system

Abstract

The system according to this embodiment aims to automatically acquire necessary information for customer support and enable a rapid response. [Solution] The system according to the embodiment comprises a confirmation unit, a questioning unit, a presentation unit, and a linking unit. The confirmation unit grasps the content of the conversation and the input content. The questioning unit automatically asks the customer questions about the missing perspectives identified by the confirmation unit. The presentation unit presents relevant articles based on the information obtained by the questioning unit. The linking unit links with an existing customer support system.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that necessary information in customer support may be insufficient, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to automatically acquire necessary information in customer support and enable a quick response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a confirmation unit, a questioning unit, a presentation unit, and a linking unit. The confirmation unit grasps the content of the conversation and the input content. The questioning unit automatically asks the customer questions about the missing perspectives identified by the confirmation unit. The presentation unit presents relevant articles based on the information obtained by the questioning unit. The linking unit links with an existing customer support system. [Effects of the Invention]

[0007] The system according to this embodiment can automatically acquire necessary information for customer support, enabling a quick response. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The customer support system according to an embodiment of the present invention is a system that uses a generating AI to quickly and efficiently respond to customer incident reports received over the phone or via a form at an inquiry desk such as product support. This customer support system instantly grasps the content of the conversation and input content and checks whether pre-set hearing points have been obtained. For example, this includes information such as the content of the error message, the model of the terminal used, and the frequency of occurrence. The generating AI analyzes this information in real time and identifies the missing points. Next, the generating AI automatically asks the customer questions about the missing points and obtains the necessary information. For example, the generating AI automatically creates a question such as, "Could you tell me the error message displayed on the screen?" and presents it to the customer. This allows customer support staff to quickly collect the necessary information. Furthermore, the generating AI presents the customer with relevant articles and knowledge base information during the input process, providing prompt customer support. For example, it can present solutions for similar error incidents based on past knowledge base information. This allows customers to receive assistance in self-resolution, improving the efficiency of customer support. This customer support system operates in conjunction with existing customer support systems. Customer support administrators need to register confirmation points according to the incident category in the service in advance. The AI ​​generator compares these verification points with the input information in real time to create a written interview response. This system enables fast and efficient customer support at the product support inquiry desk. It allows for early resolution of customer problems and improved satisfaction. As a result, the customer support system can respond to customer inquiries quickly and efficiently.

[0029] The customer support system according to this embodiment comprises a confirmation unit, a questioning unit, a presentation unit, and a linking unit. The confirmation unit grasps the content of conversations and inputs. The content of conversations and inputs include, but are not limited to, text messages, voice input, and form inputs. The confirmation unit analyzes text messages and extracts important information. The confirmation unit can also analyze voice input and convert it to text using speech recognition technology. Furthermore, the confirmation unit can analyze form inputs and extract necessary information. For example, the confirmation unit analyzes text messages using natural language processing technology and extracts important keywords. Voice input is converted to text using speech recognition technology, and the text is analyzed. Form input is analyzed based on pre-set items, and necessary information is extracted. The questioning unit automatically asks the customer questions about the missing perspectives identified by the confirmation unit. The questioning unit automatically creates and presents questions to the customer, for example, "Could you tell me the error message displayed on the screen?" The questioning unit can generate appropriate questions using natural language generation technology. For example, the questioning unit generates appropriate questions using a template-based question generation algorithm. The questioning unit can also generate questions based on trigger conditions. For example, if an error message is missing, the questioning unit generates a question about the error message. The presentation unit presents relevant articles based on the information obtained by the questioning unit. For example, the presentation unit presents solutions to similar error events based on past knowledge base data. The presentation unit can select appropriate articles using a knowledge base search algorithm. For example, the presentation unit searches the knowledge base database and presents relevant articles. The presentation unit can also present the most suitable articles based on article ranking criteria. For example, the presentation unit updates the knowledge base information to provide the latest information. The integration unit integrates with existing customer support systems. For example, the integration unit allows customer support administrators to pre-register verification points for each event category in the service. The integration unit can integrate with CRM systems and ticket management systems to provide efficient support.For example, the integration unit can refer to information registered in the CRM system and take appropriate action. Furthermore, the integration unit can also integrate with the ticket management system to manage the progress of inquiries. As a result, the customer support system according to this embodiment can respond to customer inquiries quickly and efficiently.

[0030] The verification unit understands the content of conversations and inputs. This includes, but is not limited to, text messages, voice input, and form input. For example, the verification unit analyzes text messages and extracts important information. Specifically, it uses natural language processing technology to analyze text messages and understand the context and intent. This allows for an accurate understanding of what the user wants and what problems they are facing. The verification unit can also analyze voice input and convert it to text using speech recognition technology. The speech recognition technology uses a deep learning model to achieve high-precision speech recognition. This allows for accurate text conversion and analysis of user voice input. Furthermore, the verification unit can analyze form inputs and extract necessary information. Form inputs are analyzed based on pre-configured fields, and necessary information is extracted. For example, the user's name, contact information, and inquiry details are automatically extracted and stored in a database. This allows the verification unit to handle diverse input formats and efficiently collect information. Furthermore, the verification unit can centrally manage the collected information and collaborate with other departments and systems. For example, the collected information is stored on a cloud server, making it accessible to the questioning and presentation units. Furthermore, the verification unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific situations and conditions. This allows the verification unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The questioning unit automatically asks the customer questions about the missing information identified by the verification unit. For example, the questioning unit automatically creates and presents questions such as, "Could you please tell me the error message displayed on the screen?" The questioning unit can generate appropriate questions using natural language generation technology. Specifically, the questioning unit generates appropriate questions using a template-based question generation algorithm. The template-based algorithm generates questions that are appropriate to the user's situation based on pre-configured question templates. For example, if an error message is missing, it will generate a question such as, "Could you please tell me the content of the error message?" The questioning unit can also generate questions based on trigger conditions. Trigger conditions are rules that generate questions when specific situations or conditions are met. For example, if a user enters a specific keyword, it will generate a question related to that keyword. Furthermore, the questioning unit can choose the appropriate timing and method when presenting the generated questions to the user. For example, it can present questions in real time via a chatbot or send questions via email. This allows the questioning unit to quickly present appropriate questions to the user and efficiently collect the necessary information. In addition, the questioning unit can analyze the user's answers and determine whether additional questions are needed. This allows the questioning unit to reliably collect necessary information through interaction with users, thereby improving the overall efficiency of the system.

