unit can also preferentially display related inquiry content based on the user's areas of interest. The reception unit can also suggest optimal inquiry content taking into account the user's current situation and areas of interest. This makes it possible to provide optimal inquiry content according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI and perform filtering based on the results of analysis by the AI.

[0037] When receiving an inquiry, the reception unit can select an appropriate reception means depending on the user's input method. For example, when a user makes an inquiry by voice, the reception unit can prioritize reception of the voice input. Furthermore, when a user makes an inquiry by text, the reception unit can also prioritize reception of the text input. Furthermore, when a user makes an inquiry by attaching an image, the reception unit can perform image analysis and select the optimal reception means. This improves convenience by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into AI and select the reception means based on the results of the AI ​​analysis.

[0038] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries based on the user's geographical location information. For example, if a user submits an inquiry from a specific region, the reception unit prioritizes receiving inquiries related to that region. The reception unit can also automatically filter and display related inquiry content based on the user's geographical location information. The reception unit can also suggest optimal inquiry content taking the user's geographical location information into consideration. This makes it possible to prioritize processing highly relevant inquiries by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and perform filtering based on the results of analysis by the AI.

[0039] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. For example, the reception unit can prioritize receiving inquiries regarding places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related inquiries. The reception unit can also receive related inquiries by referring to the activities of the user's friends on social media. In this way, related inquiries can be efficiently processed by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and receive the inquiry based on the results of the AI ​​analysis.

[0040] When receiving an inquiry, the reception unit can provide an appropriate reception method by reflecting the user's past feedback. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's past feedback. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI and customize the reception method based on the results of the AI ​​analysis.

[0041] The hearing unit can adjust the specific content of the hearing based on the importance of the inquiry during the hearing. For example, the hearing unit can conduct a detailed hearing for an inquiry of high importance. Furthermore, the hearing unit can also conduct a simplified hearing for an inquiry of low importance. Furthermore, the hearing unit can adjust the number of questions and content of the hearing based on the importance. This enables efficient responses by conducting hearings according to the importance of the inquiry. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry importance data into AI and adjust the content of the hearing based on the results of analysis by the AI.

[0042] The hearing unit can apply different hearing algorithms depending on the category of the inquiry during the hearing. For example, in the case of a technical inquiry, the hearing unit can conduct the hearing mainly on technical questions. Furthermore, in the case of an inquiry about a service, the hearing unit can conduct the hearing mainly on questions related to the service. Furthermore, the hearing unit can apply an optimal hearing algorithm depending on the category of the inquiry. This enables efficient response by conducting the optimal hearing depending on the category of the inquiry. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry category data into AI and apply a hearing algorithm based on the results of analysis by the AI.

[0043] The hearing unit can improve the accuracy of the hearing during the hearing by referring to the user's past hearing results. For example, the hearing unit can suggest optimal questions based on the user's past hearing results. The hearing unit can also prioritize specific questions based on the user's past hearing results. The hearing unit can also improve the accuracy of the hearing by referring to the user's past hearing results. In this way, the accuracy of the hearing is improved by referring to the past hearing results. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input the user's past hearing result data into AI and improve the accuracy of the hearing based on the results analyzed by the AI.

[0044] During a hearing, the hearing section can determine the priority of the hearing based on the time of submission of the inquiry. For example, the hearing section prioritizes hearing recently submitted inquiries. The hearing section can also postpone inquiries submitted earlier. The hearing section can also determine the priority of the hearing based on the time of submission. This enables efficient responses by setting priorities based on the time of submission. Some or all of the above-described processing in the hearing section may be performed using, for example, AI, or may be performed without using AI. For example, the hearing section can input data on the time of submission of the inquiry into AI and determine the priority based on the results of analysis by the AI.

[0045] The hearing unit can adjust the hearing order based on the relevance of the inquiries during the hearing. For example, the hearing unit prioritizes hearing highly relevant inquiries. The hearing unit can also postpone less relevant inquiries. The hearing unit can also adjust the hearing order based on the relevance of the inquiries. This enables efficient responses by prioritizing hearing highly relevant inquiries. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry relevance data into AI and adjust the order based on the results of analysis by the AI.

[0046] During the hearing, the hearing unit can adjust the use of technical terms during the hearing according to the user's level of expertise. For example, if the user has specialized knowledge, the hearing unit can use technical terms during the hearing. Furthermore, if the user does not have specialized knowledge, the hearing unit can also use simple language during the hearing. Furthermore, the hearing unit can adjust the use of technical terms during the hearing according to the user's level of expertise. This enables appropriate information collection by conducting hearings according to the user's level of expertise. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without AI. For example, the hearing unit can input the user's level of expertise data into AI and adjust the use of technical terms based on the results of the AI ​​analysis.

[0047] The triage unit can improve the accuracy of triage based on the interrelationships between inquiries during triage. For example, the triage unit groups related inquiries and performs triage. The triage unit can also analyze the interrelationships between inquiries and propose an optimal triage method. The triage unit can also improve the accuracy of triage by taking the interrelationships between inquiries into consideration. In this way, the accuracy of triage is improved by taking the interrelationships between inquiries into consideration. Some or all of the above-mentioned processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input interrelationship data between inquiries into AI and perform triage based on the results of analysis by AI.

