system

Abstract

We provide the system. [Solution] A means of receiving inquiries from customers via communication methods, A means using a natural language processing engine to analyze the content of the aforementioned inquiry, A means using an artificial intelligence model that generates the optimal countermeasure based on the aforementioned analysis results, Means for providing the generated countermeasures to the customer, A means of providing customers with a progress tracking tool to check the progress of contracts and inquiries, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the property insurance industry, there is a problem that customer satisfaction decreases due to delays in handling customer inquiries and claims. In particular, since the conventional method is centered around manual processing, it is difficult to respond quickly. Also, it is impossible to consistently respond to inquiries from various communication means, and there is a large burden on personnel and costs. It is required to solve such problems and promote the activation of the industry and optimize insurance premiums.

Means for Solving the Problems

[0005] To address the above challenges, the present invention provides a system that takes in inquiries received from customers via various communication methods, analyzes their content using a natural language processing engine, and automatically generates appropriate countermeasures using an artificial intelligence model. This allows for the rapid and accurate development of countermeasures by utilizing historically accumulated data, and enables customers to understand the situation in real time using a progress monitoring tool. In addition, if automation is difficult, the system can improve overall response capabilities by escalating the issue to personnel as needed.

[0006] A "customer" is an entity that uses the system to enter into insurance contracts, file claims, or make inquiries.

[0007] "Communication methods" refer to the media that customers use to convey inquiries, such as telephone, chat, and email.

[0008] An "inquiry" refers to a question, request for information, or complaint made by a customer to the system.

[0009] A "natural language processing engine" is a technology that analyzes the content of received inquiries and converts human language into a format that a computer can understand and interpret.

[0010] "Analysis results" refer to the results of an analysis of customer inquiries obtained using a natural language processing engine.

[0011] "Optimal countermeasures" refer to specific action plans for providing appropriate processing and information in response to customer requests, based on the analysis results.

[0012] An "artificial intelligence model" is an algorithm that uses data to learn and generate appropriate responses to inquiries.

[0013] A "progress tracking tool" is an interface or function that allows customers to check the status and progress of their contracts or inquiries.

[0014] "Escalation" is the process of transferring problems or inquiries to human staff when AI-driven automated responses are insufficient. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for automating customer service processes in the field of non-life insurance. The system functions optimally through the interaction of servers, terminals, and users.

[0037] First, the user makes an inquiry via a communication method such as phone, chat, or email. The terminal receives this inquiry and sends it to the server in digital format. This receiving process supports a variety of communication channels and is designed to enhance customer convenience.

[0038] Next, the server passes the received query to the natural language processing engine. The natural language processing engine analyzes the user's intent and concerns, and based on this, determines the need for a response. The server inputs the analysis results into an artificial intelligence model, which generates the optimal response. The artificial intelligence model utilizes past response cases and customer data to make more accurate and faster decisions.

[0039] Subsequently, the server provides the generated countermeasures to the user via the terminal. At this time, the server notifies the user of the appropriate information using the selected communication method. This enables smooth, real-time customer support.

[0040] Furthermore, the server provides a progress tracking tool so that customers can check the progress of their contracts and complaints. Through this tool, users can check the progress of their inquiries and complaints in real time.

[0041] Furthermore, if the server recognizes a case that cannot be handled automatically by AI, it will escalate it to human staff. This escalation process ensures that inquiries and complaints requiring specialized attention can be addressed promptly.

[0042] As a concrete example, if a customer makes an insurance claim regarding a malfunctioning home appliance via chat, the server analyzes the content and an AI model proposes a solution such as arranging for a repair company. This allows the customer to proceed with the repair process quickly, improving customer satisfaction.

[0043] This system automates customer service processes in the property and casualty insurance industry, significantly improving processing efficiency and accuracy.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users use their devices to send inquiries or claims regarding property insurance via phone, chat, or email.

[0047] Step 2:

[0048] The terminal formats the received inquiry data, converts it to the appropriate format, and then sends it to the server.

[0049] Step 3:

[0050] The server passes the received query to a natural language processing engine, which analyzes the user's intent and content. This analysis clarifies the purpose of the query and the information being sought.

[0051] Step 4:

[0052] Based on the analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. In doing so, it refers to past similar inquiries and complaint history to derive the optimal solution.

[0053] Step 5:

[0054] The server provides the generated countermeasures through the communication method used by the user. Specifically, this may involve sending text messages via chat or sending replies via email.

[0055] Step 6:

[0056] Users receive responses from the server via their device and review the content. They can ask additional questions or make complaints as needed.

[0057] Step 7:

[0058] The server provides a progress tracking tool to help users check the status of their inquiries and complaints in real time.

[0059] Step 8:

[0060] If an automated response by AI is difficult, the server will escalate the issue to human staff based on pre-configured criteria.

[0061] Step 9:

[0062] The server assigns escalated cases to the appropriate staff using the personnel scheduling system.

[0063] Step 10:

[0064] Users are notified via their device that an escalation has taken place and can continue to communicate directly with human staff if necessary.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In the customer service process, responding to diverse communication methods in real time and quickly and accurately analyzing inquiries is crucial for increasing customer satisfaction. Providing prompt solutions, monitoring progress, and escalating issues to human staff as needed are also essential. Current systems struggle to efficiently achieve these goals, and therefore, a solution is needed.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for receiving inquiries from customers via various communication methods in real time and converting them into a digital format; means for passing the inquiries to a natural language processing engine to analyze the content and extract the type and urgency of the claim; and means for inputting the analysis results into an artificial intelligence model to generate the optimal response based on past cases and customer data. This enables customers to receive a quick and appropriate response.

[0070] "Diverse communication methods" refers to multiple ways for customers to make inquiries, such as phone calls, chats, and emails, meaning that customers can choose the method that is most convenient for them.

[0071] "Receiving in real time" means that when a customer inquiry is received, it is immediately processed as digital data.

[0072] "Converting to digital format" refers to converting queries in different formats into unified electronic data, making them analyzable by the system.

[0073] A "natural language processing engine" refers to software that has the ability to analyze written or spoken human language and understand its meaning.

[0074] "Extracting the type and urgency of complaints" refers to determining the nature and priority of an inquiry based on its content, and then deciding on the next steps accordingly.

[0075] An "artificial intelligence model" refers to a trained program that references past data and case studies to derive the optimal solution based on the given information.

[0076] "Escalating to human staff" refers to the process of identifying cases that automated systems cannot handle and directing intervention by human experts.

[0077] The "progress tracking function" refers to a system feature that allows customers to check the current status of their inquiries or contracts in real time.

[0078] This invention is a system that automates customer service processes by utilizing various communication methods, and its implementation is possible through the interaction of servers, terminals, and users.

[0079] First, users can initiate an inquiry via phone, chat, or email. For example, a user can report an insurance claim using a smartphone chat app. These diverse communication methods allow users to make inquiries in the way that is most convenient for them.

[0080] The terminal converts received inquiries into a digital format and sends them to the server. This process is performed in real time, enabling a rapid response. The server receives this data and analyzes it using a natural language processing engine. The analysis extracts information such as the content of the inquiry, its urgency, and the type of complaint.

[0081] Next, the server inputs these analysis results into a generating AI model. This model refers to past datasets and generates the optimal course of action. For example, in the case of an insurance claim regarding a broken home appliance, the model can suggest arranging for a repair company. This kind of automation reduces response time and leads to improved customer satisfaction.

[0082] The generated countermeasures are notified to the user in real time via the device. For example, the user receives a push notification on their smartphone and can quickly take the next steps based on it.

[0083] Furthermore, the server includes a progress tracking function, allowing users to check the progress of their inquiries and complaints in real time. This enables customers to always know what stage their case is at.