[0032] The presentation unit presents relevant articles based on information obtained by the questioning unit. For example, the presentation unit can present solutions to similar error events based on past knowledge base data. The presentation unit can select appropriate articles using a knowledge base search algorithm. Specifically, the presentation unit searches the knowledge base database and presents relevant articles. The search algorithm uses keyword matching and contextual analysis to identify the articles most relevant to the user's problem. For example, if a user reports a problem of "unable to log in," the presentation unit searches the knowledge base for articles on "solutions to login errors" and presents them. The presentation unit can also present the most suitable articles based on article ranking criteria. Ranking criteria are rules that prioritize the presentation of the most reliable articles, taking into account factors such as the number of views, ratings, and update date. This allows the presentation unit to quickly provide the user with the most appropriate solution. Furthermore, the presentation unit can update the knowledge base information to provide the latest information. For example, if a new error event occurs, its solution is added to the knowledge base and reflected in subsequent search results. This allows the information display unit to provide highly accurate support based on the latest information at all times, helping users solve their problems. Furthermore, the information display unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. As a result, the information display unit can provide users with prompt and appropriate support, improving the reliability and efficiency of the entire system.

[0033] The Integration Department integrates with existing customer support systems. For example, customer support managers can pre-register verification points for each incident category in the service. The Integration Department can also integrate with CRM systems and ticket management systems to provide efficient support. Specifically, the Integration Department refers to information registered in the CRM system and takes appropriate action. The CRM system manages customer information and past inquiry history, and the Integration Department can utilize this information to provide quick and appropriate responses. The Integration Department can also integrate with ticket management systems to manage the progress of inquiries. A ticket management system issues tickets for each inquiry and manages their progress and response history. By integrating with the ticket management system, the Integration Department can grasp the status of inquiries in real time and take appropriate action. Furthermore, the Integration Department can also integrate with other support tools and systems. For example, it can integrate with chatbots and FAQ systems to automatically provide answers to user inquiries. This allows the Integration Department to integrate multiple systems and tools to provide efficient and effective support. In addition, the Integration Department can promote the automation of the support process and support the efficient use of human resources. For example, tickets can be automatically issued or assigned to specific personnel when certain conditions are met. This allows the integration department to respond to customer inquiries quickly and efficiently, improving the overall system performance.

[0034] The verification unit can analyze information such as the content of the error message, the terminal model used, and the frequency of occurrence in real time. For example, the verification unit can analyze the content of the error message to identify the cause of the problem. The verification unit can also analyze the terminal model used to identify problems associated with a specific terminal. The verification unit can also analyze the frequency of occurrence to identify frequently occurring problems. For example, the verification unit can analyze the content of the error message using natural language processing technology to identify the cause of the problem. The terminal model used is analyzed by analyzing device information to identify problems associated with a specific terminal. The frequency of occurrence is analyzed by analyzing database information to identify frequently occurring problems. This enables rapid problem solving by analyzing information such as the content of the error message, the terminal model used, and the frequency of occurrence in real time. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input the content of the error message into a generation AI, and the generation AI can identify the cause of the problem.

[0035] The questioning unit can automatically generate and present questions to customers, such as "Could you please tell me the error message displayed on the screen?". The questioning unit can generate appropriate questions using, for example, natural language generation technology. The questioning unit can also generate appropriate questions using a template-based question generation algorithm. The questioning unit can also generate questions based on trigger conditions. For example, the questioning unit can generate a question about an error message if the error message is missing. The questioning unit can generate appropriate questions using a generation AI. For example, the questioning unit can input a prompt to the generation AI, and the generation AI can generate an appropriate question. This allows for the rapid collection of necessary information by automatically creating and presenting questions to customers. Some or all of the above-described processes in the questioning unit may be performed using a generation AI or not. For example, the questioning unit can input a prompt to the generation AI, and the generation AI can generate an appropriate question.

[0036] The presentation unit can present solutions to similar error events based on past knowledge base data. For example, the presentation unit can select appropriate articles using a knowledge base search algorithm. The presentation unit can also search the knowledge base database and present relevant articles. The presentation unit can also present the most suitable articles based on article ranking criteria. For example, the presentation unit can update the knowledge base information to provide the latest information. The presentation unit can select appropriate articles using generative AI. For example, the presentation unit can input a prompt to the generative AI, which then selects an appropriate article. This enables rapid problem resolution by presenting solutions based on past knowledge base data. Some or all of the above-described processes in the presentation unit may be performed using generative AI, or they may not. For example, the presentation unit can input a prompt to the generative AI, which then selects an appropriate article.

[0037] The integration unit allows customer support managers to pre-register verification points for each incident category within the service. The integration unit can, for example, integrate with CRM systems and ticket management systems to provide efficient support. The integration unit can also refer to information registered in the CRM system and take appropriate action. The integration unit can also integrate with ticket management systems to manage the progress of inquiries. For example, the integration unit can refer to information registered in the CRM system and take appropriate action. The integration unit can integrate with ticket management systems and manage the progress of inquiries. This enables efficient support by allowing customer support managers to pre-register verification points for each incident category. Some or all of the above processing in the integration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the integration unit can input prompts into the generation AI, which can then take appropriate action.

[0038] The verification unit can improve analysis accuracy by referring to the user's past inquiry history when analyzing conversation content and input content. For example, the verification unit can refer to error messages previously reported by the user, allowing the generating AI to quickly identify similar problems. The verification unit can also identify frequently used terminal models from the user's past inquiry history and incorporate this into the analysis. The verification unit can also refer to the frequency of problems previously reported by the user, allowing the generating AI to prioritize their analysis. For example, the verification unit can retrieve the user's past inquiry history from a database, and the generating AI can use this for analysis. This improves analysis accuracy by referring to the user's past inquiry history. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's past inquiry history to improve analysis accuracy.

[0039] The verification unit can adjust the analysis method during analysis, taking into account the user's environment (e.g., network status and device status). For example, if the user's network status is unstable, the generation AI will prioritize offline analysis. The verification unit can also consider the user's device status (e.g., battery level) and have the generation AI perform a rapid analysis. The verification unit can also have the generation AI select the optimal analysis method according to the user's environment. For example, the verification unit can monitor the user's network status in real time, and the generation AI can select an appropriate analysis method. This allows for the selection of a more appropriate analysis method by considering the user's environment. Some or all of the above-described processes in the verification unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the verification unit can input a prompt to the generation AI, which can analyze the user's environment and select the optimal analysis method.

[0040] The verification unit can prioritize the analysis of highly relevant information by considering the user's geographical location during the analysis process. For example, if the user is in a specific region, the verification unit will prioritize the analysis of error messages related to that region. The verification unit can also use the user's geographical location information to enable the generating AI to identify region-specific problems. The verification unit can also consider the user's location information and enable the generating AI to select the optimal analysis method. For example, the verification unit can acquire the user's geographical location information in real time, and the generating AI can select an appropriate analysis method. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and prioritize the analysis of highly relevant information.

[0041] The verification unit can analyze the user's social media activity during analysis and obtain relevant information. For example, the verification unit can use a generating AI to analyze error messages reported by the user on social media. The verification unit can also obtain information about the device model used from the user's social media activity. The verification unit can also analyze the user's social media activity and have the generating AI identify relevant information. For example, the verification unit can retrieve the user's social media activity from a database, and the generating AI can use it for analysis. This allows the verification unit to obtain relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's social media activity and obtain relevant information.

