system

Abstract

An object of a system according to an embodiment is to improve service quality by efficiently handling inquiries from customers and clients and solving problems.SOLUTION: A system according to an embodiment includes an inquiry handling unit, a problem solving unit, an appointment setting unit, and an FAQ responding unit. The inquiry handling unit handles an inquiry. The problem solving unit performs problem solving. The appointment setting unit sets an appointment. The FAQ response unit performs the FAQ response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently handle inquiries and solve problems from customers and clients, and there is room for improvement in improving service quality.

[0005] The system according to the embodiment aims to efficiently handle inquiries and solve problems from customers and clients, thereby improving service quality. [Means for solving the problem]

[0006] The system according to the embodiment includes an inquiry response unit, a problem resolution unit, an appointment setting unit, and an FAQ response unit. The inquiry response unit responds to inquiries. The problem resolution unit resolves problems. The appointment setting unit sets appointments. The FAQ response unit responds to FAQs. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently handle inquiries and solve problems from customers and clients, thereby improving service quality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​service desk according to the embodiment of the present invention is a system that uses conversational AI technology to handle inquiries, solve problems, set appointments, and answer FAQs. As a result, the AI ​​service desk can improve the efficiency of customer support and service quality for companies and organizations.

[0029] An AI service desk according to an embodiment includes an inquiry response unit, a problem resolution unit, an appointment setting unit, and an FAQ response unit. The inquiry response unit responds to inquiries. For example, the inquiry response unit analyzes the content of a customer's inquiry and generates an appropriate response. The inquiry response unit can also generate individually customized responses based on the customer's past inquiry history. The inquiry response unit can also analyze the customer's tone of voice and language and provide an appropriate response using an emotion estimation function. For example, if the customer is angry, the problem resolution unit can respond calmly and politely. The problem resolution unit solves problems. For example, the problem resolution unit analyzes the content of the customer's problem and proposes a solution. The problem resolution unit can also track the progress of solving the customer's problem in real time and provide additional support as needed. The problem resolution unit can also automatically suggest resources and tools needed to solve the customer's problem. For example, the problem resolution unit can provide a document containing a solution to a specific error message. The appointment setting unit sets appointments. For example, the appointment setting unit analyzes the content of the customer's request, checks the schedule, and suggests an available time slot. The appointment setting unit can also suggest an optimal time slot based on the customer's past appointment history. The appointment setting unit can also link with the customer's calendar to automatically avoid schedule conflicts. For example, the unit can suggest an optimal appointment time based on available time on the calendar. The FAQ response unit responds to FAQs. For example, the FAQ response unit analyzes the content of the customer's question and retrieves and provides an appropriate answer from an FAQ database. The FAQ response unit can also periodically update the FAQ database to provide the latest information. The FAQ response unit can also automatically provide additional information or links related to the content of the customer's question. For example, the FAQ response unit can provide a detailed explanation of a specific question or a link to a related webpage. This allows the AI ​​service desk according to the embodiment to streamline customer service and improve service quality for companies and organizations. For example, faster response to inquiries can improve customer satisfaction, and problem-solving support can earn customer trust.In addition, by automating appointment setting and FAQ responses, the burden on employees can be reduced and work efficiency can be improved.

[0030] The inquiry response unit can generate an individually customized response based on the customer's past inquiry history. For example, the inquiry response unit retrieves the customer's past inquiry history from a database and generates an individually customized response by referring to past responses to similar inquiries. For example, for a customer who has previously inquired about the same product, the unit provides the latest information about that product. The inquiry response unit also analyzes the customer's past inquiry history and provides more detailed information about topics that are frequently inquired about. For example, for a customer who frequently inquires about a particular service, the unit provides instructions on how to use that service and troubleshooting guides. The inquiry response unit also generates an response tailored to the customer's preferences and interests based on the customer's past inquiry history. For example, for a customer who frequently purchases products from a particular brand, the unit provides information on new products and special offers from that brand. In this way, customer satisfaction can be improved by generating individually customized responses based on the customer's past inquiry history.

[0031] The inquiry response unit can automatically provide additional information and suggestions related to the customer's inquiry. For example, the inquiry response unit analyzes the content of a customer's inquiry and automatically provides related additional information. For example, if a customer inquires about the stock status of a product, detailed information about the product and related products are suggested. The inquiry response unit also automatically generates related suggestions based on the content of the customer's inquiry. For example, if a customer inquires about a specific service, options and upgrades related to that service are suggested. The inquiry response unit also analyzes the content of a customer's inquiry and automatically provides related FAQs and support articles. For example, if a customer inquires about a specific problem, a support article containing a solution to that problem is provided. In this way, by providing additional information and suggestions related to the customer's inquiry, customer satisfaction can be improved.

[0032] The inquiry response unit can translate customer inquiries in real time, enabling multilingual support. The inquiry response unit, for example, translates customer inquiries in real time, enabling support in different languages. For example, a customer who inquires in English is provided with an answer in English. The inquiry response unit also automatically translates customer inquiries, enabling support in multiple languages. For example, inquiries in multiple languages ​​such as Spanish and French are supported. The inquiry response unit also builds a system that translates customer inquiries in real time and generates answers in different languages. For example, a customer who inquires in Japanese is provided with an answer in Japanese. In this way, by translating customer inquiries in real time and enabling multilingual support, global customer support becomes possible.

[0033] The inquiry response unit can visualize the content of customer inquiries and provide answers in a visually easy-to-understand format. The inquiry response unit, for example, visualizes the content of customer inquiries and provides answers in a visually easy-to-understand format. For example, it displays product inventory status in graphs or charts. The inquiry response unit also analyzes the content of customer inquiries and automatically generates related visual content. For example, it explains how to use a product using videos or images. The inquiry response unit also builds a system that visualizes the content of customer inquiries and provides answers in a visually easy-to-understand format. For example, it displays troubleshooting procedures in a flowchart. This makes it possible to visualize the content of customer inquiries and provide answers in a visually easy-to-understand format, thereby promoting customer understanding.

[0034] The problem resolution department can track the progress of resolving customer problems in real time and provide additional support as needed. For example, the problem resolution department builds a system that tracks the progress of resolving customer problems in real time and provides additional support as needed. For example, if the problem is not resolved, it provides additional troubleshooting steps. The problem resolution department also monitors the progress of resolving customer problems and escalates the problem to the support team as needed. For example, if the problem is not resolved, it requests specialized support staff to handle the problem. The problem resolution department also tracks the progress of resolving customer problems in real time and provides additional resources and tools as needed. For example, it provides software and documentation necessary for problem resolution. This makes it possible to improve the efficiency of problem resolution by tracking the progress of resolving customer problems in real time and providing additional support as needed.

[0035] The problem solving unit can automatically suggest resources and tools necessary for solving a customer's problem. For example, the problem solving unit analyzes the content of a customer's problem and automatically suggests the resources and tools necessary for solving the problem. For example, it provides a document containing a solution to a specific error message. The problem solving unit also builds a system that automatically suggests the resources and tools necessary for solving a customer's problem. For example, it suggests software or hardware that will help solve the problem. The problem solving unit also automatically suggests the resources and tools necessary for solving the problem based on the content of the customer's problem. For example, it provides a video tutorial containing a solution to a specific problem. In this way, the efficiency of problem solving can be improved by automatically suggesting the resources and tools necessary for solving a customer's problem.