[0048] The triage unit can triage inquiries taking into account attribute information of the person who submitted the inquiry. For example, the triage unit proposes an optimal triage method based on the attribute information of the person who submitted the inquiry (such as age, gender, and occupation). The triage unit can also prioritize triage of related inquiries by taking into account the attribute information of the person who submitted the inquiry. The triage unit can also improve the accuracy of triage by referring to the attribute information of the person who submitted the inquiry. This enables appropriate triage by taking into account the attribute information of the person who submitted the inquiry. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input attribute information data of the person who submitted the inquiry into AI and perform triage based on the results of analysis by AI.

[0049] The triage unit can weight the triage based on the frequency of submission of the inquiry when triage. For example, the triage unit prioritizes triage of inquiries submitted frequently. The triage unit can also postpone inquiries submitted infrequently. The triage unit can also weight the triage based on the submission frequency. By weighting based on the submission frequency, efficient triage becomes possible. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input submission frequency data into AI and perform weighting based on the results of analysis by the AI.

[0050] The triage unit can triage inquiries taking into account the geographical distribution of the inquiries. For example, the triage unit groups geographically close inquiries and performs triage. The triage unit can also prioritize triage of related inquiries based on the geographical distribution. The triage unit can also propose an optimal triage method taking the geographical distribution into consideration. In this way, by taking the geographical distribution into consideration, highly related inquiries can be processed efficiently. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input geographical distribution data into AI and perform triage based on the results of the AI ​​analysis.

[0051] The segmentation unit can improve the accuracy of segmentation by referring to literature related to the query during segmentation. The segmentation unit can, for example, propose an optimal segmentation method based on related literature. The segmentation unit can also segment the query by referring to related literature. The segmentation unit can also improve the accuracy of segmentation by taking related literature into consideration. In this way, the accuracy of segmentation is improved by referring to related literature. Some or all of the above-mentioned processing in the segmentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the segmentation unit can input related literature data into AI and perform segmentation based on the results of analysis by AI.

[0052] The triage unit can triage inquiries based on their market value. For example, the triage unit prioritizes triage of inquiries with high market value. The triage unit can also postpone inquiries with low market value. The triage unit can also propose an optimal triage method taking market value into consideration. This allows important inquiries to be handled with priority by taking market value into consideration. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input market value data into AI and perform triage based on the results of the AI's analysis.

[0053] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between inquiries when providing the information. For example, the providing unit groups related inquiries and provides information. The providing unit can also analyze the interrelationships between inquiries and provide optimal information. The providing unit can also improve the accuracy of the information provided by taking into account the interrelationships between inquiries. In this way, the accuracy of the information provided is improved by taking into account the interrelationships between inquiries. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input interrelationship data between inquiries into AI and provide information based on the results of analysis by the AI.

[0054] The providing unit can provide information taking into consideration the attribute information of the person who submitted the inquiry. The providing unit provides optimal information based on, for example, the attribute information of the person who submitted the inquiry (such as age, gender, and occupation). The providing unit can also provide relevant information preferentially by taking into consideration the attribute information of the person who submitted the inquiry. The providing unit can also improve the accuracy of the information provided by referring to the attribute information of the person who submitted the inquiry. This makes it possible to provide appropriate information by taking into consideration the attribute information of the person who submitted the inquiry. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the attribute information data of the person who submitted the inquiry into AI and provide information based on the results of the analysis by AI.

[0055] The providing unit can weight the information provided based on the frequency of inquiries submitted at the time of providing the information. For example, the providing unit can prioritize providing information related to inquiries submitted frequently. The providing unit can also postpone information related to inquiries submitted infrequently. The providing unit can also weight the information provided based on the frequency of submission. As a result, weighting based on the frequency of submission enables efficient information provision. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input submission frequency data into AI and perform weighting based on the results of analysis by the AI.

[0056] The providing unit can provide information taking into consideration the geographical distribution of inquiries. For example, the providing unit can preferentially provide information related to inquiries that are geographically close. The providing unit can also provide relevant information based on the geographical distribution. The providing unit can also provide optimal information taking the geographical distribution into consideration. In this way, highly relevant information can be provided by taking the geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical distribution data into AI and provide information based on the results of analysis by the AI.

[0057] The providing unit can improve the accuracy of the information provided by referring to related literature of the query when providing the information. The providing unit, for example, provides optimal information based on related literature. The providing unit can also provide information related to the query by referring to related literature. The providing unit can also improve the accuracy of the information provided by taking related literature into consideration. As a result, the accuracy of the information provided is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input related literature data into AI and provide information based on the results of analysis by AI.