[0042] The questioning unit can generate the most suitable question by referring to the user's past answer history when creating a question. For example, the questioning unit can use the generating AI to create a relevant question based on the user's past answers. The questioning unit can also use the generating AI to identify the most suitable question from the user's past answer history. The questioning unit can also use the generating AI to create an efficient question by referring to the user's past answer history. For example, the questioning unit can retrieve the user's past answer history from a database, and the generating AI can use it for analysis. This allows the system to generate the most suitable question by referring to the user's past answer history. Some or all of the above processes in the questioning unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's past answer history and generate the most suitable question.

[0043] The questioning unit can adjust the question content when creating it, taking into account the user's current situation (e.g., time of day and location). For example, if a user is making an inquiry at night, the generating AI will create a concise question. The questioning unit can also have the generating AI create a question relevant to the user's location if the user is in a specific location. The questioning unit can also have the generating AI create the most appropriate question by considering the user's current situation. For example, the questioning unit can acquire the user's current situation in real time, and the generating AI can create an appropriate question. This allows for the generation of more appropriate questions by considering the user's current situation. Some or all of the above processing in the questioning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's current situation and create the most appropriate question.

[0044] The questioning unit can prioritize generating highly relevant questions by considering the user's geographical location information when creating questions. For example, if the user is in a specific region, the questioning unit's generating AI can create questions related to that region. The questioning unit can also have the generating AI create questions about region-specific issues based on the user's geographical location information. The questioning unit can also have the generating AI create optimal questions by considering the user's location information. For example, the questioning unit can acquire the user's geographical location information in real time, and the generating AI can create appropriate questions. This allows for the priority generation of highly relevant questions by considering the user's geographical location information. Some or all of the above processing in the questioning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and generate highly relevant questions.

[0045] The questioning unit can analyze a user's social media activity and generate relevant questions when creating questions. For example, the questioning unit can use a generating AI to create relevant questions based on what the user has reported on social media. The questioning unit can also use a generating AI to identify the most appropriate questions from the user's social media activity. The questioning unit can analyze a user's social media activity and have the generating AI create efficient questions. For example, the questioning unit can retrieve the user's social media activity from a database, and the generating AI can use it for analysis. This allows the questioning unit to generate relevant questions by analyzing the user's social media activity. Some or all of the above processes in the questioning unit may be performed using a generating AI, or they may not be performed using a generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's social media activity and generate relevant questions.

[0046] The presentation unit can select the most suitable article by referring to the user's past browsing history when presenting articles. For example, the presentation unit can use a generating AI to select relevant articles based on articles the user has previously viewed. The presentation unit can also use a generating AI to identify the most suitable article from the user's past browsing history. The presentation unit can also use a generating AI to select an efficient article by referring to the user's past browsing history. For example, the presentation unit can retrieve the user's past browsing history from a database, and the generating AI can use it for analysis. This allows the optimal article to be selected by referring to the user's past browsing history. Some or all of the above-described processes in the presentation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's past browsing history and select the most suitable article.

[0047] The presentation unit can adjust the article content when presenting an article, taking into account the user's current situation (e.g., time of day and location). For example, if a user is making an inquiry at night, the generating AI will present a concise article. The presentation unit can also have the generating AI present articles relevant to a specific location if the user is in that location. The presentation unit can also have the generating AI present the most suitable article by considering the user's current situation. For example, the presentation unit can acquire the user's current situation in real time, and the generating AI will present an appropriate article. This allows for the presentation of more appropriate article content by considering the user's current situation. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's current situation and present the most suitable article.

[0048] The presentation unit can prioritize the presentation of highly relevant articles by considering the user's geographical location information when presenting articles. For example, if the user is in a specific region, the presentation unit's generating AI can present articles related to that region. The presentation unit can also have the generating AI present articles on region-specific issues based on the user's geographical location information. The presentation unit can also have the generating AI present the most suitable articles by considering the user's location information. For example, the presentation unit can acquire the user's geographical location information in real time, and the generating AI can present appropriate articles. This allows for the priority presentation of highly relevant articles by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and present highly relevant articles.

[0049] The presentation unit can analyze the user's social media activity and present relevant articles when presenting articles. For example, the presentation unit can use a generative AI to present relevant articles based on content reported by the user on social media. The presentation unit can also use a generative AI to identify the most suitable articles from the user's social media activity. The presentation unit can analyze the user's social media activity and have the generative AI present efficient articles. For example, the presentation unit can retrieve the user's social media activity from a database, and the generative AI can use this for analysis. This allows the presentation unit to present relevant articles by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input a prompt to the generative AI, which can then analyze the user's social media activity and present relevant articles.

[0050] The integration unit can select the optimal integration method by referring to past integration history during integration. For example, the integration unit's generating AI can select the optimal integration method based on integration methods previously used by the user. The integration unit can also identify efficient integration methods from the user's past integration history using the generating AI. The integration unit can also select a rapid integration method by referring to the user's past integration history using the generating AI. For example, the integration unit can retrieve the user's past integration history from a database, and the generating AI can use it for analysis. This allows the optimal integration method to be selected by referring to past integration history. Some or all of the above-described processes in the integration unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the integration unit can input a prompt to the generating AI, which can then analyze the user's past integration history and select the optimal integration method.

[0051] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific region, the generating AI will select an integration method relevant to that region. The integration unit can also have the generating AI select a region-specific integration method based on the user's geographical location information. The integration unit can also have the generating AI select the optimal integration method by considering the user's location information. For example, the integration unit can acquire the user's geographical location information in real time, and the generating AI will select an appropriate integration method. This allows for the selection of the optimal integration method by considering the user's geographical location information. Some or all of the above-described processes in the integration unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the integration unit can input a prompt to the generating AI, which will analyze the user's geographical location information and select the optimal integration method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The verification unit can improve analysis accuracy by referring to the user's past inquiry history when analyzing conversation content and input content. For example, by referring to error messages previously reported by the user, the generating AI can quickly identify similar problems. It can also identify frequently used terminal models from the user's past inquiry history and incorporate this into the analysis. The generating AI can also prioritize analysis by referring to the frequency of occurrence of problems previously reported by the user. In this way, the analysis accuracy is improved by referring to the user's past inquiry history.

[0054] The question generation function can create optimal questions by referencing the user's past answer history. For example, the generation AI can create relevant questions based on the user's past answers. The generation AI can also identify the most suitable question from the user's past answer history. The generation AI can create efficient questions by referring to the user's past answer history. In this way, the optimal question can be generated by referring to the user's past answer history.

[0055] The presentation unit can select the most suitable article by referring to the user's past browsing history when presenting articles. For example, the generating AI can select relevant articles based on articles the user has previously viewed. The generating AI can also identify the most suitable article from the user's past browsing history. The generating AI can also select an efficient article by referring to the user's past browsing history. In this way, the most suitable article can be selected by referring to the user's past browsing history.