[0084] If the AI ​​cannot handle the issue, the server uses a prompt message to escalate it to a human staff member. An example of a prompt message might be, "We were unable to arrange repairs for your appliance. Specialized staff assistance is required." This process allows for quick handling of cases requiring more specialized assistance.

[0085] In this way, this system achieves efficiency and automation of customer service processes through the coordination of servers, terminals, and users.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users initiate inquiries to the system via phone, chat, or email, using devices such as smartphones or computers. The data entered is in text or voice format.

[0089] Step 2:

[0090] The terminal converts inquiries received from the user into a digital format. This process uses speech recognition and text analysis technologies to convert input data into standardized electronic data. The converted data is then sent to the server.

[0091] Step 3:

[0092] The server passes the digital data received from the terminal to the natural language processing engine. The engine analyzes the input data and extracts the claim type, urgency, and relevant details. The information extracted through data processing is used in the next step.

[0093] Step 4:

[0094] The server inputs the results analyzed by the natural language processing engine into an AI model. The AI ​​model refers to past datasets and generates the optimal countermeasures. Here, data calculations are used to output quick and accurate countermeasures.

[0095] Step 5:

[0096] The device notifies the user in real time of the countermeasures generated by the server. The user receives this information and can proceed to the next step. Output includes smartphone push notifications and email notifications.

[0097] Step 6:

[0098] The server displays the progress of user inquiries and complaints in real time through its progress tracking function. It outputs the data in a formatted format so that users can check the status of their own cases.

[0099] Step 7:

[0100] If the server cannot handle the issue automatically, it generates a prompt message and escalates it to human staff. When generating the prompt message, an AI model provides the necessary information to the specialist staff. This process ensures that advanced responses are provided quickly.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] In the current climate, where there is a demand for a system that can provide prompt and accurate responses to security-related inquiries, traditional methods make it difficult to perform advanced analysis, assess urgency, and escalate appropriately, resulting in a decline in customer satisfaction.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine to analyze the content of the inquiries, and means for using an artificial intelligence model to generate optimal countermeasures based on the analysis results. This enables real-time analysis of inquiries, prompt and accurate presentation of countermeasures, and automatic escalation to specialist staff as needed.

[0106] "Communication methods" refer to methods such as telephone, chat, and email that customers use to make inquiries to the server.

[0107] A "natural language processing engine" is software that includes technology for analyzing customer inquiries and understanding their meaning.

[0108] An "artificial intelligence model" is a program that generates the optimal course of action based on past datasets.

[0109] "Escalation" is a process in which the system automatically makes a decision and, if necessary, forwards the inquiry to a specialist.

[0110] A "historical dataset" is a collection of data that includes the history of past inquiries and responses, which is used by the system to learn and make more accurate decisions.

[0111] "Cloud computing technology" is a technology that provides computing resources via the internet and performs data storage and processing online.

[0112] The server uses communication methods and a natural language processing engine to receive customer inquiries and analyze their content. Specifically, when a customer makes an inquiry via phone, chat, or email, the information is first sent to the server through the terminal. The server analyzes this information using natural language processing engines such as SpaCy or NLTK to understand the customer's intent and urgency.

[0113] Based on the analysis results, the server generates the optimal solution using an artificial intelligence model based on TENSORFLOW® and provides it to the customer. If the generated solution is complex or difficult for the system to process, the escalation function will transfer it to specialist staff. This escalation is carried out quickly using cloud computing technology.

[0114] Furthermore, the server provides a progress tracking tool via Firebase, allowing customers to check the status of their inquiries in real time. This system enables customers to receive quick and accurate support for security-related issues, thereby improving customer satisfaction.

[0115] For example, if a user reports, "I've been seeing a lot of suspicious people around my house lately," the server will analyze the report and suggest countermeasures such as "increasing local patrols." It also has a process in place to request assistance from security experts if necessary. This process utilizes prompt messages.

[0116] An example of a prompt message is: "A new security incident has been reported. Based on the user's message, assess the urgency and generate the most appropriate response."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users submit inquiries via phone, chat, or email. The inquiry content is converted into digital data by the device and sent to the server. The input is the user's inquiry, and the output is the digital data sent to the server.

[0120] Step 2:

[0121] The server sends the received digital data to a natural language processing engine (e.g., SpaCy). Here, the query is analyzed, and the user's intent and urgency are extracted. The input is digital data, and the output is the analyzed query intent and urgency data. Specifically, keyword extraction and intent tagging are performed.

[0122] Step 3:

[0123] The server inputs the analysis results into an artificial intelligence model (using TensorFlow) to generate the optimal solution. This model makes decisions by referring to past response datasets. The input is the analysis results data, and the output is the optimal solution. Specifically, the proposals are scored and their priorities are evaluated.

[0124] Step 4:

[0125] The server sends the generated solution to the terminal and provides it to the user. Real-time feedback is provided via the user's chosen communication method (chat, email, etc.). The input is the optimal solution, and the output is the feedback provided.

[0126] Step 5:

[0127] When the server recognizes an inquiry as difficult to handle, it uses cloud computing technology to automatically escalate it and hand it over to specialist staff. The input is the data of the complex inquiry, and the output is the escalated inquiry and necessary explanations. Specifically, task lists are generated and notifications are sent to the responsible personnel.

[0128] Step 6:

[0129] The server provides a progress tracking tool via Firebase, allowing users to check the progress of their inquiries in real time. The input is the query status data, and the output is the update information displayed to the user. Specifically, this includes updating the user interface and issuing notifications.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that automates the customer service process in non-life insurance and further recognizes user emotions. This system functions effectively through the interaction of a server, terminal, and user.

[0132] Users use their device to submit insurance inquiries and claims via phone, chat, or email. The device receives these inquiries and records the content when it sends them to the server in digital format.

[0133] The server passes the received query to the natural language processing engine and the emotion engine. At this stage, the natural language processing engine analyzes the text and extracts the user's intent and the information they are looking for. Simultaneously, the emotion engine analyzes the user's expressions to determine their emotions and reveal their emotional state. For example, if anger or anxiety is detected, the emotion engine provides that information.

[0134] Based on these analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. By incorporating user emotional information, more personalized responses become possible. For example, a message is generated for an angry customer, aiming for a quick and considerate response.

[0135] Subsequently, the generated countermeasures are provided via the user's chosen communication method (chat, email, etc.) through the device. This allows the user to receive a response in real time.

[0136] Furthermore, the server provides users with a progress tracking tool. This tool allows users to check the processing status of inquiries and complaints in real time, supporting their peace of mind.

[0137] Based on the analysis results of the emotion engine, the system incorporates a function that automatically escalates issues if the AI ​​cannot handle them. In particular, if the emotion engine detects high stress or dissatisfaction, escalation to human staff is facilitated as a countermeasure.

[0138] As a concrete example, suppose a user files a claim for damages due to an accident via chat, and the emotion engine detects the customer's anxiety. In this case, the server may generate and provide countermeasures, including more detailed information and a swift processing schedule, to reassure the user.

[0139] This system improves the efficiency and accuracy of customer service, while also enabling flexible responses that respond to user emotions, thereby increasing customer satisfaction.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] Users can use their devices to send inquiries or complaints regarding property insurance via phone, chat, or email.

[0143] Step 2:

[0144] The terminal converts the received inquiry into the appropriate digital format and sends it to the server.

[0145] Step 3:

[0146] The server inputs the received query into a natural language processing engine, which then analyzes the text. This analysis identifies the user's intent and the information they are requesting.

[0147] Step 4:

[0148] Simultaneously, the server passes queries to the emotion engine, which analyzes the user's emotional state. For example, it determines whether the user is expressing stress or dissatisfaction.