[0036] The problem-solving department can analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, the problem-solving department analyzes data obtained in the process of resolving customer problems and builds a system that proposes preventive measures for future problems. For example, if a particular problem occurs frequently, the problem-solving department provides guidelines for preventing that problem. The problem-solving department also analyzes data obtained in the process of resolving customer problems and proposes preventive measures for future problems. For example, if a particular error message occurs frequently, the problem-solving department proposes a setting change to prevent that error. The problem-solving department also develops a system that proposes preventive measures for future problems based on data obtained in the process of resolving customer problems. For example, if a problem occurs due to a particular operation, the problem-solving department provides advice to avoid that operation. In this way, by analyzing data obtained in the process of resolving customer problems and proposing preventive measures for future problems, it is possible to prevent the problem from recurring.

[0037] The problem-solving department can share success stories of other customers to solve customer problems. For example, the problem-solving department builds a system for sharing success stories of other customers to solve customer problems. For example, it provides the experiences and solutions of customers who have solved the same problem. In addition, the problem-solving department automatically suggests success stories of other customers to solve customer problems. For example, it provides reviews and feedback from customers who have solved the same problem. In addition, the problem-solving department develops a platform for sharing success stories of other customers to solve customer problems. For example, it provides a community forum or discussion board that is useful for problem solving. In this way, the efficiency of solving customer problems can be improved by sharing success stories of other customers.

[0038] The problem solving department can provide video tutorials or interactive guides to solve customers' problems. For example, the problem solving department builds a system that provides video tutorials to solve customers' problems. For example, it provides a video that explains a solution to a specific problem. The problem solving department also provides interactive guides to solve customers' problems. For example, it provides an interactive guide that guides customers through the problem solving procedure step by step. The problem solving department also develops a system that automatically generates video tutorials or interactive guides to solve customers' problems. For example, it provides the problem solving procedure in a video or interactive format. In this way, by providing video tutorials or interactive guides, it is possible to improve the efficiency of solving customers' problems.

[0039] The appointment setting unit can suggest the optimal time slot based on the customer's past appointment history. The appointment setting unit, for example, analyzes the customer's past appointment history and builds a system that suggests the optimal time slot. For example, the suggestion is made based on time slots that have had many reservations in the past. The appointment setting unit also automatically suggests the optimal time slot based on the customer's past appointment history. For example, it prioritizes suggesting time slots that customers frequently make reservations for. The appointment setting unit also analyzes the customer's past appointment history and develops an algorithm that suggests the optimal time slot. For example, it predicts the optimal time slot based on past data. This makes it possible to improve customer convenience by suggesting the optimal time slot based on the customer's past appointment history.

[0040] The appointment setting unit can work in conjunction with the customer's calendar to automatically avoid schedule conflicts. The appointment setting unit, for example, works in conjunction with the customer's calendar to build a system that automatically avoids schedule conflicts. For example, it suggests the optimal appointment time based on the available time on the calendar. The appointment setting unit also works in conjunction with the customer's calendar to develop an algorithm that automatically avoids schedule conflicts. For example, it automatically selects a time slot that does not overlap with existing appointments. The appointment setting unit also works in conjunction with the customer's calendar to provide a function that automatically avoids schedule conflicts. For example, it checks the available time on the calendar in real time and suggests the optimal appointment time. This makes it possible to improve customer convenience by working in conjunction with the customer's calendar to automatically avoid schedule conflicts.

[0041] The appointment setting unit can automatically provide relevant materials and information when setting an appointment for a customer. The appointment setting unit, for example, builds a system that automatically provides relevant materials and information when setting an appointment for a customer. For example, it automatically sends necessary documents and materials before the interview. The appointment setting unit also automatically provides relevant information when setting an appointment for a customer. For example, it sends materials related to the purpose and agenda of the interview in advance. The appointment setting unit also develops a function that automatically provides relevant materials and information when setting an appointment for a customer. For example, it automatically collects and provides necessary information before the interview. This makes it possible to improve customer convenience by automatically providing relevant materials and information when setting an appointment for a customer.

[0042] The appointment setting unit can notify customers of their appointment settings across multiple platforms. For example, the appointment setting unit builds a system for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment confirmations via email, SMS, and a chat app. The appointment setting unit also develops a function for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment reminders via both email and SMS. The appointment setting unit also develops a system for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment details via a chat app. This allows customers to be notified of their appointment settings across multiple platforms, improving convenience for them.

[0043] The appointment setting unit can display a customer's appointment settings on a visual calendar, making it easier to manage them visually. The appointment setting unit, for example, builds a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, the appointment time slots are displayed in different colors on the calendar. The appointment setting unit also develops a function that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a calendar is provided that allows the appointment time to be adjusted by drag and drop. The appointment setting unit also develops a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, detailed appointment information is displayed as a pop-up on the calendar. This allows a customer's appointment settings to be displayed on a visual calendar, making it easier to manage them visually, thereby improving convenience for the customer.

[0044] The FAQ response unit can periodically update the FAQ database and provide the latest information. The FAQ response unit, for example, builds a system that periodically updates the FAQ database and provides the latest information. For example, it periodically adds information about new products and services. The FAQ response unit also develops an algorithm that automatically updates the FAQ database. For example, it updates the FAQ based on customer feedback and inquiries. The FAQ response unit also develops a function that periodically updates the FAQ database and provides the latest information. For example, it periodically reviews the contents of the FAQ, deletes old information, and adds new information. In this way, the FAQ database can be periodically updated and the latest information can be provided, thereby improving customer convenience.

[0045] The FAQ response unit can automatically provide additional information and links related to the content of a customer's question. For example, the FAQ response unit builds a system that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it provides detailed explanations for specific questions or links to related web pages. The FAQ response unit also develops an algorithm that automatically generates related additional information and links based on the content of a customer's question. For example, it provides links to support articles and videos related to FAQ answers. The FAQ response unit also develops a function that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it provides detailed explanations for specific questions or links to related resources. This makes it possible to improve customer convenience by providing additional information and links related to the content of a customer's question.

[0046] The FAQ response unit can analyze the content of customer questions and suggest improvements to the FAQ database. The FAQ response unit, for example, builds a system that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it reviews and improves the content of the FAQ based on frequently asked questions. The FAQ response unit also develops an algorithm that analyzes the content of customer questions and automatically suggests improvements to the FAQ database. For example, it suggests improvements when the answer to a particular question is insufficient. The FAQ response unit also develops a function that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it reviews and improves the content of the FAQ based on customer feedback. In this way, the quality of the FAQ can be improved by analyzing the content of customer questions and suggesting improvements to the FAQ database.

[0047] The FAQ response unit can provide FAQ responses in a multimedia format such as video or infographics. The FAQ response unit, for example, builds a system that provides FAQ responses in video format. For example, explanations for specific questions are provided in video to make them visually easier to understand. The FAQ response unit also provides FAQ responses in infographic format. For example, complex information is visually organized and provided in an easy-to-understand format. The FAQ response unit also develops a function that provides FAQ responses in multimedia format. For example, a combination of video, infographics, audio guides, etc. is provided. In this way, providing FAQ responses in multimedia formats such as video or infographics can promote customer understanding.

[0048] The FAQ response unit can automatically translate FAQ responses into different languages, thereby achieving multilingual support. The FAQ response unit, for example, builds a system that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it provides FAQs in multiple languages, such as English, Spanish, and French. The FAQ response unit also develops an algorithm that automatically translates FAQ responses. For example, it provides FAQs that are automatically translated according to the customer's language settings. The FAQ response unit also develops a function that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it provides FAQs that are translated in real time. This allows FAQ responses to be automatically translated into different languages, achieving multilingual support, and making it possible to respond to customers globally.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The inquiry response unit can analyze the content of a customer's inquiry and automatically provide related additional information and suggestions. For example, if a customer inquires about the availability of a product, detailed information about that product and related products will be suggested. The inquiry response unit can also automatically generate related suggestions based on the content of the customer's inquiry. For example, if a customer inquires about a specific service, options and upgrades related to that service will be suggested. The inquiry response unit can also analyze the content of a customer's inquiry and automatically provide related FAQs and support articles. For example, if a customer inquires about a specific problem, a support article containing a solution to that problem will be provided. In this way, customer satisfaction can be improved by providing additional information and suggestions related to the content of the customer's inquiry.