[0056] The verification unit can adjust the analysis method during analysis, taking into account the user's environment (e.g., network status and device status). For example, if the user's network status is unstable, the generating AI will prioritize offline analysis. The generating AI can also perform a rapid analysis by considering the user's device status (e.g., battery level). The generating AI can also select the optimal analysis method according to the user's environment. This allows for the selection of a more appropriate analysis method by considering the user's environment.

[0057] The question generation function can adjust the question content by considering the user's current situation (e.g., time of day and location) when creating a question. For example, if a user is making an inquiry at night, the generation AI will create a concise question. If the user is in a specific location, the generation AI can also create a question related to that location. The generation AI can also create the most appropriate question by considering the user's current situation. This allows for the generation of more appropriate questions by taking the user's current situation into account.

[0058] The presentation unit can prioritize the display of highly relevant articles by considering the user's geographical location when presenting articles. For example, if the user is in a specific region, the generating AI will present articles related to that region. Based on the user's geographical location, the generating AI can also present articles on region-specific issues. The generating AI can also present the most suitable articles by considering the user's location. In this way, by considering the user's geographical location, highly relevant articles can be prioritized.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The verification unit grasps the content of the conversation and input. This includes text messages, voice input, and form input. The verification unit analyzes text messages and extracts important information. It can also analyze voice input and convert it to text using speech recognition technology. Furthermore, it analyzes form input and extracts necessary information. Step 2: The questioning unit automatically asks the customer questions about the missing aspects identified by the verification unit. The questioning unit uses natural language generation technology to generate appropriate questions and present them to the customer. For example, it automatically creates questions such as, "Could you please tell me the error message displayed on the screen?" Step 3: The presentation unit presents relevant articles based on the information obtained by the questioning unit. The presentation unit uses a knowledge base search algorithm to select appropriate articles and presents solutions for similar error cases based on past knowledge base data. Step 4: The integration unit integrates with existing customer support systems. The integration unit integrates with CRM systems and ticket management systems to provide efficient support. For example, it refers to information registered in the CRM system and takes appropriate action. It also integrates with ticket management systems to manage the progress of inquiries.

[0061] (Example of form 2) The customer support system according to an embodiment of the present invention is a system that uses a generating AI to quickly and efficiently respond to customer incident reports received over the phone or via a form at an inquiry desk such as product support. This customer support system instantly grasps the content of the conversation and input content and checks whether pre-set hearing points have been obtained. For example, this includes information such as the content of the error message, the model of the terminal used, and the frequency of occurrence. The generating AI analyzes this information in real time and identifies the missing points. Next, the generating AI automatically asks the customer questions about the missing points and obtains the necessary information. For example, the generating AI automatically creates a question such as, "Could you tell me the error message displayed on the screen?" and presents it to the customer. This allows customer support staff to quickly collect the necessary information. Furthermore, the generating AI presents the customer with relevant articles and knowledge base information during the input process, providing prompt customer support. For example, it can present solutions for similar error incidents based on past knowledge base information. This allows customers to receive assistance in self-resolution, improving the efficiency of customer support. This customer support system operates in conjunction with existing customer support systems. Customer support administrators need to register confirmation points according to the incident category in the service in advance. The AI ​​generator compares these verification points with the input information in real time to create a written interview response. This system enables fast and efficient customer support at the product support inquiry desk. It allows for early resolution of customer problems and improved satisfaction. As a result, the customer support system can respond to customer inquiries quickly and efficiently.

[0062] The customer support system according to this embodiment comprises a confirmation unit, a questioning unit, a presentation unit, and a linking unit. The confirmation unit grasps the content of conversations and inputs. The content of conversations and inputs include, but are not limited to, text messages, voice input, and form inputs. The confirmation unit analyzes text messages and extracts important information. The confirmation unit can also analyze voice input and convert it to text using speech recognition technology. Furthermore, the confirmation unit can analyze form inputs and extract necessary information. For example, the confirmation unit analyzes text messages using natural language processing technology and extracts important keywords. Voice input is converted to text using speech recognition technology, and the text is analyzed. Form input is analyzed based on pre-set items, and necessary information is extracted. The questioning unit automatically asks the customer questions about the missing perspectives identified by the confirmation unit. The questioning unit automatically creates and presents questions to the customer, for example, "Could you tell me the error message displayed on the screen?" The questioning unit can generate appropriate questions using natural language generation technology. For example, the questioning unit generates appropriate questions using a template-based question generation algorithm. The questioning unit can also generate questions based on trigger conditions. For example, if an error message is missing, the questioning unit generates a question about the error message. The presentation unit presents relevant articles based on the information obtained by the questioning unit. For example, the presentation unit presents solutions to similar error events based on past knowledge base data. The presentation unit can select appropriate articles using a knowledge base search algorithm. For example, the presentation unit searches the knowledge base database and presents relevant articles. The presentation unit can also present the most suitable articles based on article ranking criteria. For example, the presentation unit updates the knowledge base information to provide the latest information. The integration unit integrates with existing customer support systems. For example, the integration unit allows customer support administrators to pre-register verification points for each event category in the service. The integration unit can integrate with CRM systems and ticket management systems to provide efficient support.For example, the integration unit can refer to information registered in the CRM system and take appropriate action. Furthermore, the integration unit can also integrate with the ticket management system to manage the progress of inquiries. As a result, the customer support system according to this embodiment can respond to customer inquiries quickly and efficiently.

[0063] The verification unit understands the content of conversations and inputs. This includes, but is not limited to, text messages, voice input, and form input. For example, the verification unit analyzes text messages and extracts important information. Specifically, it uses natural language processing technology to analyze text messages and understand the context and intent. This allows for an accurate understanding of what the user wants and what problems they are facing. The verification unit can also analyze voice input and convert it to text using speech recognition technology. The speech recognition technology uses a deep learning model to achieve high-precision speech recognition. This allows for accurate text conversion and analysis of user voice input. Furthermore, the verification unit can analyze form inputs and extract necessary information. Form inputs are analyzed based on pre-configured fields, and necessary information is extracted. For example, the user's name, contact information, and inquiry details are automatically extracted and stored in a database. This allows the verification unit to handle diverse input formats and efficiently collect information. Furthermore, the verification unit can centrally manage the collected information and collaborate with other departments and systems. For example, the collected information is stored on a cloud server, making it accessible to the questioning and presentation units. Furthermore, the verification unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific situations and conditions. This allows the verification unit to collect data efficiently and effectively, improving the overall system performance.