[0149] Step 5:

[0150] The server integrates the analysis results from its natural language processing engine and emotion engine, inputting them into an artificial intelligence model to generate appropriate responses. This includes generating personalized messages based on the user's emotions.

[0151] Step 6:

[0152] The server provides the generated solution through the communication method used by the user. In this case, it will be sent as a text message if using chat, or as an email if using email.

[0153] Step 7:

[0154] The user receives a response from the server via their device and reviews the response. They can then pursue further inquiries or complaints as needed.

[0155] Step 8:

[0156] The server provides users with a progress tracking tool. This tool allows users to check the processing status of their inquiries and complaints in real time.

[0157] Step 9:

[0158] If the emotion engine detects high levels of stress or dissatisfaction, the server automatically escalates the issue. The escalated issue is then handed over to human staff.

[0159] Step 10:

[0160] The server assigns escalated cases to the appropriate personnel through the staffing scheduling system, enabling efficient responses.

[0161] Step 11:

[0162] Users receive communication from human staff via their device, continue the conversation as needed, and receive support to resolve their problems.

[0163] (Example 2)

[0164] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] Responding to customer inquiries requires appropriate and prompt responses that take emotions into consideration. However, conventional systems fail to fully utilize emotional information, resulting in insufficient quality of service for individual customers. Furthermore, the lack of transparency in progress tracking can lead to customer dissatisfaction. In addition, there are situations where human judgment is required, and the inability to appropriately escalate these situations presents a challenge.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0167] In this invention, the server includes means of using a device that receives inquiries from customers via communication means, means of using a device equipped with natural language processing capabilities for analyzing the content of the inquiries, and means of using a device that analyzes the user's emotional state and reveals emotional information. This makes it possible to generate personalized responses that take into account the customer's emotions, and is expected to improve customer satisfaction.

[0168] "Customer communication methods" refer to the means by which users send inquiries or information using methods such as telephone, chat, or email.

[0169] "Natural language processing functionality for analyzing inquiry content" refers to a technology that linguistically analyzes received text data and extracts meaning and intent from its content.

[0170] A "device for analyzing a user's emotional state" is a system that identifies a user's emotions from text and other inputs, and reveals their psychological state.

[0171] A "generative model" is an artificial intelligence technology that uses analyzed information to create the most appropriate countermeasures or responses.

[0172] A "progress confirmation device" is an interface or tool that allows users to check the processing status of inquiries and contracts in real time.

[0173] "Means for implementing changes in priorities" refers to a function that automatically adjusts the processing order of tasks and inquiries according to the situation, and facilitates human assistance when manual judgment is required.

[0174] This invention is a system that improves customer satisfaction by quickly and accurately handling customer inquiries and complaints. This system is based on interactions between a server, terminals, and users.

[0175] Users submit insurance inquiries and claims to their devices via communication methods such as phone, chat, or email. The device records this information as digital data and transmits it to a server in a secure manner. The device used for this process is typically an internet-connected computer or smartphone.

[0176] The server is equipped with a natural language processing engine to process the received data. This engine identifies the user's intent by analyzing the syntax and meaning of the received text. It also uses an emotion engine to analyze the user's emotions from the text, detecting psychological states such as anxiety and anger. This processing utilizes dedicated analysis software installed on the server.

[0177] After analysis, the server uses a generative AI model to generate the optimal response based on the results of natural language processing and sentiment analysis. The generated message is crafted to take into account the individual user's emotions. The generated message is delivered to the user via their device using the communication method of the user's choice.

[0178] Furthermore, the server provides users with a tool to check the progress of their queries. This tool is implemented as a web interface or mobile app, allowing users to check the progress in real time.

[0179] As a concrete example, when a user reports an accident via chat, the server analyzes the text and uses an emotion engine to detect that the user is feeling anxious. The generative AI model then creates a reassuring response and provides information quickly.

[0180] An example of a prompt message that can be input into the AI ​​model is, "Analyze the user's inquiry and sentiment, and generate the most appropriate automated response message."

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] Users submit insurance inquiries and claims using their devices. In this process, users send messages via phone, chat, or email. Input is the text message entered by the user. Output is the digital data received by the device.

[0184] Step 2:

[0185] The terminal records received messages as digital data and sends them to the server using a secure method, such as the HTTPS protocol. The input is the user's digital message received by the terminal, and the output is the data sent to the server.

[0186] Step 3:

[0187] The server uses a natural language processing engine to analyze incoming data. Specifically, it extracts keywords from the text and analyzes the context to understand the user's intent. The input is text data sent from the terminal, and the output is the analyzed intent and keywords.

[0188] Step 4:

[0189] The server uses an emotion engine to analyze emotions from user messages. Specifically, it detects emotional nuances from the user's writing style and expressions, and identifies states such as anxiety or anger. The input is text data, and the output is the emotion analysis result.

[0190] Step 5:

[0191] The server uses a generative AI model to generate optimal responses based on the results of natural language processing and sentiment analysis. Specifically, it automatically generates appropriate responses according to the identified intentions and emotions. The input is the result of the intention and emotion analysis, and the output is the generated response message.

[0192] Step 6:

[0193] The terminal receives generated messages from the server and delivers them to the user via a communication method selected by the user (e.g., chat or email). The input is the corresponding message sent from the server, and the output is the message displayed to the user.

[0194] Step 7:

[0195] The server provides users with processing progress through a progress tracking tool. This tool is accessible via a web interface or application and delivers real-time information to users. The input is the processing status of the query, and the output is the progress status displayed in the tool.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] Responding appropriately and promptly to customer inquiries and complaints is extremely important for businesses. However, traditional methods often fail to adequately consider the customer's emotional state and provide uniform responses, limiting the improvement of customer satisfaction. Furthermore, while human interaction is necessary, especially with emotionally sensitive customers, delays in making such judgments can lead to increased customer dissatisfaction.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine and an emotion analysis engine for analyzing the content of the inquiry and the customer's emotional state, and means for using an artificial intelligence model that generates an optimal response that takes the customer's emotional state into consideration based on the analysis results. This makes it possible to provide an optimal response that takes the customer's emotions into consideration, thereby improving customer satisfaction.

[0201] "Customer" refers to a consumer or legal entity that uses a company's services or products.

[0202] "Communication methods" refer to the ways in which customers interact with a company, such as telephone, email, and chat.

[0203] "Inquiry" refers to the act of a customer communicating their questions or problems to a company, or the content of such communication.

[0204] A "natural language processing engine" refers to a system that uses technology to analyze natural human language using machines.

[0205] An "emotion analysis engine" refers to a system that uses technology to extract emotional states from text data.

[0206] An "artificial intelligence model" refers to an algorithm or program that learns from large amounts of data and presents the optimal solution to a specific problem.

[0207] "Countermeasures" refer to appropriate solutions or methods of responding to inquiries.

[0208] A "progress tracking tool" refers to an interface or system that allows customers to check the processing status of their inquiries or contracts.

[0209] "Escalation" refers to the process of transferring a problem to a superior or a specialized team to receive a more expert response.

[0210] The system that realizes this invention includes a process of receiving customer inquiries, analyzing their emotions, and providing the most appropriate response. The server is central to the system and performs its functions in the following steps.

[0211] Users make inquiries using devices such as smartphones, via communication methods such as phone, chat, or email. This information is converted into a digital format by the device and sent to the server. The server analyzes the text using a natural language processing engine such as Google Cloud Natural Language API and understands the user's emotional state using an emotion engine such as Amazon Comprehend.