[0051] The inquiry response unit can translate customer inquiries in real time, enabling multilingual support. For example, it can translate customer inquiries in real time, enabling support in different languages. For example, a customer who inquires in English can receive a response in English. The inquiry response unit can also automatically translate customer inquiries, enabling support in multiple languages. For example, it can handle inquiries in multiple languages, such as Spanish and French. The inquiry response unit can also build a system that translates customer inquiries in real time and generates responses in different languages. For example, a customer who inquires in Japanese can receive a response in Japanese. This allows for real-time translation of customer inquiries and enables multilingual support, enabling global customer support.

[0052] The inquiry response unit can visualize the content of customer inquiries and provide answers in a visually easy-to-understand format. For example, the content of customer inquiries can be visualized and answers can be provided in a visually easy-to-understand format. For example, product inventory status can be displayed in graphs or charts. The inquiry response unit can also analyze customer inquiries and automatically generate related visual content. For example, it can explain how to use a product using videos or images. The inquiry response unit can also visualize customer inquiries and build a system that provides answers in a visually easy-to-understand format. For example, it can display troubleshooting steps in a flowchart. This can help customers understand by visualizing the content of customer inquiries and providing answers in a visually easy-to-understand format.

[0053] The problem resolution department can track the progress of resolving customer problems in real time and provide additional support as needed. For example, a system can be built to track the progress of resolving customer problems in real time and provide additional support as needed. For example, if the problem is not resolved, additional troubleshooting steps can be provided. The problem resolution department can also monitor the progress of resolving customer problems and escalate the issue to the support team as needed. For example, if the problem is not resolved, specialized support staff can be called in to handle the issue. The problem resolution department can also track the progress of resolving customer problems in real time and provide additional resources or tools as needed. For example, they can provide the software or documentation necessary to resolve the problem. This makes it possible to improve the efficiency of problem resolution by tracking the progress of resolving customer problems in real time and providing additional support as needed.

[0054] The problem-solving unit can automatically suggest resources and tools necessary for solving a customer's problem. For example, it analyzes the content of the customer's problem and automatically suggests the resources and tools necessary for solving the problem. For example, it provides a document containing a solution to a specific error message. The problem-solving unit also builds a system that automatically suggests resources and tools necessary for solving a customer's problem. For example, it suggests software or hardware that will help solve the problem. The problem-solving unit also automatically suggests resources and tools necessary for solving the problem based on the content of the customer's problem. For example, it provides a video tutorial containing a solution to a specific problem. In this way, the efficiency of problem solving can be improved by automatically suggesting the resources and tools necessary for solving a customer's problem.

[0055] The problem-solving department can analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, a system can be built that analyzes data obtained in the process of resolving customer problems and proposes preventive measures for future problems. For example, if a particular problem occurs frequently, guidelines can be provided to prevent that problem. The problem-solving department can also analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, if a particular error message occurs frequently, a setting change can be proposed to prevent that error. The problem-solving department can also develop a system that proposes preventive measures for future problems based on data obtained in the process of resolving customer problems. For example, if a problem occurs due to a particular operation, advice can be provided to avoid that operation. In this way, by analyzing data obtained in the process of resolving customer problems and proposing preventive measures for future problems, it is possible to prevent the problem from recurring.

[0056] The problem-solving department can share success stories of other customers to solve customer problems. For example, a system for sharing success stories of other customers to solve customer problems can be built. For example, the system can provide the experiences and solutions of customers who have solved the same problem. The problem-solving department can also automatically suggest success stories of other customers to solve customer problems. For example, the system can provide reviews and feedback from customers who have solved the same problem. The problem-solving department can also develop a platform for sharing success stories of other customers to solve customer problems. For example, the system can provide a community forum or discussion board that is useful for problem solving. This can improve the efficiency of solving customer problems by sharing success stories of other customers.

[0057] The problem-solving department can provide video tutorials or interactive guides to solve customer problems. For example, a system is built to provide video tutorials to solve customer problems. For example, a video explaining a solution to a specific problem is provided. The problem-solving department also provides interactive guides to solve customer problems. For example, an interactive guide that guides customers through the problem-solving procedure step by step is provided. The problem-solving department also develops a system that automatically generates video tutorials or interactive guides to solve customer problems. For example, the problem-solving procedure is provided in a video or interactive format. In this way, by providing video tutorials or interactive guides, the efficiency of solving customer problems can be improved.

[0058] The appointment setting unit can suggest the optimal time slot based on the customer's past appointment history. For example, a system can be constructed that analyzes the customer's past appointment history and suggests the optimal time slot. For example, suggestions can be made based on time slots that have had the most reservations in the past. The appointment setting unit can also automatically suggest the optimal time slot based on the customer's past appointment history. For example, it can prioritize suggestions for time slots that customers frequently make reservations for. The appointment setting unit can also analyze the customer's past appointment history and develop an algorithm that suggests the optimal time slot. For example, it can predict the optimal time slot based on past data. This can improve customer convenience by suggesting the optimal time slot based on the customer's past appointment history.

[0059] The appointment setting unit can work in conjunction with the customer's calendar to automatically avoid schedule conflicts. For example, a system can be built that works in conjunction with the customer's calendar to automatically avoid schedule conflicts. For example, the system can suggest the optimal appointment time based on the available time on the calendar. The appointment setting unit can also work in conjunction with the customer's calendar to develop an algorithm that automatically avoids schedule conflicts. For example, the system can automatically select a time slot that does not overlap with existing appointments. The appointment setting unit can also work in conjunction with the customer's calendar to provide a function that automatically avoids schedule conflicts. For example, the system can check the available time on the calendar in real time and suggest the optimal appointment time. This makes it possible to improve customer convenience by working in conjunction with the customer's calendar to automatically avoid schedule conflicts.

[0060] The appointment setting unit can automatically provide relevant materials and information when setting an appointment for a customer. For example, a system is constructed that automatically provides relevant materials and information when setting an appointment for a customer. For example, necessary documents and materials are automatically sent before the interview. The appointment setting unit also automatically provides relevant information when setting an appointment for a customer. For example, materials related to the purpose and agenda of the interview are sent in advance. The appointment setting unit also develops a function that automatically provides relevant materials and information when setting an appointment for a customer. For example, necessary information is automatically collected and provided before the interview. This makes it possible to improve customer convenience by automatically providing relevant materials and information when setting an appointment for a customer.

[0061] The appointment setting unit can notify customers of their appointment settings across multiple platforms. For example, a system is built to notify customers of their appointment settings across multiple platforms. For example, appointment confirmations are sent via email, SMS, and a chat app. The appointment setting unit also develops a function to notify customers of their appointment settings across multiple platforms. For example, appointment reminders are sent via both email and SMS. The appointment setting unit also develops a system to notify customers of their appointment settings across multiple platforms. For example, appointment details are sent via a chat app. This allows customers to be notified of their appointment settings across multiple platforms, improving customer convenience.

[0062] The appointment setting unit can display a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a system is constructed that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, appointment time slots are displayed in different colors on a calendar. The appointment setting unit also develops a function that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a calendar is provided that allows the appointment time to be adjusted by drag and drop. The appointment setting unit also develops a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, detailed appointment information is displayed as a pop-up on the calendar. This allows a customer's appointment settings to be displayed on a visual calendar, making it easier to manage them visually, thereby improving convenience for the customer.