[0064] The questioning unit automatically asks the customer questions about the missing information identified by the verification unit. For example, the questioning unit automatically creates and presents questions such as, "Could you please tell me the error message displayed on the screen?" The questioning unit can generate appropriate questions using natural language generation technology. Specifically, the questioning unit generates appropriate questions using a template-based question generation algorithm. The template-based algorithm generates questions that are appropriate to the user's situation based on pre-configured question templates. For example, if an error message is missing, it will generate a question such as, "Could you please tell me the content of the error message?" The questioning unit can also generate questions based on trigger conditions. Trigger conditions are rules that generate questions when specific situations or conditions are met. For example, if a user enters a specific keyword, it will generate a question related to that keyword. Furthermore, the questioning unit can choose the appropriate timing and method when presenting the generated questions to the user. For example, it can present questions in real time via a chatbot or send questions via email. This allows the questioning unit to quickly present appropriate questions to the user and efficiently collect the necessary information. In addition, the questioning unit can analyze the user's answers and determine whether additional questions are needed. This allows the questioning unit to reliably collect necessary information through interaction with users, thereby improving the overall efficiency of the system.

[0065] The presentation unit presents relevant articles based on information obtained by the questioning unit. For example, the presentation unit can present solutions to similar error events based on past knowledge base data. The presentation unit can select appropriate articles using a knowledge base search algorithm. Specifically, the presentation unit searches the knowledge base database and presents relevant articles. The search algorithm uses keyword matching and contextual analysis to identify the articles most relevant to the user's problem. For example, if a user reports a problem of "unable to log in," the presentation unit searches the knowledge base for articles on "solutions to login errors" and presents them. The presentation unit can also present the most suitable articles based on article ranking criteria. Ranking criteria are rules that prioritize the presentation of the most reliable articles, taking into account factors such as the number of views, ratings, and update date. This allows the presentation unit to quickly provide the user with the most appropriate solution. Furthermore, the presentation unit can update the knowledge base information to provide the latest information. For example, if a new error event occurs, its solution is added to the knowledge base and reflected in subsequent search results. This allows the information display unit to provide highly accurate support based on the latest information at all times, helping users solve their problems. Furthermore, the information display unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. As a result, the information display unit can provide users with prompt and appropriate support, improving the reliability and efficiency of the entire system.

[0066] The Integration Department integrates with existing customer support systems. For example, customer support managers can pre-register verification points for each incident category in the service. The Integration Department can also integrate with CRM systems and ticket management systems to provide efficient support. Specifically, the Integration Department refers to information registered in the CRM system and takes appropriate action. The CRM system manages customer information and past inquiry history, and the Integration Department can utilize this information to provide quick and appropriate responses. The Integration Department can also integrate with ticket management systems to manage the progress of inquiries. A ticket management system issues tickets for each inquiry and manages their progress and response history. By integrating with the ticket management system, the Integration Department can grasp the status of inquiries in real time and take appropriate action. Furthermore, the Integration Department can also integrate with other support tools and systems. For example, it can integrate with chatbots and FAQ systems to automatically provide answers to user inquiries. This allows the Integration Department to integrate multiple systems and tools to provide efficient and effective support. In addition, the Integration Department can promote the automation of the support process and support the efficient use of human resources. For example, tickets can be automatically issued or assigned to specific personnel when certain conditions are met. This allows the integration department to respond to customer inquiries quickly and efficiently, improving the overall system performance.

[0067] The verification unit can analyze information such as the content of the error message, the terminal model used, and the frequency of occurrence in real time. For example, the verification unit can analyze the content of the error message to identify the cause of the problem. The verification unit can also analyze the terminal model used to identify problems associated with a specific terminal. The verification unit can also analyze the frequency of occurrence to identify frequently occurring problems. For example, the verification unit can analyze the content of the error message using natural language processing technology to identify the cause of the problem. The terminal model used is analyzed by analyzing device information to identify problems associated with a specific terminal. The frequency of occurrence is analyzed by analyzing database information to identify frequently occurring problems. This enables rapid problem solving by analyzing information such as the content of the error message, the terminal model used, and the frequency of occurrence in real time. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input the content of the error message into a generation AI, and the generation AI can identify the cause of the problem.

[0068] The questioning unit can automatically generate and present questions to customers, such as "Could you please tell me the error message displayed on the screen?". The questioning unit can generate appropriate questions using, for example, natural language generation technology. The questioning unit can also generate appropriate questions using a template-based question generation algorithm. The questioning unit can also generate questions based on trigger conditions. For example, the questioning unit can generate a question about an error message if the error message is missing. The questioning unit can generate appropriate questions using a generation AI. For example, the questioning unit can input a prompt to the generation AI, and the generation AI can generate an appropriate question. This allows for the rapid collection of necessary information by automatically creating and presenting questions to customers. Some or all of the above-described processes in the questioning unit may be performed using a generation AI or not. For example, the questioning unit can input a prompt to the generation AI, and the generation AI can generate an appropriate question.

[0069] The presentation unit can present solutions to similar error events based on past knowledge base data. For example, the presentation unit can select appropriate articles using a knowledge base search algorithm. The presentation unit can also search the knowledge base database and present relevant articles. The presentation unit can also present the most suitable articles based on article ranking criteria. For example, the presentation unit can update the knowledge base information to provide the latest information. The presentation unit can select appropriate articles using generative AI. For example, the presentation unit can input a prompt to the generative AI, which then selects an appropriate article. This enables rapid problem resolution by presenting solutions based on past knowledge base data. Some or all of the above-described processes in the presentation unit may be performed using generative AI, or they may not. For example, the presentation unit can input a prompt to the generative AI, which then selects an appropriate article.

[0070] The integration unit allows customer support managers to pre-register verification points for each incident category within the service. The integration unit can, for example, integrate with CRM systems and ticket management systems to provide efficient support. The integration unit can also refer to information registered in the CRM system and take appropriate action. The integration unit can also integrate with ticket management systems to manage the progress of inquiries. For example, the integration unit can refer to information registered in the CRM system and take appropriate action. The integration unit can integrate with ticket management systems and manage the progress of inquiries. This enables efficient support by allowing customer support managers to pre-register verification points for each incident category. Some or all of the above processing in the integration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the integration unit can input prompts into the generation AI, which can then take appropriate action.

[0071] The verification unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the verification unit may have the generating AI prioritize the analysis of error messages. If the user is relaxed, the verification unit may have the generating AI prioritize the analysis of detailed information about the device model being used. If the user is in a hurry, the verification unit may have the generating AI prioritize the analysis of frequently occurring problems. For example, the verification unit estimates the user's emotions using an emotion engine or generating AI and adjusts the analysis priority based on the estimated emotions. This allows for a more appropriate response by adjusting the analysis priority based on the user's emotions. Some or all of the above processing in the verification unit may be performed using the generating AI or not. For example, the verification unit may input a prompt to the generating AI, which may estimate the user's emotions and adjust the analysis priority.