[0212] Based on the analysis results, the server uses artificial intelligence models such as Azure® Machine Learning to generate optimal responses that take customer emotions into consideration. These responses are sent from the server to the terminal in real time for the user to receive. The system also provides a progress tracking tool that allows users to check the processing status of their inquiries in real time.

[0213] For example, if a user submits an inquiry such as "My recent transaction hasn't been approved," a natural language processing engine analyzes the content, and a sentiment analysis engine detects frustration. A generative AI model is then used to generate a quick solution, sending the user a message such as, "We'll investigate immediately. Please wait while we find the cause of the problem and take the best possible action." An example of a prompt to input into the generative AI model would be, "Please provide an appropriate response message for a user who is upset about a transaction error."

[0214] This system enables flexible and personalized responses tailored to each customer's individual emotional state.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] Users use their devices to make inquiries via communication methods such as phone, chat, or email. The input is text data of the inquiry. This text data is converted into a digital format within the device and sent to the server as output.

[0218] Step 2:

[0219] The server receives the query text, which is then analyzed by a natural language processing engine. This process takes the query text as input and, through analysis, extracts output that reveals the user's intent and the information they are seeking. Specifically, the Google Cloud Natural Language API is used to perform the text analysis.

[0220] Step 3:

[0221] The server uses an emotion analysis engine to analyze the emotional state from the previously generated text data. In this step, the already analyzed text data is used as input, and data indicating the emotional state is output based on it. Amazon Comprehend is used to generate emotion tags such as anger, anxiety, and satisfaction.

[0222] Step 4:

[0223] The server utilizes an artificial intelligence model to generate optimal responses by considering emotional state data and intentions. The input is user intention and emotional data, and the output is customized messages and procedures for customer interaction. Azure Machine Learning enables this process.

[0224] Step 5:

[0225] The server sends the generated countermeasures to the terminal and provides real-time notification to the user. The input for this step is the generated countermeasures, and the output is information that displays the countermeasures on the user's terminal.

[0226] Step 6:

[0227] The server uses a progress tracking tool to allow users to check the processing status of their queries in real time. In this step, query progress data is used as input and displayed to the user as output. Often, this is visualized in a web interface or application.

[0228] These steps, when coordinated, enable prompt and emotionally sensitive responses to customers.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention is a system for automating customer service processes in the field of non-life insurance. The system functions optimally through the interaction of servers, terminals, and users.

[0246] First, the user makes an inquiry via a communication method such as phone, chat, or email. The terminal receives this inquiry and sends it to the server in digital format. This receiving process supports a variety of communication channels and is designed to enhance customer convenience.

[0247] Next, the server passes the received query to the natural language processing engine. The natural language processing engine analyzes the user's intent and concerns, and based on this, determines the need for a response. The server inputs the analysis results into an artificial intelligence model, which generates the optimal response. The artificial intelligence model utilizes past response cases and customer data to make more accurate and faster decisions.

[0248] Subsequently, the server provides the generated countermeasures to the user via the terminal. At this time, the server notifies the user of the appropriate information using the selected communication method. This enables smooth, real-time customer support.

[0249] Furthermore, the server provides a progress tracking tool so that customers can check the progress of their contracts and complaints. Through this tool, users can check the progress of their inquiries and complaints in real time.

[0250] Furthermore, if the server recognizes a case that cannot be handled automatically by AI, it will escalate it to human staff. This escalation process ensures that inquiries and complaints requiring specialized attention can be addressed promptly.

[0251] As a concrete example, if a customer makes an insurance claim regarding a malfunctioning home appliance via chat, the server analyzes the content and an AI model proposes a solution such as arranging for a repair company. This allows the customer to proceed with the repair process quickly, improving customer satisfaction.

[0252] This system automates customer service processes in the property and casualty insurance industry, significantly improving processing efficiency and accuracy.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] Users use their devices to send inquiries or claims regarding property insurance via phone, chat, or email.

[0256] Step 2:

[0257] The terminal formats the received inquiry data, converts it to the appropriate format, and then sends it to the server.

[0258] Step 3:

[0259] The server passes the received query to a natural language processing engine, which analyzes the user's intent and content. This analysis clarifies the purpose of the query and the information being sought.

[0260] Step 4:

[0261] Based on the analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. In doing so, it refers to past similar inquiries and complaint history to derive the optimal solution.

[0262] Step 5:

[0263] The server provides the generated countermeasures through the communication method used by the user. Specifically, this may involve sending text messages via chat or sending replies via email.

[0264] Step 6:

[0265] Users receive responses from the server via their device and review the content. They can ask additional questions or make complaints as needed.

[0266] Step 7:

[0267] The server provides a progress tracking tool to help users check the status of their inquiries and complaints in real time.

[0268] Step 8:

[0269] If an automated response by AI is difficult, the server will escalate the issue to human staff based on pre-configured criteria.

[0270] Step 9:

[0271] The server assigns escalated cases to the appropriate staff using the personnel scheduling system.

[0272] Step 10:

[0273] Users are notified via their device that an escalation has taken place and can continue to communicate directly with human staff if necessary.

[0274] (Example 1)

[0275] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In the customer service process, responding to diverse communication methods in real time and quickly and accurately analyzing inquiries is crucial for increasing customer satisfaction. Providing prompt solutions, monitoring progress, and escalating issues to human staff as needed are also essential. Current systems struggle to efficiently achieve these goals, and therefore, a solution is needed.

[0277] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0278] In this invention, the server includes means for receiving inquiries from customers in real time via various communication means and converting them into digital form, means for passing the inquiries to a natural language processing engine, analyzing the content, and extracting the type and urgency of the claims, and means for inputting the analysis results into an artificial intelligence model and generating an optimal response based on past cases and customer data. As a result, customers can receive a quick and appropriate response.

[0279] "Various communication means" refers to multiple inquiry methods from customers such as phone calls, chats, and emails, meaning that customers can choose the means that is most convenient for them.

[0280] "Receiving in real time" means immediately processing an inquiry from a customer as digital data when it is received.

[0281] "Converting to digital form" means converting inquiries in different forms into unified electronic data so that they can be analyzed by the system.

[0282] "Natural language processing engine" refers to software that has the ability to analyze written or spoken human language and understand its meaning.

[0283] "Extracting the type and urgency of the claims" means judging the nature and priority from the content of the inquiry and determining the next step based on that.

[0284] "Artificial intelligence model" refers to a trained program that refers to past data and cases and derives an optimal solution based on the given information.

[0285] "Escalation to human staff" means the process of identifying cases that cannot be handled by an automated system and instructing intervention by a person with specialized knowledge.

[0286] The "progress confirmation function" refers to a mechanism within a system that allows customers to check the current status of their inquiries or contracts in real time.

[0287] The present invention is a system that automates the customer service process by making use of various communication means, and can be realized through the interaction of servers, terminals, and users.

[0288] First, the user starts an inquiry using a phone, chat, or email. For example, the user can report an insurance claim using the chat app on a smartphone. With such diverse communication means, it is possible for the user to make an inquiry in the most convenient way for them.

[0289] The terminal converts the received inquiry into a digital format and sends it to the server. This process is carried out in real time, enabling a prompt response. The server receives this data and analyzes it using a natural language processing engine. As a result of the analysis, the content, urgency, type of claim, etc. of the inquiry are extracted.

[0290] Next, the server inputs the analysis result into a generated AI model. This model refers to past datasets and generates an optimal response. For example, in the case of an insurance claim regarding a malfunctioning household appliance, the model can propose arranging a repair technician. Such automation reduces the response time and leads to an improvement in customer satisfaction.

[0291] The generated response is notified to the user in real time through the terminal. For example, the user can receive a push notification on their smartphone and quickly proceed with the next steps based on it.