[0058] The providing unit can provide information taking into consideration the market value of the inquiry when providing the information. For example, the providing unit can provide information related to inquiries with high market value preferentially. The providing unit can also postpone information related to inquiries with low market value. The providing unit can also provide optimal information taking market value into consideration. In this way, important information can be provided preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input market value data into AI and provide information based on the results of analysis by AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past inquiry history and automatically suggest similar inquiries. For example, if a user previously inquired about "the Internet is not connecting," the reception unit can suggest past solutions when a similar problem recurs. The reception unit can also present predicted problems in advance based on the frequency and content of the user's past inquiries. Furthermore, the reception unit can predict problems that are likely to occur during specific time periods based on the user's past inquiry history and suggest appropriate responses. This makes it possible to efficiently solve problems by utilizing the user's past inquiry history.

[0061] The triage unit can automatically evaluate the priority of inquiries and adjust the processing order according to their importance. For example, inquiries about system failures can be processed as a high priority, while general questions can be treated as a low priority and postponed. The triage unit can also evaluate the scope of the inquiry's impact and prioritize issues that have a wide impact. Furthermore, the triage unit can evaluate the urgency of the inquiry and immediately notify an engineer if an emergency response is required. This enables efficient processing according to the priority of the inquiry.

[0062] The reception unit can automatically select the most suitable support center based on the user's current geographical location information. For example, if a user makes an inquiry from a specific region, the inquiry will be transferred to the support center closest to that region. The reception unit can also provide information about region-specific problems and services based on the user's geographical location information. Furthermore, the reception unit can also suggest the most suitable response time, taking into account the user's geographical location information. This enables efficient support that takes geographical factors into account.

[0063] The triage unit can analyze the interrelationships between inquiries and group related inquiries for processing. For example, multiple inquiries about the same problem can be processed as a single group, allowing for efficient responses. The triage unit can also propose a common solution based on related inquiries. Furthermore, the triage unit can determine the optimal processing order by taking into account the interrelationships between inquiries. This makes it possible to efficiently process related inquiries.

[0064] The reception unit can analyze the user's social media activity and automatically suggest related inquiries. For example, if the user mentions a specific product on social media, inquiries about that product will be preferentially suggested. The reception unit can also present predicted problems in advance based on the user's social media activity history. Furthermore, the reception unit can suggest related inquiries by referring to the activities of the user's friends on social media. This makes it possible to efficiently respond to inquiries by utilizing social media activity.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception department accepts customer inquiries. Customer inquiries include technical issues, product questions, and support requests. The reception department accepts customer inquiries using, for example, a chatbot. The reception department is available 24 hours a day and can provide support in multiple languages, including English, Japanese, and Chinese. Step 2: The hearing department asks for confirmation items based on the inquiry received by the reception department. Confirmation items include the necessary information and the type of question. For example, the hearing department may create a list of confirmation items and present it to the customer. It is also possible to use AI to ask for confirmation items based on the customer's declaration. Step 3: The isolation section isolates the inquiry based on the information gathered by the hearing section. Isolation includes the type of problem, priority, and scope of impact. The isolation section, for example, estimates the suspected problem area. It can also use AI to isolate the inquiry. Step 4: The provision unit provides information to the engineer based on the details identified by the isolation unit. The information provided includes error messages, log files, system configuration information, etc. The provision unit provides the engineer with, for example, the contents of error messages and information about the customer's environment. It can also provide information to the engineer using AI.

[0067] (Example 2) A system according to an embodiment of the present invention uses a chatbot to efficiently process customer inquiries and maximize the efficiency of collaboration with engineers. In this system, the chatbot accepts customer inquiries, automatically asks for confirmation based on the customer's description, categorizes the inquiry, and provides the necessary information to the engineer. This system streamlines collaboration between customers and engineers, reducing labor costs and manpower. The system also provides 24-hour support and supports various languages. For example, the system accepts a customer's inquiry. For example, a customer may inquire about an issue such as "I can't connect to the Internet." This inquiry is entered into the chatbot. The chatbot then automatically asks for confirmation based on the customer's description. For example, it asks questions such as "Is the router turned on?" or "Is an error message displayed?" This allows the system to grasp the details of the inquiry. The chatbot then categorizes the inquiry. For example, if the router is not turned on, it instructs the user to turn it on. If an error message is displayed, the system infers the suspected problem based on the error message. Finally, the chatbot provides the necessary information to the engineer, who then responds. For example, the system provides the content of error messages and information about the customer's environment to the engineer, allowing the engineer to respond quickly. This enables the system to automatically correct long-form questions and essay tests in Japanese language.