[0072] The verification unit can improve analysis accuracy by referring to the user's past inquiry history when analyzing conversation content and input content. For example, the verification unit can refer to error messages previously reported by the user, allowing the generating AI to quickly identify similar problems. The verification unit can also identify frequently used terminal models from the user's past inquiry history and incorporate this into the analysis. The verification unit can also refer to the frequency of problems previously reported by the user, allowing the generating AI to prioritize their analysis. For example, the verification unit can retrieve the user's past inquiry history from a database, and the generating AI can use this for analysis. This improves analysis accuracy by referring to the user's past inquiry history. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's past inquiry history to improve analysis accuracy.

[0073] The verification unit can adjust the analysis method during analysis, taking into account the user's environment (e.g., network status and device status). For example, if the user's network status is unstable, the generation AI will prioritize offline analysis. The verification unit can also consider the user's device status (e.g., battery level) and have the generation AI perform a rapid analysis. The verification unit can also have the generation AI select the optimal analysis method according to the user's environment. For example, the verification unit can monitor the user's network status in real time, and the generation AI can select an appropriate analysis method. This allows for the selection of a more appropriate analysis method by considering the user's environment. Some or all of the above-described processes in the verification unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the verification unit can input a prompt to the generation AI, which can analyze the user's environment and select the optimal analysis method.

[0074] The verification unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the verification unit can use the generating AI to provide a simple and highly visible display method. If the user is relaxed, the verification unit can also use the generating AI to provide a display method that includes detailed information. If the user is in a hurry, the verification unit can also use the generating AI to provide a concise display method. For example, the verification unit estimates the user's emotions using an emotion engine or generating AI, and adjusts the display method of the analysis results based on the estimated user emotions. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Some or all of the above processing in the verification unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then estimate the user's emotions and adjust the display method of the analysis results.

[0075] The verification unit can prioritize the analysis of highly relevant information by considering the user's geographical location during the analysis process. For example, if the user is in a specific region, the verification unit will prioritize the analysis of error messages related to that region. The verification unit can also use the user's geographical location information to enable the generating AI to identify region-specific problems. The verification unit can also consider the user's location information and enable the generating AI to select the optimal analysis method. For example, the verification unit can acquire the user's geographical location information in real time, and the generating AI can select an appropriate analysis method. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and prioritize the analysis of highly relevant information.

[0076] The verification unit can analyze the user's social media activity during analysis and obtain relevant information. For example, the verification unit can use a generating AI to analyze error messages reported by the user on social media. The verification unit can also obtain information about the device model used from the user's social media activity. The verification unit can also analyze the user's social media activity and have the generating AI identify relevant information. For example, the verification unit can retrieve the user's social media activity from a database, and the generating AI can use it for analysis. This allows the verification unit to obtain relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the verification unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the verification unit can input a prompt to the generating AI, which can then analyze the user's social media activity and obtain relevant information.

[0077] The questioning unit can estimate the user's emotions and adjust the wording of the question based on the estimated emotions. For example, if the user is stressed, the generating AI can create a simple and easy-to-understand question. If the user is relaxed, the generating AI can also create a more detailed question. If the user is in a hurry, the generating AI can also create a question that can be answered quickly. For example, the questioning unit estimates the user's emotions using an emotion engine or generating AI, and adjusts the wording of the question based on the estimated emotions. This allows for more appropriate questions by adjusting the wording of the question based on the user's emotions. Some or all of the above processing in the questioning unit may be performed using or without generating AI. For example, the questioning unit can input a prompt to the generating AI, which can estimate the user's emotions and adjust the wording of the question.

[0078] The questioning unit can generate the most suitable question by referring to the user's past answer history when creating a question. For example, the questioning unit can use the generating AI to create a relevant question based on the user's past answers. The questioning unit can also use the generating AI to identify the most suitable question from the user's past answer history. The questioning unit can also use the generating AI to create an efficient question by referring to the user's past answer history. For example, the questioning unit can retrieve the user's past answer history from a database, and the generating AI can use it for analysis. This allows the system to generate the most suitable question by referring to the user's past answer history. Some or all of the above processes in the questioning unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's past answer history and generate the most suitable question.

[0079] The questioning unit can adjust the question content when creating it, taking into account the user's current situation (e.g., time of day and location). For example, if a user is making an inquiry at night, the generating AI will create a concise question. The questioning unit can also have the generating AI create a question relevant to the user's location if the user is in a specific location. The questioning unit can also have the generating AI create the most appropriate question by considering the user's current situation. For example, the questioning unit can acquire the user's current situation in real time, and the generating AI can create an appropriate question. This allows for the generation of more appropriate questions by considering the user's current situation. Some or all of the above processing in the questioning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's current situation and create the most appropriate question.

[0080] The questioning unit can estimate the user's emotions and adjust the order of questions based on the estimated emotions. For example, if the user is nervous, the generating AI may start with simple questions. If the user is relaxed, the generating AI may ask more detailed questions first. If the user is in a hurry, the generating AI may prioritize important questions. For example, the questioning unit estimates the user's emotions using an emotion engine or generating AI and adjusts the order of questions based on the estimated emotions. This allows for more appropriate questions by adjusting the order of questions based on the user's emotions. Some or all of the above processing in the questioning unit may be performed using or without generating AI. For example, the questioning unit can input prompts to the generating AI, which can estimate the user's emotions and adjust the order of questions.

[0081] The questioning unit can prioritize generating highly relevant questions by considering the user's geographical location information when creating questions. For example, if the user is in a specific region, the questioning unit's generating AI can create questions related to that region. The questioning unit can also have the generating AI create questions about region-specific issues based on the user's geographical location information. The questioning unit can also have the generating AI create optimal questions by considering the user's location information. For example, the questioning unit can acquire the user's geographical location information in real time, and the generating AI can create appropriate questions. This allows for the priority generation of highly relevant questions by considering the user's geographical location information. Some or all of the above processing in the questioning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and generate highly relevant questions.

[0082] The questioning unit can analyze a user's social media activity and generate relevant questions when creating questions. For example, the questioning unit can use a generating AI to create relevant questions based on what the user has reported on social media. The questioning unit can also use a generating AI to identify the most appropriate questions from the user's social media activity. The questioning unit can analyze a user's social media activity and have the generating AI create efficient questions. For example, the questioning unit can retrieve the user's social media activity from a database, and the generating AI can use it for analysis. This allows the questioning unit to generate relevant questions by analyzing the user's social media activity. Some or all of the above processes in the questioning unit may be performed using a generating AI, or they may not be performed using a generating AI. For example, the questioning unit can input a prompt to the generating AI, which can then analyze the user's social media activity and generate relevant questions.

[0083] The presentation unit can estimate the user's emotions and determine the priority of articles to present based on the estimated emotions. For example, if the user is stressed, the presentation unit may prioritize articles that the generating AI can quickly resolve. If the user is relaxed, the presentation unit may also prioritize articles that include detailed explanations. If the user is in a hurry, the presentation unit may also prioritize articles that get straight to the point. For example, the presentation unit estimates the user's emotions using an emotion engine or generating AI, and determines the priority of articles to present based on the estimated emotions. This allows for the presentation of more appropriate articles by prioritizing articles based on the user's emotions. Some or all of the above processing in the presentation unit may be performed using or without generating AI. For example, the presentation unit can input a prompt to the generating AI, which can estimate the user's emotions and determine the priority of articles.