[0063] The FAQ response unit can periodically update the FAQ database and provide the latest information. For example, a system can be built that periodically updates the FAQ database and provides the latest information. For example, information about new products and services can be periodically added. The FAQ response unit can also develop an algorithm that automatically updates the FAQ database. For example, it can update the FAQ based on customer feedback and inquiries. The FAQ response unit can also develop a function that periodically updates the FAQ database and provides the latest information. For example, it can periodically review the contents of the FAQ, delete old information, and add new information. In this way, the FAQ database can be periodically updated to provide the latest information, thereby improving customer convenience.

[0064] The FAQ response unit can automatically provide additional information and links related to the content of a customer's question. For example, a system can be built that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it can provide detailed explanations for specific questions or links to related web pages. The FAQ response unit can also develop an algorithm that automatically generates related additional information and links based on the content of a customer's question. For example, it can provide links to support articles and videos related to FAQ answers. The FAQ response unit can also develop a function that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it can provide detailed explanations for specific questions or links to related resources. This can improve customer convenience by providing additional information and links related to the content of a customer's question.

[0065] The FAQ response unit can analyze the content of customer questions and suggest improvements to the FAQ database. For example, a system can be built that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, the content of the FAQ can be reviewed and improved based on frequently asked questions. The FAQ response unit can also develop an algorithm that analyzes the content of customer questions and automatically suggests improvements to the FAQ database. For example, it can suggest improvements when the answer to a particular question is insufficient. The FAQ response unit can also develop a function that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it can review and improve the content of the FAQ based on customer feedback. In this way, the quality of the FAQ can be improved by analyzing the content of customer questions and suggesting improvements to the FAQ database.

[0066] The FAQ response unit can provide FAQ responses in a multimedia format, such as video or infographics. For example, a system can be built that provides FAQ responses in video format. For example, explanations for specific questions can be provided in video to make them visually easier to understand. The FAQ response unit can also provide FAQ responses in infographic format. For example, complex information can be visually organized and provided in an easy-to-understand format. The FAQ response unit can also develop a function that provides FAQ responses in multimedia format. For example, a combination of video, infographics, audio guides, etc. can be provided. In this way, providing FAQ responses in multimedia formats, such as video or infographics, can promote customer understanding.

[0067] The FAQ response unit can automatically translate FAQ responses into different languages, achieving multilingual support. For example, a system can be built that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, FAQs can be provided in multiple languages, such as English, Spanish, and French. The FAQ response unit can also develop an algorithm that automatically translates FAQ responses. For example, it can provide FAQs that are automatically translated according to the customer's language settings. The FAQ response unit can also develop a function that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it can provide FAQs that are translated in real time. This allows FAQ responses to be automatically translated into different languages, achieving multilingual support, making it possible to respond to customers globally.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The inquiry response unit responds to inquiries. For example, the inquiry response unit analyzes the content of the customer's inquiry and generates an appropriate response. The inquiry response unit can also generate individually customized responses based on the customer's past inquiry history. Furthermore, the inquiry response unit can analyze the customer's tone of voice and choice of words and use emotion estimation functionality to respond appropriately. For example, if the customer is angry, the inquiry response unit will respond calmly and politely. Step 2: The problem solver solves the problem. For example, the problem solver analyzes the customer's problem and proposes a solution. The problem solver can also track the progress of resolving the customer's problem in real time and provide additional support if needed. Furthermore, the problem solver can automatically suggest resources and tools needed to solve the customer's problem. For example, the problem solver can provide a document containing a solution for a specific error message. Step 3: The appointment setting unit sets the appointment. For example, the appointment setting unit analyzes the customer's request, checks the customer's schedule, and suggests an available time slot. The appointment setting unit can also suggest the optimal time slot based on the customer's past appointment history. Furthermore, the appointment setting unit can link with the customer's calendar and automatically avoid schedule conflicts. For example, it can suggest the optimal appointment time based on the available time slots on the calendar. Step 4: The FAQ response unit responds to the FAQ. For example, the FAQ response unit analyzes the customer's question and retrieves and provides an appropriate answer from the FAQ database. The FAQ response unit can also periodically update the FAQ database to provide the latest information. Furthermore, the FAQ response unit can automatically provide additional information or links related to the customer's question. For example, it can provide a detailed explanation of a specific question or a link to a related web page.

[0070] (Example 2) The AI ​​service desk according to the embodiment of the present invention is a system that uses conversational AI technology to handle inquiries, solve problems, set appointments, and answer FAQs. As a result, the AI ​​service desk can improve the efficiency of customer support and service quality for companies and organizations.

[0071] An AI service desk according to an embodiment includes an inquiry response unit, a problem resolution unit, an appointment setting unit, and an FAQ response unit. The inquiry response unit responds to inquiries. For example, the inquiry response unit analyzes the content of a customer's inquiry and generates an appropriate response. The inquiry response unit can also generate individually customized responses based on the customer's past inquiry history. The inquiry response unit can also analyze the customer's tone of voice and language and provide an appropriate response using an emotion estimation function. For example, if the customer is angry, the problem resolution unit can respond calmly and politely. The problem resolution unit solves problems. For example, the problem resolution unit analyzes the content of the customer's problem and proposes a solution. The problem resolution unit can also track the progress of solving the customer's problem in real time and provide additional support as needed. The problem resolution unit can also automatically suggest resources and tools needed to solve the customer's problem. For example, the problem resolution unit can provide a document containing a solution to a specific error message. The appointment setting unit sets appointments. For example, the appointment setting unit analyzes the content of the customer's request, checks the schedule, and suggests an available time slot. The appointment setting unit can also suggest an optimal time slot based on the customer's past appointment history. The appointment setting unit can also link with the customer's calendar to automatically avoid schedule conflicts. For example, the unit can suggest an optimal appointment time based on available time on the calendar. The FAQ response unit responds to FAQs. For example, the FAQ response unit analyzes the content of the customer's question and retrieves and provides an appropriate answer from an FAQ database. The FAQ response unit can also periodically update the FAQ database to provide the latest information. The FAQ response unit can also automatically provide additional information or links related to the content of the customer's question. For example, the FAQ response unit can provide a detailed explanation of a specific question or a link to a related webpage. This allows the AI ​​service desk according to the embodiment to streamline customer service and improve service quality for companies and organizations. For example, faster response to inquiries can improve customer satisfaction, and problem-solving support can earn customer trust.In addition, by automating appointment setting and FAQ responses, the burden on employees can be reduced and work efficiency can be improved.

[0072] The inquiry response unit can generate an individually customized response based on the customer's past inquiry history. For example, the inquiry response unit retrieves the customer's past inquiry history from a database and generates an individually customized response by referring to past responses to similar inquiries. For example, for a customer who has previously inquired about the same product, the unit provides the latest information about that product. The inquiry response unit also analyzes the customer's past inquiry history and provides more detailed information about topics that are frequently inquired about. For example, for a customer who frequently inquires about a particular service, the unit provides instructions on how to use that service and troubleshooting guides. The inquiry response unit also generates an response tailored to the customer's preferences and interests based on the customer's past inquiry history. For example, for a customer who frequently purchases products from a particular brand, the unit provides information on new products and special offers from that brand. In this way, customer satisfaction can be improved by generating individually customized responses based on the customer's past inquiry history.

[0073] The inquiry handling unit can analyze the customer's voice tone and language and use the emotion estimation function to provide an appropriate response. The inquiry handling unit, for example, analyzes the customer's voice tone in real time and uses the emotion estimation function to estimate the customer's emotional state. For example, if the customer is angry, the unit responds calmly and politely. The inquiry handling unit also analyzes the customer's language and uses the emotion estimation function to estimate the customer's emotions. For example, if the customer is feeling anxious, the unit provides an answer that gives the customer a sense of security. The inquiry handling unit also analyzes the customer's voice tone and language in combination and uses the emotion estimation function to more accurately estimate the customer's emotions. For example, if the customer is happy, the unit responds in a way that further elicits positive emotions. In this way, customer satisfaction can be improved by providing an appropriate response according to the customer's emotions.