[0068] A chatbot system according to an embodiment includes a reception unit, a hearing unit, a classification unit, and a provision unit. The reception unit receives customer inquiries. Customer inquiries include, but are not limited to, technical issues, product questions, and support requests. The reception unit receives customer inquiries using, for example, a chatbot. The reception unit is available 24 hours a day and can support various languages. For example, support for multiple languages, such as English, Japanese, and Chinese, can reduce night shift labor hours due to time differences. The hearing unit asks questions to confirm based on the inquiries received by the reception unit. The questions to confirm include, but are not limited to, required information and types of questions. For example, the hearing unit creates a list of questions to present to the customer. The hearing unit can also use AI to ask questions to confirm based on the customer's report. For example, the hearing unit asks questions such as, "Is the router turned on?" or "Is an error message displayed?" The isolation unit isolates the inquiry based on the information gathered by the hearing unit. Isolation includes, for example, the type of problem, priority, and scope of impact, but is not limited to these examples. The isolation unit, for example, estimates the suspected malfunction location. The isolation unit can also isolate the inquiry using AI. For example, the isolation unit identifies the suspected malfunction location based on the content of an error message or an analysis of a log file. The providing unit provides information to an engineer based on the information isolated by the isolation unit. The provided information includes, for example, an error message, a log file, and system configuration information, but is not limited to these examples. The providing unit provides, for example, the content of an error message and information about the customer's environment to the engineer. The providing unit can also provide information to the engineer using AI. For example, the providing unit inputs the content of an error message and information about the customer's environment into AI, and provides the engineer with the results of the AI's analysis. This allows the chatbot system according to the embodiment to efficiently process customer inquiries and maximize the efficiency of collaboration with engineers.

[0069] The hearing unit can present the confirmation items to the customer in a list. Examples of listing include, but are not limited to, the order of items, importance, and display format. The hearing unit can present the confirmation items to the customer in a list. For example, the hearing unit can display the confirmation items in list format to allow the customer to select an item. The hearing unit can also sort and display the confirmation items in order of importance. For example, the hearing unit can display important confirmation items at the top so that the customer can respond to them first. The hearing unit can also change the display format of the confirmation items. For example, the hearing unit can not only display the confirmation items in text format, but also visually present them using images or videos. Listing the confirmation items improves the efficiency of the hearing. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without AI. For example, the hearing unit can input the confirmation items into AI and present the list created by the AI ​​to the customer.

[0070] The isolation unit can identify a suspected malfunction location. Examples of suspected malfunction locations include, but are not limited to, analysis of error messages and log files. The isolation unit can identify a suspected malfunction location based on the content of an error message. For example, the isolation unit can analyze the content of an error message and infer a suspected malfunction location based on a specific error code. The isolation unit can also identify a suspected malfunction location based on analysis of a log file. For example, the isolation unit can analyze the content of a log file and detect a specific error message or abnormal operation. The isolation unit can also identify a suspected malfunction location based on system configuration information. For example, the isolation unit can analyze the system configuration information and identify a specific hardware or software problem. This can infer a suspected malfunction location, thereby accelerating problem resolution. Some or all of the above-described processing in the isolation unit can be performed using, for example, AI, or can be performed without AI. For example, the isolation unit can input the content of an error message or a log file into AI and identify a suspected malfunction location based on the AI's analysis results.

[0071] The providing unit can provide the content of an error message and information about the customer's environment to the engineer. The information to be provided includes, but is not limited to, error messages, log files, and system configuration information. The providing unit, for example, provides the content of an error message to the engineer. For example, the providing unit transmits the content of an error message to the engineer in text format. The providing unit can also provide the content of a log file to the engineer. For example, the providing unit transmits the content of a log file to the engineer in text format. The providing unit can also provide the system configuration information to the engineer. For example, the providing unit transmits the system configuration information to the engineer in text format. This makes it possible to provide the engineer with necessary information so that they can respond quickly. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the content of an error message or a log file into AI and provide the engineer with the results of the AI's analysis.

[0072] The reception department is capable of providing 24-hour support. 24-hour support includes, but is not limited to, a shift system and the use of support tools. The reception department is capable of providing 24-hour support, for example. For example, the reception department may establish a shift system and accept customer inquiries 24 hours a day. The reception department can also provide 24-hour support using support tools. For example, the reception department may use a chatbot to accept customer inquiries 24 hours a day. This enables 24-hour support and reduces the amount of work required for night shifts due to time differences. Some or all of the above-described processing in the reception department may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input data from a chatbot into AI, which can then accept customer inquiries 24 hours a day.

[0073] The reception unit can support multiple languages. Supportable languages ​​include, but are not limited to, English, Japanese, and Chinese, for example. The reception unit can support multiple languages, for example. For example, the reception unit can support multiple languages, such as English, Japanese, and Chinese. The reception unit can also support multiple languages ​​using AI. For example, the reception unit uses AI to accept customer inquiries in multiple languages. This enables multilingual support and promotes international use. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI, for example. For example, the reception unit can accept customer inquiries in multiple languages ​​using AI.

[0074] The reception unit can estimate the user's emotions and adjust the inquiry reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly receive inquiries. This improves the user experience by providing the optimal reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and adjust the reception method based on the analysis results of the generation AI.

[0075] The reception unit can analyze past inquiry history and select an appropriate reception method. For example, the reception unit can automatically display inquiries that the user has frequently made in the past as candidates. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. This makes it possible to efficiently receive inquiries by utilizing the past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past inquiry history into AI and select a reception method based on the results of the AI ​​analysis.