[0292] Furthermore, the server has a progress confirmation function, and the user can check the progress of their inquiries and claims in real time. As a result, customers can always be aware of which stage their cases are in.

[0293] If the AI ​​cannot handle the issue, the server uses a prompt message to escalate it to a human staff member. An example of a prompt message might be, "We were unable to arrange repairs for your appliance. Specialized staff assistance is required." This process allows for quick handling of cases requiring more specialized assistance.

[0294] In this way, this system achieves efficiency and automation of customer service processes through the coordination of servers, terminals, and users.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] Users initiate inquiries to the system via phone, chat, or email, using devices such as smartphones or computers. The data entered is in text or voice format.

[0298] Step 2:

[0299] The terminal converts inquiries received from the user into a digital format. This process uses speech recognition and text analysis technologies to convert input data into standardized electronic data. The converted data is then sent to the server.

[0300] Step 3:

[0301] The server passes the digital data received from the terminal to the natural language processing engine. The engine analyzes the input data and extracts the claim type, urgency, and relevant details. The information extracted through data processing is used in the next step.

[0302] Step 4:

[0303] The server inputs the results analyzed by the natural language processing engine into the generative AI model. The AI model refers to past datasets and generates optimal countermeasures. Here, quick and accurate countermeasures are output through data operations.

[0304] Step 5:

[0305] The terminal notifies the user in real time of the countermeasures generated by the server. The user can receive this information and proceed to the next procedure. Output includes push notifications on smartphones and guidance via email.

[0306] Step 6:

[0307] The server displays the progress of the user's inquiries and claims in real time through a progress confirmation function. Output is provided as formatted data so that the user can check the status of their case.

[0308] Step 7:

[0309] When automatic response is difficult, the server generates a prompt message and escalates it to human staff. When generating the prompt message, the AI model provides the information necessary for the specialist staff. Through this process, high-level responses are provided quickly.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In the current situation where a mechanism for quickly and accurately providing countermeasures in security-related inquiries is required, conventional methods have problems such as difficulty in advanced analysis, evaluation of urgency, and appropriate escalation, leading to a decline in customer satisfaction.

[0313] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine to analyze the content of the inquiries, and means for using an artificial intelligence model to generate optimal countermeasures based on the analysis results. This enables real-time analysis of inquiries, prompt and accurate presentation of countermeasures, and automatic escalation to specialist staff as needed.

[0315] "Communication methods" refer to methods such as telephone, chat, and email that customers use to make inquiries to the server.

[0316] A "natural language processing engine" is software that includes technology for analyzing customer inquiries and understanding their meaning.

[0317] An "artificial intelligence model" is a program that generates the optimal course of action based on past datasets.

[0318] "Escalation" is a process in which the system automatically makes a decision and, if necessary, forwards the inquiry to a specialist.

[0319] A "historical dataset" is a collection of data that includes the history of past inquiries and responses, which is used by the system to learn and make more accurate decisions.

[0320] "Cloud computing technology" is a technology that provides computing resources via the internet and performs data storage and processing online.

[0321] The server uses communication methods and a natural language processing engine to receive customer inquiries and analyze their content. Specifically, when a customer makes an inquiry via phone, chat, or email, the information is first sent to the server through the terminal. The server analyzes this information using natural language processing engines such as SpaCy or NLTK to understand the customer's intent and urgency.

[0322] Based on the analysis results, the server generates the optimal solution using an artificial intelligence model based on TensorFlow and provides it to the customer. If the generated solution is complex or difficult for the system to process, the escalation function will transfer it to specialist staff. This escalation is carried out quickly using cloud computing technology.

[0323] Furthermore, the server provides a progress tracking tool via Firebase, allowing customers to check the status of their inquiries in real time. This system enables customers to receive quick and accurate support for security-related issues, thereby improving customer satisfaction.

[0324] For example, if a user reports, "I've been seeing a lot of suspicious people around my house lately," the server will analyze the report and suggest countermeasures such as "increasing local patrols." It also has a process in place to request assistance from security experts if necessary. This process utilizes prompt messages.

[0325] An example of a prompt message is: "A new security incident has been reported. Based on the user's message, assess the urgency and generate the most appropriate response."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] Users submit inquiries via phone, chat, or email. The inquiry content is converted into digital data by the device and sent to the server. The input is the user's inquiry, and the output is the digital data sent to the server.

[0329] Step 2:

[0330] The server sends the received digital data to a natural language processing engine (e.g., SpaCy). Here, the query is analyzed, and the user's intent and urgency are extracted. The input is digital data, and the output is the analyzed query intent and urgency data. Specifically, keyword extraction and intent tagging are performed.

[0331] Step 3:

[0332] The server inputs the analysis results into an artificial intelligence model (using TensorFlow) to generate the optimal solution. This model makes decisions by referring to past response datasets. The input is the analysis results data, and the output is the optimal solution. Specifically, the proposals are scored and their priorities are evaluated.

[0333] Step 4:

[0334] The server sends the generated solution to the terminal and provides it to the user. Real-time feedback is provided via the user's chosen communication method (chat, email, etc.). The input is the optimal solution, and the output is the feedback provided.

[0335] Step 5:

[0336] When the server recognizes an inquiry as difficult to handle, it uses cloud computing technology to automatically escalate it and hand it over to specialist staff. The input is the data of the complex inquiry, and the output is the escalated inquiry and necessary explanations. Specifically, task lists are generated and notifications are sent to the responsible personnel.

[0337] Step 6:

[0338] The server provides a progress tracking tool via Firebase, allowing users to check the progress of their inquiries in real time. The input is the query status data, and the output is the update information displayed to the user. Specifically, this includes updating the user interface and issuing notifications.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] This invention is a system that automates the customer service process in non-life insurance and further recognizes user emotions. This system functions effectively through the interaction of a server, terminal, and user.

[0341] Users use their device to submit insurance inquiries and claims via phone, chat, or email. The device receives these inquiries and records the content when it sends them to the server in digital format.

[0342] The server passes the received query to the natural language processing engine and the emotion engine. At this stage, the natural language processing engine analyzes the text and extracts the user's intent and the information they are looking for. Simultaneously, the emotion engine analyzes the user's expressions to determine their emotions and reveal their emotional state. For example, if anger or anxiety is detected, the emotion engine provides that information.

[0343] Based on these analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. By incorporating user emotional information, more personalized responses become possible. For example, a message is generated for an angry customer, aiming for a quick and considerate response.

[0344] Subsequently, the generated countermeasures are provided via the user's chosen communication method (chat, email, etc.) through the device. This allows the user to receive a response in real time.

[0345] Furthermore, the server provides users with a progress tracking tool. This tool allows users to check the processing status of inquiries and complaints in real time, supporting their peace of mind.

[0346] Based on the analysis results of the emotion engine, the system incorporates a function that automatically escalates issues if the AI ​​cannot handle them. In particular, if the emotion engine detects high stress or dissatisfaction, escalation to human staff is facilitated as a countermeasure.

[0347] As a concrete example, suppose a user files a claim for damages due to an accident via chat, and the emotion engine detects the customer's anxiety. In this case, the server may generate and provide countermeasures, including more detailed information and a swift processing schedule, to reassure the user.

[0348] This system improves the efficiency and accuracy of customer service, while also enabling flexible responses that respond to user emotions, thereby increasing customer satisfaction.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] Users can use their devices to send inquiries or complaints regarding property insurance via phone, chat, or email.

[0352] Step 2:

[0353] The terminal converts the received inquiry into the appropriate digital format and sends it to the server.

[0354] Step 3:

[0355] The server inputs the received query into a natural language processing engine, which then analyzes the text. This analysis identifies the user's intent and the information they are requesting.