[0074] The inquiry response unit can automatically provide additional information and suggestions related to the customer's inquiry. For example, the inquiry response unit analyzes the content of a customer's inquiry and automatically provides related additional information. For example, if a customer inquires about the stock status of a product, detailed information about the product and related products are suggested. The inquiry response unit also automatically generates related suggestions based on the content of the customer's inquiry. For example, if a customer inquires about a specific service, options and upgrades related to that service are suggested. The inquiry response unit also analyzes the content of a customer's inquiry and automatically provides related FAQs and support articles. For example, if a customer inquires about a specific problem, a support article containing a solution to that problem is provided. In this way, by providing additional information and suggestions related to the customer's inquiry, customer satisfaction can be improved.

[0075] The inquiry response unit can translate customer inquiries in real time, enabling multilingual support. The inquiry response unit, for example, translates customer inquiries in real time, enabling support in different languages. For example, a customer who inquires in English is provided with an answer in English. The inquiry response unit also automatically translates customer inquiries, enabling support in multiple languages. For example, inquiries in multiple languages ​​such as Spanish and French are supported. The inquiry response unit also builds a system that translates customer inquiries in real time and generates answers in different languages. For example, a customer who inquires in Japanese is provided with an answer in Japanese. In this way, by translating customer inquiries in real time and enabling multilingual support, global customer support becomes possible.

[0076] The inquiry response unit can visualize the content of customer inquiries and provide answers in a visually easy-to-understand format. The inquiry response unit, for example, visualizes the content of customer inquiries and provides answers in a visually easy-to-understand format. For example, it displays product inventory status in graphs or charts. The inquiry response unit also analyzes the content of customer inquiries and automatically generates related visual content. For example, it explains how to use a product using videos or images. The inquiry response unit also builds a system that visualizes the content of customer inquiries and provides answers in a visually easy-to-understand format. For example, it displays troubleshooting procedures in a flowchart. This makes it possible to visualize the content of customer inquiries and provide answers in a visually easy-to-understand format, thereby promoting customer understanding.

[0077] The inquiry response unit uses the emotion estimation function to respond according to the customer's emotions and draw out positive emotions. The inquiry response unit, for example, uses the emotion estimation function to respond according to the customer's emotions. For example, if the customer is dissatisfied, the inquiry response unit responds quickly and courteously to draw out positive emotions. The inquiry response unit also builds a system that analyzes the customer's emotions in real time and takes appropriate action. For example, if the customer is happy, the inquiry response unit takes action to draw out even more joy. The inquiry response unit also uses the emotion estimation function to develop a system that responds according to the customer's emotions and draws out positive emotions. For example, if the customer is feeling anxious, the inquiry response unit takes action to give the customer a sense of security. In this way, customer satisfaction can be improved by responding according to the customer's emotions and drawing out positive emotions.

[0078] The problem resolution department can track the progress of resolving customer problems in real time and provide additional support as needed. For example, the problem resolution department builds a system that tracks the progress of resolving customer problems in real time and provides additional support as needed. For example, if the problem is not resolved, it provides additional troubleshooting steps. The problem resolution department also monitors the progress of resolving customer problems and escalates the problem to the support team as needed. For example, if the problem is not resolved, it requests specialized support staff to handle the problem. The problem resolution department also tracks the progress of resolving customer problems in real time and provides additional resources and tools as needed. For example, it provides software and documentation necessary for problem resolution. This makes it possible to improve the efficiency of problem resolution by tracking the progress of resolving customer problems in real time and providing additional support as needed.

[0079] The problem solving unit can automatically suggest resources and tools necessary for solving a customer's problem. For example, the problem solving unit analyzes the content of a customer's problem and automatically suggests the resources and tools necessary for solving the problem. For example, it provides a document containing a solution to a specific error message. The problem solving unit also builds a system that automatically suggests the resources and tools necessary for solving a customer's problem. For example, it suggests software or hardware that will help solve the problem. The problem solving unit also automatically suggests the resources and tools necessary for solving the problem based on the content of the customer's problem. For example, it provides a video tutorial containing a solution to a specific problem. In this way, the efficiency of problem solving can be improved by automatically suggesting the resources and tools necessary for solving a customer's problem.

[0080] The problem-solving department can analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, the problem-solving department analyzes data obtained in the process of resolving customer problems and builds a system that proposes preventive measures for future problems. For example, if a particular problem occurs frequently, the problem-solving department provides guidelines for preventing that problem. The problem-solving department also analyzes data obtained in the process of resolving customer problems and proposes preventive measures for future problems. For example, if a particular error message occurs frequently, the problem-solving department proposes a setting change to prevent that error. The problem-solving department also develops a system that proposes preventive measures for future problems based on data obtained in the process of resolving customer problems. For example, if a problem occurs due to a particular operation, the problem-solving department provides advice to avoid that operation. In this way, by analyzing data obtained in the process of resolving customer problems and proposing preventive measures for future problems, it is possible to prevent the problem from recurring.

[0081] The problem-solving department can share success stories of other customers to solve customer problems. For example, the problem-solving department builds a system for sharing success stories of other customers to solve customer problems. For example, it provides the experiences and solutions of customers who have solved the same problem. In addition, the problem-solving department automatically suggests success stories of other customers to solve customer problems. For example, it provides reviews and feedback from customers who have solved the same problem. In addition, the problem-solving department develops a platform for sharing success stories of other customers to solve customer problems. For example, it provides a community forum or discussion board that is useful for problem solving. In this way, the efficiency of solving customer problems can be improved by sharing success stories of other customers.

[0082] The problem solving department can provide video tutorials or interactive guides to solve customers' problems. For example, the problem solving department builds a system that provides video tutorials to solve customers' problems. For example, it provides a video that explains a solution to a specific problem. The problem solving department also provides interactive guides to solve customers' problems. For example, it provides an interactive guide that guides customers through the problem solving procedure step by step. The problem solving department also develops a system that automatically generates video tutorials or interactive guides to solve customers' problems. For example, it provides the problem solving procedure in a video or interactive format. In this way, by providing video tutorials or interactive guides, it is possible to improve the efficiency of solving customers' problems.

[0083] The problem-solving unit can use the emotion estimation function to take measures to reduce the customer's stress level. For example, the problem-solving unit uses the emotion estimation function to analyze the customer's stress level in real time and take appropriate measures. For example, if the customer is feeling stressed, the problem-solving unit takes measures to help the customer relax. The problem-solving unit also builds a system that takes measures to reduce the customer's stress level. For example, kind words and encouraging messages are provided to a customer who is feeling stressed. The problem-solving unit also uses the emotion estimation function to take measures to reduce the customer's stress level. For example, if the customer is feeling stressed, a simplified problem-solving procedure is provided. In this way, customer satisfaction can be improved by taking measures to reduce the customer's stress level.

[0084] The appointment setting unit can suggest the optimal time slot based on the customer's past appointment history. The appointment setting unit, for example, analyzes the customer's past appointment history and builds a system that suggests the optimal time slot. For example, the suggestion is made based on time slots that have had many reservations in the past. The appointment setting unit also automatically suggests the optimal time slot based on the customer's past appointment history. For example, it prioritizes suggesting time slots that customers frequently make reservations for. The appointment setting unit also analyzes the customer's past appointment history and develops an algorithm that suggests the optimal time slot. For example, it predicts the optimal time slot based on past data. This makes it possible to improve customer convenience by suggesting the optimal time slot based on the customer's past appointment history.