[0076] The reception unit can perform filtering based on the user's current situation or areas of interest when receiving an inquiry. For example, when the user inputs their current situation, the reception unit automatically filters and displays related inquiry content. The reception unit can also preferentially display related inquiry content based on the user's areas of interest. The reception unit can also suggest optimal inquiry content taking into account the user's current situation and areas of interest. This makes it possible to provide optimal inquiry content according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI and perform filtering based on the results of analysis by the AI.

[0077] When receiving an inquiry, the reception unit can select an appropriate reception means depending on the user's input method. For example, when a user makes an inquiry by voice, the reception unit can prioritize reception of the voice input. Furthermore, when a user makes an inquiry by text, the reception unit can also prioritize reception of the text input. Furthermore, when a user makes an inquiry by attaching an image, the reception unit can perform image analysis and select the optimal reception means. This improves convenience by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into AI and select the reception means based on the results of the AI ​​analysis.

[0078] The reception unit can estimate the user's emotions and determine the priority of inquiries to be received based on the estimated user emotions. For example, if the user makes an urgent inquiry, the reception unit can set the priority to high. Furthermore, if the user is relaxed, the reception unit can also receive the inquiry at normal priority. Furthermore, if the user is stressed, the reception unit can also set the priority to high in order to respond quickly. This enables a quick response by setting the priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and determine the priority based on the analysis results of the generation AI.

[0079] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries based on the user's geographical location information. For example, if a user submits an inquiry from a specific region, the reception unit prioritizes receiving inquiries related to that region. The reception unit can also automatically filter and display related inquiry content based on the user's geographical location information. The reception unit can also suggest optimal inquiry content taking the user's geographical location information into consideration. This makes it possible to prioritize processing highly relevant inquiries by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and perform filtering based on the results of analysis by the AI.

[0080] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. For example, the reception unit can prioritize receiving inquiries regarding places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related inquiries. The reception unit can also receive related inquiries by referring to the activities of the user's friends on social media. In this way, related inquiries can be efficiently processed by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and receive the inquiry based on the results of the AI ​​analysis.

[0081] When receiving an inquiry, the reception unit can provide an appropriate reception method by reflecting the user's past feedback. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's past feedback. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI and customize the reception method based on the results of the AI ​​analysis.

[0082] The hearing unit can estimate the user's emotions and adjust the way the user expresses the emotions based on the estimated user emotions. For example, if the user is nervous, the hearing unit can use a calm tone when listening. Furthermore, if the user is relaxed, the hearing unit can use a friendly tone when listening. Furthermore, if the user is in a hurry, the hearing unit can quickly provide a brief summary of the main points. This improves the user experience by providing a listening method that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the hearing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the hearing unit can input the user's emotion data into the generation AI and adjust the way the user expresses the emotion based on the analysis results of the generation AI.

[0083] The hearing unit can adjust the specific content of the hearing based on the importance of the inquiry during the hearing. For example, the hearing unit can conduct a detailed hearing for an inquiry of high importance. Furthermore, the hearing unit can also conduct a simplified hearing for an inquiry of low importance. Furthermore, the hearing unit can adjust the number of questions and content of the hearing based on the importance. This enables efficient responses by conducting hearings according to the importance of the inquiry. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry importance data into AI and adjust the content of the hearing based on the results of analysis by the AI.

[0084] The hearing unit can apply different hearing algorithms depending on the category of the inquiry during the hearing. For example, in the case of a technical inquiry, the hearing unit can conduct the hearing mainly on technical questions. Furthermore, in the case of an inquiry about a service, the hearing unit can conduct the hearing mainly on questions related to the service. Furthermore, the hearing unit can apply an optimal hearing algorithm depending on the category of the inquiry. This enables efficient response by conducting the optimal hearing depending on the category of the inquiry. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry category data into AI and apply a hearing algorithm based on the results of analysis by the AI.

[0085] The hearing unit can improve the accuracy of the hearing during the hearing by referring to the user's past hearing results. For example, the hearing unit can suggest optimal questions based on the user's past hearing results. The hearing unit can also prioritize specific questions based on the user's past hearing results. The hearing unit can also improve the accuracy of the hearing by referring to the user's past hearing results. In this way, the accuracy of the hearing is improved by referring to the past hearing results. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input the user's past hearing result data into AI and improve the accuracy of the hearing based on the results analyzed by the AI.

[0086] The hearing unit can estimate the user's emotions and adjust the length of the hearing based on the estimated user emotions. For example, if the user is nervous, the hearing unit can perform a short, to-the-point hearing. Furthermore, if the user is relaxed, the hearing unit can perform a detailed hearing. Furthermore, if the user is in a hurry, the hearing unit can perform a quick, to-the-point hearing. This improves the user experience by providing a hearing length that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the hearing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the hearing unit can input the user's emotion data into the generation AI and adjust the length of the hearing based on the analysis results of the generation AI.