[0356] Step 4:

[0357] Simultaneously, the server passes queries to the emotion engine, which analyzes the user's emotional state. For example, it determines whether the user is expressing stress or dissatisfaction.

[0358] Step 5:

[0359] The server integrates the analysis results from its natural language processing engine and emotion engine, inputting them into an artificial intelligence model to generate appropriate responses. This includes generating personalized messages based on the user's emotions.

[0360] Step 6:

[0361] The server provides the generated solution through the communication method used by the user. In this case, it will be sent as a text message if using chat, or as an email if using email.

[0362] Step 7:

[0363] The user receives a response from the server via their device and reviews the response. They can then pursue further inquiries or complaints as needed.

[0364] Step 8:

[0365] The server provides users with a progress tracking tool. This tool allows users to check the processing status of their inquiries and complaints in real time.

[0366] Step 9:

[0367] If the emotion engine detects high levels of stress or dissatisfaction, the server automatically escalates the issue. The escalated issue is then handed over to human staff.

[0368] Step 10:

[0369] The server assigns escalated cases to the appropriate personnel through the staffing scheduling system, enabling efficient responses.

[0370] Step 11:

[0371] Users receive communication from human staff via their device, continue the conversation as needed, and receive support to resolve their problems.

[0372] (Example 2)

[0373] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0374] Responding to customer inquiries requires appropriate and prompt responses that take emotions into consideration. However, conventional systems fail to fully utilize emotional information, resulting in insufficient quality of service for individual customers. Furthermore, the lack of transparency in progress tracking can lead to customer dissatisfaction. In addition, there are situations where human judgment is required, and the inability to appropriately escalate these situations presents a challenge.

[0375] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0376] In this invention, the server includes means of using a device that receives inquiries from customers via communication means, means of using a device equipped with natural language processing capabilities for analyzing the content of the inquiries, and means of using a device that analyzes the user's emotional state and reveals emotional information. This makes it possible to generate personalized responses that take into account the customer's emotions, and is expected to improve customer satisfaction.

[0377] "Customer communication methods" refer to the means by which users send inquiries or information using methods such as telephone, chat, or email.

[0378] "Natural language processing functionality for analyzing inquiry content" refers to a technology that linguistically analyzes received text data and extracts meaning and intent from its content.

[0379] A "device for analyzing a user's emotional state" is a system that identifies a user's emotions from text and other inputs, and reveals their psychological state.

[0380] A "generative model" is an artificial intelligence technology that uses analyzed information to create the most appropriate countermeasures or responses.

[0381] A "progress confirmation device" is an interface or tool that allows users to check the processing status of inquiries and contracts in real time.

[0382] "Means for implementing changes in priorities" refers to a function that automatically adjusts the processing order of tasks and inquiries according to the situation, and facilitates human assistance when manual judgment is required.

[0383] This invention is a system that improves customer satisfaction by quickly and accurately handling customer inquiries and complaints. This system is based on interactions between a server, terminals, and users.

[0384] Users submit insurance inquiries and claims to their devices via communication methods such as phone, chat, or email. The device records this information as digital data and transmits it to a server in a secure manner. The device used for this process is typically an internet-connected computer or smartphone.

[0385] The server is equipped with a natural language processing engine to process the received data. This engine identifies the user's intent by analyzing the syntax and meaning of the received text. It also uses an emotion engine to analyze the user's emotions from the text, detecting psychological states such as anxiety and anger. This processing utilizes dedicated analysis software installed on the server.

[0386] After analysis, the server uses a generative AI model to generate the optimal response based on the results of natural language processing and sentiment analysis. The generated message is crafted to take into account the individual user's emotions. The generated message is delivered to the user via their device using the communication method of the user's choice.

[0387] Furthermore, the server provides users with a tool to check the progress of their queries. This tool is implemented as a web interface or mobile app, allowing users to check the progress in real time.

[0388] As a concrete example, when a user reports an accident via chat, the server analyzes the text and uses an emotion engine to detect that the user is feeling anxious. The generative AI model then creates a reassuring response and provides information quickly.

[0389] An example of a prompt message that can be input into the AI ​​model is, "Analyze the user's inquiry and sentiment, and generate the most appropriate automated response message."

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] Users submit insurance inquiries and claims using their devices. In this process, users send messages via phone, chat, or email. Input is the text message entered by the user. Output is the digital data received by the device.

[0393] Step 2:

[0394] The terminal records received messages as digital data and sends them to the server using a secure method, such as the HTTPS protocol. The input is the user's digital message received by the terminal, and the output is the data sent to the server.

[0395] Step 3:

[0396] The server uses a natural language processing engine to analyze incoming data. Specifically, it extracts keywords from the text and analyzes the context to understand the user's intent. The input is text data sent from the terminal, and the output is the analyzed intent and keywords.

[0397] Step 4:

[0398] The server uses an emotion engine to analyze emotions from user messages. Specifically, it detects emotional nuances from the user's writing style and expressions, and identifies states such as anxiety or anger. The input is text data, and the output is the emotion analysis result.

[0399] Step 5:

[0400] The server uses a generative AI model to generate optimal responses based on the results of natural language processing and sentiment analysis. Specifically, it automatically generates appropriate responses according to the identified intentions and emotions. The input is the result of the intention and emotion analysis, and the output is the generated response message.

[0401] Step 6:

[0402] The terminal receives generated messages from the server and delivers them to the user via a communication method selected by the user (e.g., chat or email). The input is the corresponding message sent from the server, and the output is the message displayed to the user.

[0403] Step 7:

[0404] The server provides users with processing progress through a progress tracking tool. This tool is accessible via a web interface or application and delivers real-time information to users. The input is the processing status of the query, and the output is the progress status displayed in the tool.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0407] Responding appropriately and promptly to customer inquiries and complaints is extremely important for businesses. However, traditional methods often fail to adequately consider the customer's emotional state and provide uniform responses, limiting the improvement of customer satisfaction. Furthermore, while human interaction is necessary, especially with emotionally sensitive customers, delays in making such judgments can lead to increased customer dissatisfaction.

[0408] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0409] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine and an emotion analysis engine for analyzing the content of the inquiry and the customer's emotional state, and means for using an artificial intelligence model that generates an optimal response that takes the customer's emotional state into consideration based on the analysis results. This makes it possible to provide an optimal response that takes the customer's emotions into consideration, thereby improving customer satisfaction.

[0410] "Customer" refers to a consumer or legal entity that uses a company's services or products.

[0411] "Communication methods" refer to the ways in which customers interact with a company, such as telephone, email, and chat.

[0412] "Inquiry" refers to the act of a customer communicating their questions or problems to a company, or the content of such communication.

[0413] A "natural language processing engine" refers to a system that uses technology to analyze natural human language using machines.

[0414] An "emotion analysis engine" refers to a system that uses technology to extract emotional states from text data.

[0415] An "artificial intelligence model" refers to an algorithm or program that learns from large amounts of data and presents the optimal solution to a specific problem.

[0416] "Countermeasures" refer to appropriate solutions or methods of responding to inquiries.

[0417] A "progress tracking tool" refers to an interface or system that allows customers to check the processing status of their inquiries or contracts.

[0418] "Escalation" refers to the process of transferring a problem to a superior or a specialized team to receive a more expert response.

[0419] The system that realizes this invention includes a process of receiving customer inquiries, analyzing their emotions, and providing the most appropriate response. The server is central to the system and performs its functions in the following steps.

[0420] Users make inquiries using devices such as smartphones, via communication methods such as phone, chat, or email. This information is converted into a digital format by the device and sent to the server. The server analyzes the text using a natural language processing engine such as Google Cloud Natural Language API and understands the user's emotional state using an emotion engine such as Amazon Comprehend.