[0085] The appointment setting unit can work in conjunction with the customer's calendar to automatically avoid schedule conflicts. The appointment setting unit, for example, works in conjunction with the customer's calendar to build a system that automatically avoids schedule conflicts. For example, it suggests the optimal appointment time based on the available time on the calendar. The appointment setting unit also works in conjunction with the customer's calendar to develop an algorithm that automatically avoids schedule conflicts. For example, it automatically selects a time slot that does not overlap with existing appointments. The appointment setting unit also works in conjunction with the customer's calendar to provide a function that automatically avoids schedule conflicts. For example, it checks the available time on the calendar in real time and suggests the optimal appointment time. This makes it possible to improve customer convenience by working in conjunction with the customer's calendar to automatically avoid schedule conflicts.

[0086] The appointment setting unit can automatically provide relevant materials and information when setting an appointment for a customer. The appointment setting unit, for example, builds a system that automatically provides relevant materials and information when setting an appointment for a customer. For example, it automatically sends necessary documents and materials before the interview. The appointment setting unit also automatically provides relevant information when setting an appointment for a customer. For example, it sends materials related to the purpose and agenda of the interview in advance. The appointment setting unit also develops a function that automatically provides relevant materials and information when setting an appointment for a customer. For example, it automatically collects and provides necessary information before the interview. This makes it possible to improve customer convenience by automatically providing relevant materials and information when setting an appointment for a customer.

[0087] The appointment setting unit can notify customers of their appointment settings across multiple platforms. For example, the appointment setting unit builds a system for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment confirmations via email, SMS, and a chat app. The appointment setting unit also develops a function for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment reminders via both email and SMS. The appointment setting unit also develops a system for notifying customers of their appointment settings across multiple platforms. For example, it sends appointment details via a chat app. This allows customers to be notified of their appointment settings across multiple platforms, improving convenience for them.

[0088] The appointment setting unit can display a customer's appointment settings on a visual calendar, making it easier to manage them visually. The appointment setting unit, for example, builds a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, the appointment time slots are displayed in different colors on the calendar. The appointment setting unit also develops a function that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a calendar is provided that allows the appointment time to be adjusted by drag and drop. The appointment setting unit also develops a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, detailed appointment information is displayed as a pop-up on the calendar. This allows a customer's appointment settings to be displayed on a visual calendar, making it easier to manage them visually, thereby improving convenience for the customer.

[0089] The appointment setting unit can use the emotion estimation function to estimate the customer's desired time slot and propose the optimal appointment. The appointment setting unit, for example, uses the emotion estimation function to build a system that estimates the customer's desired time slot and proposes the optimal appointment. For example, the optimal time slot is proposed based on the customer's past behavioral patterns and emotion data. The appointment setting unit also analyzes the customer's emotion data and develops an algorithm that estimates the customer's desired time slot. For example, it prioritizes proposing a time slot when the customer is relaxed. The appointment setting unit also uses the emotion estimation function to develop a function that estimates the customer's desired time slot and proposes the optimal appointment. For example, it analyzes the customer's emotional state in real time and proposes the optimal time slot. This makes it possible to estimate the customer's desired time slot and propose the optimal appointment, thereby improving customer convenience.

[0090] The FAQ response unit can periodically update the FAQ database and provide the latest information. The FAQ response unit, for example, builds a system that periodically updates the FAQ database and provides the latest information. For example, it periodically adds information about new products and services. The FAQ response unit also develops an algorithm that automatically updates the FAQ database. For example, it updates the FAQ based on customer feedback and inquiries. The FAQ response unit also develops a function that periodically updates the FAQ database and provides the latest information. For example, it periodically reviews the contents of the FAQ, deletes old information, and adds new information. In this way, the FAQ database can be periodically updated and the latest information can be provided, thereby improving customer convenience.

[0091] The FAQ response unit can automatically provide additional information and links related to the content of a customer's question. For example, the FAQ response unit builds a system that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it provides detailed explanations for specific questions or links to related web pages. The FAQ response unit also develops an algorithm that automatically generates related additional information and links based on the content of a customer's question. For example, it provides links to support articles and videos related to FAQ answers. The FAQ response unit also develops a function that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it provides detailed explanations for specific questions or links to related resources. This makes it possible to improve customer convenience by providing additional information and links related to the content of a customer's question.

[0092] The FAQ response unit can analyze the content of customer questions and suggest improvements to the FAQ database. The FAQ response unit, for example, builds a system that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it reviews and improves the content of the FAQ based on frequently asked questions. The FAQ response unit also develops an algorithm that analyzes the content of customer questions and automatically suggests improvements to the FAQ database. For example, it suggests improvements when the answer to a particular question is insufficient. The FAQ response unit also develops a function that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it reviews and improves the content of the FAQ based on customer feedback. In this way, the quality of the FAQ can be improved by analyzing the content of customer questions and suggesting improvements to the FAQ database.

[0093] The FAQ response unit can provide FAQ responses in a multimedia format such as video or infographics. The FAQ response unit, for example, builds a system that provides FAQ responses in video format. For example, explanations for specific questions are provided in video to make them visually easier to understand. The FAQ response unit also provides FAQ responses in infographic format. For example, complex information is visually organized and provided in an easy-to-understand format. The FAQ response unit also develops a function that provides FAQ responses in multimedia format. For example, a combination of video, infographics, audio guides, etc. is provided. In this way, providing FAQ responses in multimedia formats such as video or infographics can promote customer understanding.

[0094] The FAQ response unit can automatically translate FAQ responses into different languages, thereby achieving multilingual support. The FAQ response unit, for example, builds a system that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it provides FAQs in multiple languages, such as English, Spanish, and French. The FAQ response unit also develops an algorithm that automatically translates FAQ responses. For example, it provides FAQs that are automatically translated according to the customer's language settings. The FAQ response unit also develops a function that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it provides FAQs that are translated in real time. This allows FAQ responses to be automatically translated into different languages, achieving multilingual support, and making it possible to respond to customers globally.

[0095] The FAQ response unit can use the emotion estimation function to evaluate customer satisfaction with questions and identify areas for improvement. The FAQ response unit, for example, uses the emotion estimation function to build a system that evaluates customer satisfaction with questions in real time. For example, it analyzes customers' facial expressions and voices and scores their satisfaction. The FAQ response unit also develops an algorithm that evaluates customer satisfaction with questions and identifies areas for improvement. For example, it suggests areas for improvement for questions with low satisfaction. The FAQ response unit also uses the emotion estimation function to develop a function that evaluates customer satisfaction with questions and identifies areas for improvement. For example, it reviews and improves the content of FAQs for questions with low satisfaction. In this way, it is possible to evaluate customer satisfaction with questions and identify areas for improvement, thereby improving the quality of FAQs.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The inquiry response unit can analyze the content of a customer's inquiry and automatically provide related additional information and suggestions. For example, if a customer inquires about the availability of a product, detailed information about that product and related products will be suggested. The inquiry response unit can also automatically generate related suggestions based on the content of the customer's inquiry. For example, if a customer inquires about a specific service, options and upgrades related to that service will be suggested. The inquiry response unit can also analyze the content of a customer's inquiry and automatically provide related FAQs and support articles. For example, if a customer inquires about a specific problem, a support article containing a solution to that problem will be provided. In this way, customer satisfaction can be improved by providing additional information and suggestions related to the content of the customer's inquiry.