[0087] During a hearing, the hearing section can determine the priority of the hearing based on the time of submission of the inquiry. For example, the hearing section prioritizes hearing recently submitted inquiries. The hearing section can also postpone inquiries submitted earlier. The hearing section can also determine the priority of the hearing based on the time of submission. This enables efficient responses by setting priorities based on the time of submission. Some or all of the above-described processing in the hearing section may be performed using, for example, AI, or may be performed without using AI. For example, the hearing section can input data on the time of submission of the inquiry into AI and determine the priority based on the results of analysis by the AI.

[0088] The hearing unit can adjust the hearing order based on the relevance of the inquiries during the hearing. For example, the hearing unit prioritizes hearing highly relevant inquiries. The hearing unit can also postpone less relevant inquiries. The hearing unit can also adjust the hearing order based on the relevance of the inquiries. This enables efficient responses by prioritizing hearing highly relevant inquiries. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input inquiry relevance data into AI and adjust the order based on the results of analysis by the AI.

[0089] During the hearing, the hearing unit can adjust the use of technical terms during the hearing according to the user's level of expertise. For example, if the user has specialized knowledge, the hearing unit can use technical terms during the hearing. Furthermore, if the user does not have specialized knowledge, the hearing unit can also use simple language during the hearing. Furthermore, the hearing unit can adjust the use of technical terms during the hearing according to the user's level of expertise. This enables appropriate information collection by conducting hearings according to the user's level of expertise. Some or all of the above-described processing in the hearing unit may be performed using, for example, AI, or may be performed without AI. For example, the hearing unit can input the user's level of expertise data into AI and adjust the use of technical terms based on the results of the AI ​​analysis.

[0090] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user emotions. For example, if the user is nervous, the classification unit can perform classification using simple criteria. Furthermore, if the user is relaxed, the classification unit can also perform classification using detailed criteria. Furthermore, if the user is in a hurry, the classification unit can also set criteria for quick classification. This enables efficient problem solving by providing classification criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the classification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the classification unit can input the user's emotion data into the generation AI and adjust the classification criteria based on the analysis results of the generation AI.

[0091] The triage unit can improve the accuracy of triage based on the interrelationships between inquiries during triage. For example, the triage unit groups related inquiries and performs triage. The triage unit can also analyze the interrelationships between inquiries and propose an optimal triage method. The triage unit can also improve the accuracy of triage by taking the interrelationships between inquiries into consideration. In this way, the accuracy of triage is improved by taking the interrelationships between inquiries into consideration. Some or all of the above-mentioned processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input interrelationship data between inquiries into AI and perform triage based on the results of analysis by AI.

[0092] The triage unit can triage inquiries taking into account attribute information of the person who submitted the inquiry. For example, the triage unit proposes an optimal triage method based on the attribute information of the person who submitted the inquiry (such as age, gender, and occupation). The triage unit can also prioritize triage of related inquiries by taking into account the attribute information of the person who submitted the inquiry. The triage unit can also improve the accuracy of triage by referring to the attribute information of the person who submitted the inquiry. This enables appropriate triage by taking into account the attribute information of the person who submitted the inquiry. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input attribute information data of the person who submitted the inquiry into AI and perform triage based on the results of analysis by AI.

[0093] The triage unit can weight the triage based on the frequency of submission of the inquiry when triage. For example, the triage unit prioritizes triage of inquiries submitted frequently. The triage unit can also postpone inquiries submitted infrequently. The triage unit can also weight the triage based on the submission frequency. By weighting based on the submission frequency, efficient triage becomes possible. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input submission frequency data into AI and perform weighting based on the results of analysis by the AI.

[0094] The segmentation unit can estimate the user's emotions and adjust the order in which the segmentation results are displayed based on the estimated user's emotions. For example, if the user is nervous, the segmentation unit can display important results first. Furthermore, if the user is relaxed, the segmentation unit can also display detailed results in an orderly manner. Furthermore, if the user is in a hurry, the segmentation unit can also display results that highlight the main points first. This enables efficient information provision by displaying results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the segmentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the segmentation unit can input the user's emotion data into the generation AI and adjust the order in which the results are displayed based on the analysis results of the generation AI.

[0095] The triage unit can triage inquiries taking into account the geographical distribution of the inquiries. For example, the triage unit groups geographically close inquiries and performs triage. The triage unit can also prioritize triage of related inquiries based on the geographical distribution. The triage unit can also propose an optimal triage method taking the geographical distribution into consideration. In this way, by taking the geographical distribution into consideration, highly related inquiries can be processed efficiently. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input geographical distribution data into AI and perform triage based on the results of the AI ​​analysis.

[0096] The segmentation unit can improve the accuracy of segmentation by referring to literature related to the query during segmentation. The segmentation unit can, for example, propose an optimal segmentation method based on related literature. The segmentation unit can also segment the query by referring to related literature. The segmentation unit can also improve the accuracy of segmentation by taking related literature into consideration. In this way, the accuracy of segmentation is improved by referring to related literature. Some or all of the above-mentioned processing in the segmentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the segmentation unit can input related literature data into AI and perform segmentation based on the results of analysis by AI.