[0084] The presentation unit can select the most suitable article by referring to the user's past browsing history when presenting articles. For example, the presentation unit can use a generating AI to select relevant articles based on articles the user has previously viewed. The presentation unit can also use a generating AI to identify the most suitable article from the user's past browsing history. The presentation unit can also use a generating AI to select an efficient article by referring to the user's past browsing history. For example, the presentation unit can retrieve the user's past browsing history from a database, and the generating AI can use it for analysis. This allows the optimal article to be selected by referring to the user's past browsing history. Some or all of the above-described processes in the presentation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's past browsing history and select the most suitable article.

[0085] The presentation unit can adjust the article content when presenting an article, taking into account the user's current situation (e.g., time of day and location). For example, if a user is making an inquiry at night, the generating AI will present a concise article. The presentation unit can also have the generating AI present articles relevant to a specific location if the user is in that location. The presentation unit can also have the generating AI present the most suitable article by considering the user's current situation. For example, the presentation unit can acquire the user's current situation in real time, and the generating AI will present an appropriate article. This allows for the presentation of more appropriate article content by considering the user's current situation. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's current situation and present the most suitable article.

[0086] The presentation unit can estimate the user's emotions and adjust how the article is displayed based on those emotions. For example, if the user is nervous, the presentation unit can use a generative AI to provide a simple and highly visible display. If the user is relaxed, the presentation unit can use a generative AI to provide a display that includes detailed information. If the user is in a hurry, the presentation unit can use a generative AI to provide a concise display. For example, the presentation unit estimates the user's emotions using an emotion engine or generative AI and adjusts how the article is displayed based on those emotions. This allows for a more appropriate display by adjusting how the article is displayed based on the user's emotions. Some or all of the above processing in the presentation unit may be performed using a generative AI or not. For example, the presentation unit can input a prompt to the generative AI, which can then estimate the user's emotions and adjust how the article is displayed.

[0087] The presentation unit can prioritize the presentation of highly relevant articles by considering the user's geographical location information when presenting articles. For example, if the user is in a specific region, the presentation unit's generating AI can present articles related to that region. The presentation unit can also have the generating AI present articles on region-specific issues based on the user's geographical location information. The presentation unit can also have the generating AI present the most suitable articles by considering the user's location information. For example, the presentation unit can acquire the user's geographical location information in real time, and the generating AI can present appropriate articles. This allows for the priority presentation of highly relevant articles by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input a prompt to the generating AI, which can then analyze the user's geographical location information and present highly relevant articles.

[0088] The presentation unit can analyze the user's social media activity and present relevant articles when presenting articles. For example, the presentation unit can use a generative AI to present relevant articles based on content reported by the user on social media. The presentation unit can also use a generative AI to identify the most suitable articles from the user's social media activity. The presentation unit can analyze the user's social media activity and have the generative AI present efficient articles. For example, the presentation unit can retrieve the user's social media activity from a database, and the generative AI can use this for analysis. This allows the presentation unit to present relevant articles by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input a prompt to the generative AI, which can then analyze the user's social media activity and present relevant articles.

[0089] The integration unit can select the optimal integration method by referring to past integration history during integration. For example, the integration unit's generating AI can select the optimal integration method based on integration methods previously used by the user. The integration unit can also identify efficient integration methods from the user's past integration history using the generating AI. The integration unit can also select a rapid integration method by referring to the user's past integration history using the generating AI. For example, the integration unit can retrieve the user's past integration history from a database, and the generating AI can use it for analysis. This allows the optimal integration method to be selected by referring to past integration history. Some or all of the above-described processes in the integration unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the integration unit can input a prompt to the generating AI, which can then analyze the user's past integration history and select the optimal integration method.

[0090] The collaboration unit can estimate the user's emotions and adjust the frequency of collaboration based on the estimated emotions. For example, if the user is stressed, the collaboration unit's generating AI will increase the frequency of collaboration. If the user is relaxed, the collaboration unit's generating AI can also appropriately adjust the frequency of collaboration. If the user is in a hurry, the collaboration unit's generating AI can also optimize the frequency of collaboration. For example, the collaboration unit estimates the user's emotions using an emotion engine or generating AI, and adjusts the frequency of collaboration based on the estimated emotions. This allows for more appropriate collaboration by adjusting the frequency of collaboration based on the user's emotions. Some or all of the above processing in the collaboration unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the collaboration unit can input a prompt to the generating AI, which will estimate the user's emotions and adjust the frequency of collaboration.

[0091] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific region, the generating AI will select an integration method relevant to that region. The integration unit can also have the generating AI select a region-specific integration method based on the user's geographical location information. The integration unit can also have the generating AI select the optimal integration method by considering the user's location information. For example, the integration unit can acquire the user's geographical location information in real time, and the generating AI will select an appropriate integration method. This allows for the selection of the optimal integration method by considering the user's geographical location information. Some or all of the above-described processes in the integration unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the integration unit can input a prompt to the generating AI, which will analyze the user's geographical location information and select the optimal integration method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The verification unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize the analysis of error messages. If the user is relaxed, the generating AI can also prioritize the analysis of detailed information about the device model being used. If the user is in a hurry, the generating AI can also prioritize the analysis of frequently occurring problems. By adjusting the analysis priority based on the user's emotions, a more appropriate response becomes possible.

[0094] The questioning function can estimate the user's emotions and adjust the wording of the question based on those emotions. For example, if the user is stressed, the generating AI will create a simple and easy-to-understand question. If the user is relaxed, the generating AI can create a more detailed question. If the user is in a hurry, the generating AI can create a question that can be answered quickly. By adjusting the wording of questions based on the user's emotions, more appropriate questions can be asked.

[0095] The presentation unit can estimate the user's emotions and determine the priority of articles to present based on those emotions. For example, if the user is stressed, the generating AI will prioritize articles that can provide a quick solution. If the user is relaxed, the generating AI may prioritize articles with detailed explanations. If the user is in a hurry, the generating AI may prioritize articles that get straight to the point. In this way, by prioritizing articles based on the user's emotions, more appropriate articles can be presented.

[0096] The interaction unit can estimate the user's emotions and adjust the frequency of interaction based on those emotions. For example, if the user is stressed, the generating AI will increase the frequency of interaction. If the user is relaxed, the generating AI can also adjust the frequency of interaction appropriately. If the user is in a hurry, the generating AI can also optimize the frequency of interaction. By adjusting the frequency of interaction based on the user's emotions, more appropriate interaction becomes possible.