[0098] The inquiry response unit can translate customer inquiries in real time, enabling multilingual support. For example, it can translate customer inquiries in real time, enabling support in different languages. For example, a customer who inquires in English can receive a response in English. The inquiry response unit can also automatically translate customer inquiries, enabling support in multiple languages. For example, it can handle inquiries in multiple languages, such as Spanish and French. The inquiry response unit can also build a system that translates customer inquiries in real time and generates responses in different languages. For example, a customer who inquires in Japanese can receive a response in Japanese. This allows for real-time translation of customer inquiries and enables multilingual support, enabling global customer support.

[0099] The inquiry response unit can visualize the content of customer inquiries and provide answers in a visually easy-to-understand format. For example, the content of customer inquiries can be visualized and answers can be provided in a visually easy-to-understand format. For example, product inventory status can be displayed in graphs or charts. The inquiry response unit can also analyze customer inquiries and automatically generate related visual content. For example, it can explain how to use a product using videos or images. The inquiry response unit can also visualize customer inquiries and build a system that provides answers in a visually easy-to-understand format. For example, it can display troubleshooting steps in a flowchart. This can help customers understand by visualizing the content of customer inquiries and providing answers in a visually easy-to-understand format.

[0100] The inquiry response unit uses the emotion estimation function to respond according to the customer's emotions and draw out positive emotions. For example, the emotion estimation function is used to respond according to the customer's emotions. For example, if the customer is dissatisfied, a quick and courteous response is provided to draw out positive emotions. The inquiry response unit also builds a system that analyzes the customer's emotions in real time and takes appropriate action. For example, if the customer is happy, a response is provided to draw out even more joy. The inquiry response unit also uses the emotion estimation function to develop a system that responds according to the customer's emotions and draws out positive emotions. For example, if the customer is feeling anxious, a response is provided that gives the customer a sense of security. In this way, customer satisfaction can be improved by responding according to the customer's emotions and drawing out positive emotions.

[0101] The problem resolution department can track the progress of resolving customer problems in real time and provide additional support as needed. For example, a system can be built to track the progress of resolving customer problems in real time and provide additional support as needed. For example, if the problem is not resolved, additional troubleshooting steps can be provided. The problem resolution department can also monitor the progress of resolving customer problems and escalate the issue to the support team as needed. For example, if the problem is not resolved, specialized support staff can be called in to handle the issue. The problem resolution department can also track the progress of resolving customer problems in real time and provide additional resources or tools as needed. For example, they can provide the software or documentation necessary to resolve the problem. This makes it possible to improve the efficiency of problem resolution by tracking the progress of resolving customer problems in real time and providing additional support as needed.

[0102] The problem-solving unit can automatically suggest resources and tools necessary for solving a customer's problem. For example, it analyzes the content of the customer's problem and automatically suggests the resources and tools necessary for solving the problem. For example, it provides a document containing a solution to a specific error message. The problem-solving unit also builds a system that automatically suggests resources and tools necessary for solving a customer's problem. For example, it suggests software or hardware that will help solve the problem. The problem-solving unit also automatically suggests resources and tools necessary for solving the problem based on the content of the customer's problem. For example, it provides a video tutorial containing a solution to a specific problem. In this way, the efficiency of problem solving can be improved by automatically suggesting the resources and tools necessary for solving a customer's problem.

[0103] The problem-solving department can analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, a system can be built that analyzes data obtained in the process of resolving customer problems and proposes preventive measures for future problems. For example, if a particular problem occurs frequently, guidelines can be provided to prevent that problem. The problem-solving department can also analyze data obtained in the process of resolving customer problems and propose preventive measures for future problems. For example, if a particular error message occurs frequently, a setting change can be proposed to prevent that error. The problem-solving department can also develop a system that proposes preventive measures for future problems based on data obtained in the process of resolving customer problems. For example, if a problem occurs due to a particular operation, advice can be provided to avoid that operation. In this way, by analyzing data obtained in the process of resolving customer problems and proposing preventive measures for future problems, it is possible to prevent the problem from recurring.

[0104] The problem-solving department can share success stories of other customers to solve customer problems. For example, a system for sharing success stories of other customers to solve customer problems can be built. For example, the system can provide the experiences and solutions of customers who have solved the same problem. The problem-solving department can also automatically suggest success stories of other customers to solve customer problems. For example, the system can provide reviews and feedback from customers who have solved the same problem. The problem-solving department can also develop a platform for sharing success stories of other customers to solve customer problems. For example, the system can provide a community forum or discussion board that is useful for problem solving. This can improve the efficiency of solving customer problems by sharing success stories of other customers.

[0105] The problem-solving department can provide video tutorials or interactive guides to solve customer problems. For example, a system is built to provide video tutorials to solve customer problems. For example, a video explaining a solution to a specific problem is provided. The problem-solving department also provides interactive guides to solve customer problems. For example, an interactive guide that guides customers through the problem-solving procedure step by step is provided. The problem-solving department also develops a system that automatically generates video tutorials or interactive guides to solve customer problems. For example, the problem-solving procedure is provided in a video or interactive format. In this way, by providing video tutorials or interactive guides, the efficiency of solving customer problems can be improved.

[0106] The problem-solving unit can use the emotion estimation function to take measures to reduce the customer's stress level. For example, the emotion estimation function is used to analyze the customer's stress level in real time and take appropriate measures. For example, if the customer is feeling stressed, measures to help the customer relax are taken. The problem-solving unit also builds a system that takes measures to reduce the customer's stress level. For example, kind words and encouraging messages are provided to a customer who is feeling stressed. The problem-solving unit also uses the emotion estimation function to take measures to reduce the customer's stress level. For example, if the customer is feeling stressed, simplified problem-solving procedures are provided. In this way, customer satisfaction can be improved by taking measures to reduce the customer's stress level.

[0107] The appointment setting unit can suggest the optimal time slot based on the customer's past appointment history. For example, a system can be constructed that analyzes the customer's past appointment history and suggests the optimal time slot. For example, suggestions can be made based on time slots that have had the most reservations in the past. The appointment setting unit can also automatically suggest the optimal time slot based on the customer's past appointment history. For example, it can prioritize suggestions for time slots that customers frequently make reservations for. The appointment setting unit can also analyze the customer's past appointment history and develop an algorithm that suggests the optimal time slot. For example, it can predict the optimal time slot based on past data. This can improve customer convenience by suggesting the optimal time slot based on the customer's past appointment history.

[0108] The appointment setting unit can work in conjunction with the customer's calendar to automatically avoid schedule conflicts. For example, a system can be built that works in conjunction with the customer's calendar to automatically avoid schedule conflicts. For example, the system can suggest the optimal appointment time based on the available time on the calendar. The appointment setting unit can also work in conjunction with the customer's calendar to develop an algorithm that automatically avoids schedule conflicts. For example, the system can automatically select a time slot that does not overlap with existing appointments. The appointment setting unit can also work in conjunction with the customer's calendar to provide a function that automatically avoids schedule conflicts. For example, the system can check the available time on the calendar in real time and suggest the optimal appointment time. This makes it possible to improve customer convenience by working in conjunction with the customer's calendar to automatically avoid schedule conflicts.

[0109] The appointment setting unit can automatically provide relevant materials and information when setting an appointment for a customer. For example, a system is constructed that automatically provides relevant materials and information when setting an appointment for a customer. For example, necessary documents and materials are automatically sent before the interview. The appointment setting unit also automatically provides relevant information when setting an appointment for a customer. For example, materials related to the purpose and agenda of the interview are sent in advance. The appointment setting unit also develops a function that automatically provides relevant materials and information when setting an appointment for a customer. For example, necessary information is automatically collected and provided before the interview. This makes it possible to improve customer convenience by automatically providing relevant materials and information when setting an appointment for a customer.