[0421] Based on the analysis results, the server uses artificial intelligence models such as Azure Machine Learning to generate optimal responses that take customer emotions into consideration. These responses are sent from the server to the terminal in real time for the user to receive. The system also provides a progress tracking tool that allows users to check the processing status of their inquiries in real time.

[0422] For example, if a user submits an inquiry such as "My recent transaction hasn't been approved," a natural language processing engine analyzes the content, and a sentiment analysis engine detects frustration. A generative AI model is then used to generate a quick solution, sending the user a message such as, "We'll investigate immediately. Please wait while we find the cause of the problem and take the best possible action." An example of a prompt to input into the generative AI model would be, "Please provide an appropriate response message for a user who is upset about a transaction error."

[0423] This system enables flexible and personalized responses tailored to each customer's individual emotional state.

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] Users use their devices to make inquiries via communication methods such as phone, chat, or email. The input is text data of the inquiry. This text data is converted into a digital format within the device and sent to the server as output.

[0427] Step 2:

[0428] The server receives the query text, which is then analyzed by a natural language processing engine. This process takes the query text as input and, through analysis, extracts output that reveals the user's intent and the information they are seeking. Specifically, the Google Cloud Natural Language API is used to perform the text analysis.

[0429] Step 3:

[0430] The server uses an emotion analysis engine to analyze the emotional state from the previously generated text data. In this step, the already analyzed text data is used as input, and data indicating the emotional state is output based on it. Amazon Comprehend is used to generate emotion tags such as anger, anxiety, and satisfaction.

[0431] Step 4:

[0432] The server utilizes an artificial intelligence model to generate optimal responses by considering emotional state data and intentions. The input is user intention and emotional data, and the output is customized messages and procedures for customer interaction. Azure Machine Learning enables this process.

[0433] Step 5:

[0434] The server sends the generated countermeasures to the terminal and provides real-time notification to the user. The input for this step is the generated countermeasures, and the output is information that displays the countermeasures on the user's terminal.

[0435] Step 6:

[0436] The server uses a progress tracking tool to allow users to check the processing status of their queries in real time. In this step, query progress data is used as input and displayed to the user as output. Often, this is visualized in a web interface or application.

[0437] These steps, when coordinated, enable prompt and emotionally sensitive responses to customers.

[0438] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0439] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0444] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0445] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0446] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0447] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0448] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0449] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0450] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0451] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0452] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0453] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0454] This invention is a system for automating customer service processes in the field of non-life insurance. The system functions optimally through the interaction of servers, terminals, and users.

[0455] First, the user makes an inquiry via a communication method such as phone, chat, or email. The terminal receives this inquiry and sends it to the server in digital format. This receiving process supports a variety of communication channels and is designed to enhance customer convenience.

[0456] Next, the server passes the received query to the natural language processing engine. The natural language processing engine analyzes the user's intent and concerns, and based on this, determines the need for a response. The server inputs the analysis results into an artificial intelligence model, which generates the optimal response. The artificial intelligence model utilizes past response cases and customer data to make more accurate and faster decisions.

[0457] Subsequently, the server provides the generated countermeasures to the user via the terminal. At this time, the server notifies the user of the appropriate information using the selected communication method. This enables smooth, real-time customer support.

[0458] Furthermore, the server provides a progress tracking tool so that customers can check the progress of their contracts and complaints. Through this tool, users can check the progress of their inquiries and complaints in real time.

[0459] Furthermore, if the server recognizes a case that cannot be handled automatically by AI, it will escalate it to human staff. This escalation process ensures that inquiries and complaints requiring specialized attention can be addressed promptly.

[0460] As a concrete example, if a customer makes an insurance claim regarding a malfunctioning home appliance via chat, the server analyzes the content and an AI model proposes a solution such as arranging for a repair company. This allows the customer to proceed with the repair process quickly, improving customer satisfaction.

[0461] This system automates customer service processes in the property and casualty insurance industry, significantly improving processing efficiency and accuracy.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] Users use their devices to send inquiries or claims regarding property insurance via phone, chat, or email.

[0465] Step 2:

[0466] The terminal formats the received inquiry data, converts it to the appropriate format, and then sends it to the server.

[0467] Step 3:

[0468] The server passes the received query to a natural language processing engine, which analyzes the user's intent and content. This analysis clarifies the purpose of the query and the information being sought.

[0469] Step 4:

[0470] Based on the analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. In doing so, it refers to past similar inquiries and complaint history to derive the optimal solution.

[0471] Step 5:

[0472] The server provides the generated countermeasures through the communication method used by the user. Specifically, this may involve sending text messages via chat or sending replies via email.

[0473] Step 6:

[0474] Users receive responses from the server via their device and review the content. They can ask additional questions or make complaints as needed.

[0475] Step 7:

[0476] The server provides a progress tracking tool to help users check the status of their inquiries and complaints in real time.

[0477] Step 8:

[0478] If an automated response by AI is difficult, the server will escalate the issue to human staff based on pre-configured criteria.

[0479] Step 9:

[0480] The server assigns escalated cases to the appropriate staff using the personnel scheduling system.

[0481] Step 10:

[0482] Users are notified via their device that an escalation has taken place and can continue to communicate directly with human staff if necessary.

[0483] (Example 1)

[0484] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] In the customer service process, responding to diverse communication methods in real time and quickly and accurately analyzing inquiries is crucial for increasing customer satisfaction. Providing prompt solutions, monitoring progress, and escalating issues to human staff as needed are also essential. Current systems struggle to efficiently achieve these goals, and therefore, a solution is needed.

[0486] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0487] In this invention, the server includes means for receiving inquiries from customers via various communication methods in real time and converting them into a digital format; means for passing the inquiries to a natural language processing engine to analyze the content and extract the type and urgency of the claim; and means for inputting the analysis results into an artificial intelligence model to generate the optimal response based on past cases and customer data. This enables customers to receive a quick and appropriate response.

[0488] "Diverse communication methods" refers to multiple ways for customers to make inquiries, such as phone calls, chats, and emails, meaning that customers can choose the method that is most convenient for them.

[0489] "Receiving in real time" means that when a customer inquiry is received, it is immediately processed as digital data.

[0490] "Converting to digital format" refers to converting queries in different formats into unified electronic data, making them analyzable by the system.

[0491] A "natural language processing engine" refers to software that has the ability to analyze written or spoken human language and understand its meaning.

[0492] "Extracting the type and urgency of complaints" refers to determining the nature and priority of an inquiry based on its content, and then deciding on the next steps accordingly.

[0493] An "artificial intelligence model" refers to a trained program that references past data and case studies to derive the optimal solution based on the given information.

[0494] "Escalating to human staff" refers to the process of identifying cases that automated systems cannot handle and directing intervention by human experts.

[0495] The "progress tracking function" refers to a system feature that allows customers to check the current status of their inquiries or contracts in real time.

[0496] This invention is a system that automates customer service processes by utilizing various communication methods, and its implementation is possible through the interaction of servers, terminals, and users.

[0497] First, users can initiate an inquiry via phone, chat, or email. For example, a user can report an insurance claim using a smartphone chat app. These diverse communication methods allow users to make inquiries in the way that is most convenient for them.

[0498] The terminal converts received inquiries into a digital format and sends them to the server. This process is performed in real time, enabling a rapid response. The server receives this data and analyzes it using a natural language processing engine. The analysis extracts information such as the content of the inquiry, its urgency, and the type of complaint.

[0499] Next, the server inputs these analysis results into a generating AI model. This model refers to past datasets and generates the optimal course of action. For example, in the case of an insurance claim regarding a broken home appliance, the model can suggest arranging for a repair company. This kind of automation reduces response time and leads to improved customer satisfaction.