[0097] The triage unit can triage inquiries based on their market value. For example, the triage unit prioritizes triage of inquiries with high market value. The triage unit can also postpone inquiries with low market value. The triage unit can also propose an optimal triage method taking market value into consideration. This allows important inquiries to be handled with priority by taking market value into consideration. Some or all of the above-described processing in the triage unit may be performed using, for example, AI, or may be performed without using AI. For example, the triage unit can input market value data into AI and perform triage based on the results of the AI's analysis.

[0098] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide important information first. Furthermore, if the user is relaxed, the providing unit can provide detailed information in an orderly manner. Furthermore, if the user is in a hurry, the providing unit can provide information that highlights the main points first. This enables efficient response by providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and determine the priority of information based on the analysis results of the generation AI.

[0099] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between inquiries when providing the information. For example, the providing unit groups related inquiries and provides information. The providing unit can also analyze the interrelationships between inquiries and provide optimal information. The providing unit can also improve the accuracy of the information provided by taking into account the interrelationships between inquiries. In this way, the accuracy of the information provided is improved by taking into account the interrelationships between inquiries. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input interrelationship data between inquiries into AI and provide information based on the results of analysis by the AI.

[0100] The providing unit can provide information taking into consideration the attribute information of the person who submitted the inquiry. The providing unit provides optimal information based on, for example, the attribute information of the person who submitted the inquiry (such as age, gender, and occupation). The providing unit can also provide relevant information preferentially by taking into consideration the attribute information of the person who submitted the inquiry. The providing unit can also improve the accuracy of the information provided by referring to the attribute information of the person who submitted the inquiry. This makes it possible to provide appropriate information by taking into consideration the attribute information of the person who submitted the inquiry. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the attribute information data of the person who submitted the inquiry into AI and provide information based on the results of the analysis by AI.

[0101] The providing unit can weight the information provided based on the frequency of inquiries submitted at the time of providing the information. For example, the providing unit can prioritize providing information related to inquiries submitted frequently. The providing unit can also postpone information related to inquiries submitted infrequently. The providing unit can also weight the information provided based on the frequency of submission. As a result, weighting based on the frequency of submission enables efficient information provision. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input submission frequency data into AI and perform weighting based on the results of analysis by the AI.

[0102] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This improves visibility by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and adjust the display method based on the analysis results of the generation AI.

[0103] The providing unit can provide information taking into consideration the geographical distribution of inquiries. For example, the providing unit can preferentially provide information related to inquiries that are geographically close. The providing unit can also provide relevant information based on the geographical distribution. The providing unit can also provide optimal information taking the geographical distribution into consideration. In this way, highly relevant information can be provided by taking the geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical distribution data into AI and provide information based on the results of analysis by the AI.

[0104] The providing unit can improve the accuracy of the information provided by referring to related literature of the query when providing the information. The providing unit, for example, provides optimal information based on related literature. The providing unit can also provide information related to the query by referring to related literature. The providing unit can also improve the accuracy of the information provided by taking related literature into consideration. As a result, the accuracy of the information provided is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input related literature data into AI and provide information based on the results of analysis by AI.

[0105] The providing unit can provide information taking into consideration the market value of the inquiry when providing the information. For example, the providing unit can provide information related to inquiries with high market value preferentially. The providing unit can also postpone information related to inquiries with low market value. The providing unit can also provide optimal information taking market value into consideration. In this way, important information can be provided preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input market value data into AI and provide information based on the results of analysis by AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, hearing unit, isolation unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives customer inquiries. The hearing unit is realized by the specific processing unit 290 of the data processing device 12 and hears about confirmation items. The isolation unit is realized by the specific processing unit 290 of the data processing device 12 and isolates the inquiry content. The providing unit is realized by the control unit 46A of the smart device 14 and provides information to an engineer. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, hearing unit, isolation unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives customer inquiries. The hearing unit is realized by the specific processing unit 290 of the data processing device 12 and hears about confirmation items. The isolation unit is realized by the specific processing unit 290 of the data processing device 12 and isolates the inquiry content. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides information to an engineer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, hearing unit, isolation unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives customer inquiries. The hearing unit is realized by the specific processing unit 290 of the data processing device 12 and hears about confirmation items. The isolation unit is realized by the specific processing unit 290 of the data processing device 12 and isolates the content of the inquiry. The providing unit is realized by the control unit 46A of the headset type terminal 314 and provides information to an engineer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, hearing unit, isolation unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives customer inquiries. The hearing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and hears about matters to be confirmed. The isolation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and isolates the content of the inquiry. The providing unit is realized, for example, by the control unit 46A of the robot 414 and provides information to an engineer.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past inquiry history and automatically suggest similar inquiries. For example, if a user previously inquired about "the Internet is not connecting," the reception unit can suggest past solutions when a similar problem recurs. The reception unit can also present predicted problems in advance based on the frequency and content of the user's past inquiries. Furthermore, the reception unit can predict problems that are likely to occur during specific time periods based on the user's past inquiry history and suggest appropriate responses. This makes it possible to efficiently solve problems by utilizing the user's past inquiry history.