[0110] The appointment setting unit can notify customers of their appointment settings across multiple platforms. For example, a system is built to notify customers of their appointment settings across multiple platforms. For example, appointment confirmations are sent via email, SMS, and a chat app. The appointment setting unit also develops a function to notify customers of their appointment settings across multiple platforms. For example, appointment reminders are sent via both email and SMS. The appointment setting unit also develops a system to notify customers of their appointment settings across multiple platforms. For example, appointment details are sent via a chat app. This allows customers to be notified of their appointment settings across multiple platforms, improving customer convenience.

[0111] The appointment setting unit can display a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a system is constructed that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, appointment time slots are displayed in different colors on a calendar. The appointment setting unit also develops a function that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, a calendar is provided that allows the appointment time to be adjusted by drag and drop. The appointment setting unit also develops a system that displays a customer's appointment settings on a visual calendar, making it easier to manage them visually. For example, detailed appointment information is displayed as a pop-up on the calendar. This allows a customer's appointment settings to be displayed on a visual calendar, making it easier to manage them visually, thereby improving convenience for the customer.

[0112] The appointment setting unit can use the emotion estimation function to estimate the customer's desired time slot and propose the optimal appointment. For example, a system is constructed that uses the emotion estimation function to estimate the customer's desired time slot and propose the optimal appointment. For example, the optimal time slot is proposed based on the customer's past behavioral patterns and emotional data. The appointment setting unit also analyzes the customer's emotional data and develops an algorithm that estimates the customer's desired time slot. For example, it prioritizes proposing time slots when the customer is relaxed. The appointment setting unit also uses the emotion estimation function to develop a function that estimates the customer's desired time slot and proposes the optimal appointment. For example, it analyzes the customer's emotional state in real time and proposes the optimal time slot. This makes it possible to estimate the customer's desired time slot and propose the optimal appointment, thereby improving customer convenience.

[0113] The FAQ response unit can periodically update the FAQ database and provide the latest information. For example, a system can be built that periodically updates the FAQ database and provides the latest information. For example, information about new products and services can be periodically added. The FAQ response unit can also develop an algorithm that automatically updates the FAQ database. For example, it can update the FAQ based on customer feedback and inquiries. The FAQ response unit can also develop a function that periodically updates the FAQ database and provides the latest information. For example, it can periodically review the contents of the FAQ, delete old information, and add new information. In this way, the FAQ database can be periodically updated to provide the latest information, thereby improving customer convenience.

[0114] The FAQ response unit can automatically provide additional information and links related to the content of a customer's question. For example, a system can be built that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it can provide detailed explanations for specific questions or links to related web pages. The FAQ response unit can also develop an algorithm that automatically generates related additional information and links based on the content of a customer's question. For example, it can provide links to support articles and videos related to FAQ answers. The FAQ response unit can also develop a function that analyzes the content of a customer's question and automatically provides related additional information and links. For example, it can provide detailed explanations for specific questions or links to related resources. This can improve customer convenience by providing additional information and links related to the content of a customer's question.

[0115] The FAQ response unit can analyze the content of customer questions and suggest improvements to the FAQ database. For example, a system can be built that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, the content of the FAQ can be reviewed and improved based on frequently asked questions. The FAQ response unit can also develop an algorithm that analyzes the content of customer questions and automatically suggests improvements to the FAQ database. For example, it can suggest improvements when the answer to a particular question is insufficient. The FAQ response unit can also develop a function that analyzes the content of customer questions and suggests improvements to the FAQ database. For example, it can review and improve the content of the FAQ based on customer feedback. In this way, the quality of the FAQ can be improved by analyzing the content of customer questions and suggesting improvements to the FAQ database.

[0116] The FAQ response unit can provide FAQ responses in a multimedia format, such as video or infographics. For example, a system can be built that provides FAQ responses in video format. For example, explanations for specific questions can be provided in video to make them visually easier to understand. The FAQ response unit can also provide FAQ responses in infographic format. For example, complex information can be visually organized and provided in an easy-to-understand format. The FAQ response unit can also develop a function that provides FAQ responses in multimedia format. For example, a combination of video, infographics, audio guides, etc. can be provided. In this way, providing FAQ responses in multimedia formats, such as video or infographics, can promote customer understanding.

[0117] The FAQ response unit can automatically translate FAQ responses into different languages, achieving multilingual support. For example, a system can be built that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, FAQs can be provided in multiple languages, such as English, Spanish, and French. The FAQ response unit can also develop an algorithm that automatically translates FAQ responses. For example, it can provide FAQs that are automatically translated according to the customer's language settings. The FAQ response unit can also develop a function that automatically translates FAQ responses into different languages ​​and achieves multilingual support. For example, it can provide FAQs that are translated in real time. This allows FAQ responses to be automatically translated into different languages, achieving multilingual support, making it possible to respond to customers globally.

[0118] The FAQ response unit can use the emotion estimation function to evaluate customer satisfaction with questions and identify areas for improvement. For example, the emotion estimation function is used to build a system that evaluates customer satisfaction with questions in real time. For example, the emotion estimation function is used to analyze customers' facial expressions and voices and score their satisfaction. The FAQ response unit also develops an algorithm that evaluates customer satisfaction with questions and identifies areas for improvement. For example, it suggests areas for improvement for questions with low satisfaction. The FAQ response unit also uses the emotion estimation function to develop a function that evaluates customer satisfaction with questions and identifies areas for improvement. For example, it reviews and improves the content of FAQs for questions with low satisfaction. In this way, the quality of FAQs can be improved by evaluating customer satisfaction with questions and identifying areas for improvement.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The inquiry response unit responds to inquiries. For example, the inquiry response unit analyzes the content of the customer's inquiry and generates an appropriate response. The inquiry response unit can also generate individually customized responses based on the customer's past inquiry history. Furthermore, the inquiry response unit can analyze the customer's tone of voice and choice of words and use emotion estimation functionality to respond appropriately. For example, if the customer is angry, the inquiry response unit will respond calmly and politely. Step 2: The problem solver solves the problem. For example, the problem solver analyzes the customer's problem and proposes a solution. The problem solver can also track the progress of resolving the customer's problem in real time and provide additional support if needed. Furthermore, the problem solver can automatically suggest resources and tools needed to solve the customer's problem. For example, the problem solver can provide a document containing a solution for a specific error message. Step 3: The appointment setting unit sets the appointment. For example, the appointment setting unit analyzes the customer's request, checks the customer's schedule, and suggests an available time slot. The appointment setting unit can also suggest the optimal time slot based on the customer's past appointment history. Furthermore, the appointment setting unit can link with the customer's calendar and automatically avoid schedule conflicts. For example, it can suggest the optimal appointment time based on the available time slots on the calendar. Step 4: The FAQ response unit responds to the FAQ. For example, the FAQ response unit analyzes the customer's question and retrieves and provides an appropriate answer from the FAQ database. The FAQ response unit can also periodically update the FAQ database to provide the latest information. Furthermore, the FAQ response unit can automatically provide additional information or links related to the customer's question. For example, it can provide a detailed explanation of a specific question or a link to a related web page.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an inquiry handling department that handles inquiries; a problem solving department that solves problems; an appointment setting unit for setting appointments; an FAQ response unit that responds to FAQs; A system characterized by:

2. The inquiry response unit: Generate personalized responses based on customer inquiries 2. The system of claim 1.

3. The inquiry response unit: Analyze the customer's tone of voice and language to respond appropriately 2. The system of claim 1.

4. The inquiry response unit: Automatically provide additional information and suggestions related to customer inquiries 2. The system of claim 1.

5. The inquiry response unit: Translate customer inquiries in real time to provide multilingual support 2. The system of claim 1.

6. The inquiry response unit: Visualize customer inquiries and provide answers in a visually easy-to-understand format 2. The system of claim 1.

Images