[0097] The verification unit can improve analysis accuracy by referring to the user's past inquiry history when analyzing conversation content and input content. For example, by referring to error messages previously reported by the user, the generating AI can quickly identify similar problems. It can also identify frequently used terminal models from the user's past inquiry history and incorporate this into the analysis. The generating AI can also prioritize analysis by referring to the frequency of occurrence of problems previously reported by the user. In this way, the analysis accuracy is improved by referring to the user's past inquiry history.

[0098] The question generation function can create optimal questions by referencing the user's past answer history. For example, the generation AI can create relevant questions based on the user's past answers. The generation AI can also identify the most suitable question from the user's past answer history. The generation AI can create efficient questions by referring to the user's past answer history. In this way, the optimal question can be generated by referring to the user's past answer history.

[0099] The presentation unit can select the most suitable article by referring to the user's past browsing history when presenting articles. For example, the generating AI can select relevant articles based on articles the user has previously viewed. The generating AI can also identify the most suitable article from the user's past browsing history. The generating AI can also select an efficient article by referring to the user's past browsing history. In this way, the most suitable article can be selected by referring to the user's past browsing history.

[0100] The verification unit can adjust the analysis method during analysis, taking into account the user's environment (e.g., network status and device status). For example, if the user's network status is unstable, the generating AI will prioritize offline analysis. The generating AI can also perform a rapid analysis by considering the user's device status (e.g., battery level). The generating AI can also select the optimal analysis method according to the user's environment. This allows for the selection of a more appropriate analysis method by considering the user's environment.

[0101] The question generation function can adjust the question content by considering the user's current situation (e.g., time of day and location) when creating a question. For example, if a user is making an inquiry at night, the generation AI will create a concise question. If the user is in a specific location, the generation AI can also create a question related to that location. The generation AI can also create the most appropriate question by considering the user's current situation. This allows for the generation of more appropriate questions by taking the user's current situation into account.

[0102] The presentation unit can prioritize the display of highly relevant articles by considering the user's geographical location when presenting articles. For example, if the user is in a specific region, the generating AI will present articles related to that region. Based on the user's geographical location, the generating AI can also present articles on region-specific issues. The generating AI can also present the most suitable articles by considering the user's location. In this way, by considering the user's geographical location, highly relevant articles can be prioritized.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The verification unit grasps the content of the conversation and input. This includes text messages, voice input, and form input. The verification unit analyzes text messages and extracts important information. It can also analyze voice input and convert it to text using speech recognition technology. Furthermore, it analyzes form input and extracts necessary information. Step 2: The questioning unit automatically asks the customer questions about the missing aspects identified by the verification unit. The questioning unit uses natural language generation technology to generate appropriate questions and present them to the customer. For example, it automatically creates questions such as, "Could you please tell me the error message displayed on the screen?" Step 3: The presentation unit presents relevant articles based on the information obtained by the questioning unit. The presentation unit uses a knowledge base search algorithm to select appropriate articles and presents solutions for similar error cases based on past knowledge base data. Step 4: The integration unit integrates with existing customer support systems. The integration unit integrates with CRM systems and ticket management systems to provide efficient support. For example, it refers to information registered in the CRM system and takes appropriate action. It also integrates with ticket management systems to manage the progress of inquiries.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the confirmation unit, questioning unit, presentation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the smart device 14 and grasps the content of the conversation and input. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically asks the customer questions about the missing perspectives. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents relevant articles. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and collaborates with an existing customer support system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the confirmation unit, questioning unit, presentation unit, and linking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the smart glasses 214 and grasps the content of the conversation and input. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically asks the customer questions about the missing perspectives. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents relevant articles. The linking unit is implemented by the specific processing unit 290 of the data processing unit 12 and links with an existing customer support system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the confirmation unit, questioning unit, presentation unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the headset terminal 314 and grasps the content of the conversation and input. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically asks the customer questions about the missing perspectives. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents relevant articles. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and collaborates with an existing customer support system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the confirmation unit, questioning unit, presentation unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the robot 414 and grasps the content of the conversation and input. The questioning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically asks the customer questions about the missing perspectives. The presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents relevant articles. The collaboration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collaborates with an existing customer support system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A confirmation unit that grasps the content of the conversation and input, The questioning unit automatically asks the customer questions about the missing perspectives identified by the verification unit, A presentation unit presents relevant articles based on the information obtained by the aforementioned questioning unit, It includes an integration unit that connects with the existing customer support system. A system characterized by the following features. (Note 2) The aforementioned verification unit is The system analyzes information such as the content of the error message, the model of the device being used, and the frequency of occurrence in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned question section is, The system automatically generates and presents questions such as, "Could you please tell me the error message displayed on the screen?" to the customer. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Based on past knowledge base information, we will provide solutions for similar error events. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Customer support administrators register verification criteria for each incident category in the service in advance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is When analyzing conversation content and input data, we improve analysis accuracy by referring to the user's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is During analysis, the analysis method is adjusted to take into account the user's environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is During analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is During analysis, the user's social media activity is analyzed to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question section is, When creating a question, the system generates the most suitable question by referring to the user's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question section is, When creating questions, adjust the question content to take into account the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question section is, The system estimates the user's emotions and adjusts the order of questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question section is, When creating questions, the system prioritizes generating highly relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question section is, When creating questions, the system analyzes the user's social media activity and generates relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, It estimates the user's emotions and determines the priority of articles to present based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting articles, the system selects the most relevant articles by referencing the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting an article, adjust the article content to take into account the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, It estimates the user's sentiment and adjusts how articles are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When displaying articles, the system prioritizes showing articles that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When displaying articles, the system analyzes the user's social media activity and presents relevant articles. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When integrating, the system will refer to past integration history to select the most suitable integration method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, When integrating, the system selects the optimal integration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A confirmation unit that grasps the content of the conversation and input, The questioning unit automatically asks the customer questions about the missing perspectives identified by the verification unit, A presentation unit presents relevant articles based on the information obtained by the aforementioned questioning unit, It includes an integration unit that connects with the existing customer support system. A system characterized by the following features.

2. The aforementioned verification unit is The system analyzes information such as the content of the error message, the model of the device being used, and the frequency of occurrence in real time. The system according to feature 1.

3. The aforementioned question section is, Automatically generate and present questions to customers. The system according to feature 1.

4. The aforementioned display unit is, Based on past knowledge base information, we will provide solutions for similar error events. The system according to feature 1.

5. The aforementioned linkage unit is, Customer support administrators register verification criteria for each incident category in the service in advance. The system according to feature 1.

6. The aforementioned verification unit is It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

7. The aforementioned verification unit is When analyzing conversation content and input data, we improve analysis accuracy by referring to the user's past inquiry history. The system according to feature 1.

8. The aforementioned verification unit is During analysis, the analysis method is adjusted to take into account the user's environment. The system according to feature 1.

9. The aforementioned verification unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned verification unit is During analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system according to feature 1.

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