[0108] The hearing unit can estimate the user's emotions and adjust the content of the hearing questions based on the estimated user emotions. For example, if the user is feeling stressed, concise and clear questions can be asked to reduce the user's burden. Also, if the user is relaxed, detailed questions can be asked to collect deeper information. Furthermore, if the user is in a hurry, questions that focus on the main points can be asked quickly to conduct an efficient hearing. This improves the user experience by providing optimal hearing according to the user's emotions.

[0109] The triage unit can automatically evaluate the priority of inquiries and adjust the processing order according to their importance. For example, inquiries about system failures can be processed as a high priority, while general questions can be treated as a low priority and postponed. The triage unit can also evaluate the scope of the inquiry's impact and prioritize issues that have a wide impact. Furthermore, the triage unit can evaluate the urgency of the inquiry and immediately notify an engineer if an emergency response is required. This enables efficient processing according to the priority of the inquiry.

[0110] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user emotions. For example, if the user is nervous, simple, visually easy-to-understand information can be provided. If the user is relaxed, detailed text information can be provided. Furthermore, if the user is in a hurry, short information that focuses on the main points can be provided. This makes it possible to provide optimal information according to the user's emotions, improving the user experience.

[0111] The reception unit can automatically select the most suitable support center based on the user's current geographical location information. For example, if a user makes an inquiry from a specific region, the inquiry will be transferred to the support center closest to that region. The reception unit can also provide information about region-specific problems and services based on the user's geographical location information. Furthermore, the reception unit can also suggest the most suitable response time, taking into account the user's geographical location information. This enables efficient support that takes geographical factors into account.

[0112] The hearing unit can estimate the user's emotions and adjust the hearing progress speed based on the estimated user emotions. For example, if the user is feeling stressed, the hearing can be progressed at a slow pace. If the user is relaxed, the hearing can be progressed at a normal pace. Furthermore, if the user is in a hurry, the hearing can be progressed quickly. This allows the hearing to be progressed optimally according to the user's emotions, improving the user experience.

[0113] The triage unit can analyze the interrelationships between inquiries and group related inquiries for processing. For example, multiple inquiries about the same problem can be processed as a single group, allowing for efficient responses. The triage unit can also propose a common solution based on related inquiries. Furthermore, the triage unit can determine the optimal processing order by taking into account the interrelationships between inquiries. This makes it possible to efficiently process related inquiries.

[0114] The providing unit can estimate the user's emotions and adjust the level of detail of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, concise and to the point information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, short information that can be quickly understood can be provided. This makes it possible to provide optimal information according to the user's emotions, improving the user experience.

[0115] The reception unit can analyze the user's social media activity and automatically suggest related inquiries. For example, if the user mentions a specific product on social media, inquiries about that product will be preferentially suggested. The reception unit can also present predicted problems in advance based on the user's social media activity history. Furthermore, the reception unit can suggest related inquiries by referring to the activities of the user's friends on social media. This makes it possible to efficiently respond to inquiries by utilizing social media activity.

[0116] The providing unit can estimate the user's emotions and adjust the order of information to be provided based on the estimated user's emotions. For example, if the user is nervous, important information can be provided first. If the user is relaxed, detailed information can be provided in an orderly manner. Furthermore, if the user is in a hurry, information that covers the main points can be provided first. This makes it possible to provide optimal information according to the user's emotions, improving the user experience.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception department accepts customer inquiries. Customer inquiries include technical issues, product questions, and support requests. The reception department accepts customer inquiries using, for example, a chatbot. The reception department is available 24 hours a day and can provide support in multiple languages, including English, Japanese, and Chinese. Step 2: The hearing department asks for confirmation items based on the inquiry received by the reception department. Confirmation items include the necessary information and the type of question. For example, the hearing department may create a list of confirmation items and present it to the customer. It is also possible to use AI to ask for confirmation items based on the customer's declaration. Step 3: The isolation section isolates the inquiry based on the information gathered by the hearing section. Isolation includes the type of problem, priority, and scope of impact. The isolation section, for example, estimates the suspected problem area. It can also use AI to isolate the inquiry. Step 4: The provision unit provides information to the engineer based on the details identified by the isolation unit. The information provided includes error messages, log files, system configuration information, etc. The provision unit provides the engineer with, for example, the contents of error messages and information about the customer's environment. It can also provide information to the engineer using AI.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts customer inquiries, a hearing unit that hears confirmation matters based on the inquiry content received by the reception unit; a classification unit that classifies the inquiry content based on the content heard by the hearing unit; a providing unit that provides information to an engineer based on the content separated by the separating unit. A system characterized by:

2. The hearing section Create a list of items to check and present it to the customer 2. The system of claim 1.

3. The cutting section is Identify suspected defects 2. The system of claim 1.

4. The providing unit Provide the engineer with the contents of the error message and the customer's environment information 2. The system of claim 1.

5. The reception unit Available 24 hours a day 2. The system of claim 1.

6. The reception unit Supports multiple languages 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the way inquiries are received based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze past inquiry history and select the appropriate reception method 2. The system of claim 1.

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