[0500] The generated countermeasures are notified to the user in real time via the device. For example, the user receives a push notification on their smartphone and can quickly take the next steps based on it.

[0501] Furthermore, the server includes a progress tracking function, allowing users to check the progress of their inquiries and complaints in real time. This enables customers to always know what stage their case is at.

[0502] If the AI ​​cannot handle the issue, the server uses a prompt message to escalate it to a human staff member. An example of a prompt message might be, "We were unable to arrange repairs for your appliance. Specialized staff assistance is required." This process allows for quick handling of cases requiring more specialized assistance.

[0503] In this way, this system achieves efficiency and automation of customer service processes through the coordination of servers, terminals, and users.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] Users initiate inquiries to the system via phone, chat, or email, using devices such as smartphones or computers. The data entered is in text or voice format.

[0507] Step 2:

[0508] The terminal converts inquiries received from the user into a digital format. This process uses speech recognition and text analysis technologies to convert input data into standardized electronic data. The converted data is then sent to the server.

[0509] Step 3:

[0510] The server passes the digital data received from the terminal to the natural language processing engine. The engine analyzes the input data and extracts the claim type, urgency, and relevant details. The information extracted through data processing is used in the next step.

[0511] Step 4:

[0512] The server inputs the results analyzed by the natural language processing engine into an AI model. The AI ​​model refers to past datasets and generates the optimal countermeasures. Here, data calculations are used to output quick and accurate countermeasures.

[0513] Step 5:

[0514] The device notifies the user in real time of the countermeasures generated by the server. The user receives this information and can proceed to the next step. Output includes smartphone push notifications and email notifications.

[0515] Step 6:

[0516] The server displays the progress of user inquiries and complaints in real time through its progress tracking function. It outputs the data in a formatted format so that users can check the status of their own cases.

[0517] Step 7:

[0518] If the server cannot handle the issue automatically, it generates a prompt message and escalates it to human staff. When generating the prompt message, an AI model provides the necessary information to the specialist staff. This process ensures that advanced responses are provided quickly.

[0519] (Application Example 1)

[0520] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] In the current climate, where there is a demand for a system that can provide prompt and accurate responses to security-related inquiries, traditional methods make it difficult to perform advanced analysis, assess urgency, and escalate appropriately, resulting in a decline in customer satisfaction.

[0522] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0523] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine to analyze the content of the inquiries, and means for using an artificial intelligence model to generate optimal countermeasures based on the analysis results. This enables real-time analysis of inquiries, prompt and accurate presentation of countermeasures, and automatic escalation to specialist staff as needed.

[0524] "Communication methods" refer to methods such as telephone, chat, and email that customers use to make inquiries to the server.

[0525] A "natural language processing engine" is software that includes technology for analyzing customer inquiries and understanding their meaning.

[0526] An "artificial intelligence model" is a program that generates the optimal course of action based on past datasets.

[0527] "Escalation" is a process in which the system automatically makes a decision and, if necessary, forwards the inquiry to a specialist.

[0528] A "historical dataset" is a collection of data that includes the history of past inquiries and responses, which is used by the system to learn and make more accurate decisions.

[0529] "Cloud computing technology" is a technology that provides computing resources via the internet and performs data storage and processing online.

[0530] The server uses communication methods and a natural language processing engine to receive customer inquiries and analyze their content. Specifically, when a customer makes an inquiry via phone, chat, or email, the information is first sent to the server through the terminal. The server analyzes this information using natural language processing engines such as SpaCy or NLTK to understand the customer's intent and urgency.

[0531] Based on the analysis results, the server generates the optimal solution using an artificial intelligence model based on TensorFlow and provides it to the customer. If the generated solution is complex or difficult for the system to process, the escalation function will transfer it to specialist staff. This escalation is carried out quickly using cloud computing technology.

[0532] Furthermore, the server provides a progress tracking tool via Firebase, allowing customers to check the status of their inquiries in real time. This system enables customers to receive quick and accurate support for security-related issues, thereby improving customer satisfaction.

[0533] For example, if a user reports, "I've been seeing a lot of suspicious people around my house lately," the server will analyze the report and suggest countermeasures such as "increasing local patrols." It also has a process in place to request assistance from security experts if necessary. This process utilizes prompt messages.

[0534] An example of a prompt message is: "A new security incident has been reported. Based on the user's message, assess the urgency and generate the most appropriate response."

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] Users submit inquiries via phone, chat, or email. The inquiry content is converted into digital data by the device and sent to the server. The input is the user's inquiry, and the output is the digital data sent to the server.

[0538] Step 2:

[0539] The server sends the received digital data to a natural language processing engine (e.g., SpaCy). Here, the query is analyzed, and the user's intent and urgency are extracted. The input is digital data, and the output is the analyzed query intent and urgency data. Specifically, keyword extraction and intent tagging are performed.

[0540] Step 3:

[0541] The server inputs the analysis results into an artificial intelligence model (using TensorFlow) to generate the optimal solution. This model makes decisions by referring to past response datasets. The input is the analysis results data, and the output is the optimal solution. Specifically, the proposals are scored and their priorities are evaluated.

[0542] Step 4:

[0543] The server sends the generated solution to the terminal and provides it to the user. Real-time feedback is provided via the user's chosen communication method (chat, email, etc.). The input is the optimal solution, and the output is the feedback provided.

[0544] Step 5:

[0545] When the server recognizes an inquiry as difficult to handle, it uses cloud computing technology to automatically escalate it and hand it over to specialist staff. The input is the data of the complex inquiry, and the output is the escalated inquiry and necessary explanations. Specifically, task lists are generated and notifications are sent to the responsible personnel.

[0546] Step 6:

[0547] The server provides a progress tracking tool via Firebase, allowing users to check the progress of their inquiries in real time. The input is the query status data, and the output is the update information displayed to the user. Specifically, this includes updating the user interface and issuing notifications.

[0548] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0549] This invention is a system that automates the customer service process in non-life insurance and further recognizes user emotions. This system functions effectively through the interaction of a server, terminal, and user.

[0550] Users use their device to submit insurance inquiries and claims via phone, chat, or email. The device receives these inquiries and records the content when it sends them to the server in digital format.

[0551] The server passes the received query to the natural language processing engine and the emotion engine. At this stage, the natural language processing engine analyzes the text and extracts the user's intent and the information they are looking for. Simultaneously, the emotion engine analyzes the user's expressions to determine their emotions and reveal their emotional state. For example, if anger or anxiety is detected, the emotion engine provides that information.

[0552] Based on these analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. By incorporating user emotional information, more personalized responses become possible. For example, a message is generated for an angry customer, aiming for a quick and considerate response.

[0553] Subsequently, the generated countermeasures are provided via the user's chosen communication method (chat, email, etc.) through the device. This allows the user to receive a response in real time.

[0554] Furthermore, the server provides users with a progress tracking tool. This tool allows users to check the processing status of inquiries and complaints in real time, supporting their peace of mind.

[0555] Based on the analysis results of the emotion engine, the system incorporates a function that automatically escalates issues if the AI ​​cannot handle them. In particular, if the emotion engine detects high stress or dissatisfaction, escalation to human staff is facilitated as a countermeasure.

[0556] As a concrete example, suppose a user files a claim for damages due to an accident via chat, and the emotion engine detects the customer's anxiety. In this case, the server may generate and provide countermeasures, including more detailed information and a swift processing schedule, to reassure the user.

[0557] This system improves the efficiency and accuracy of customer service, while also enabling flexible responses that respond to user emotions, thereby increasing customer satisfaction.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] Users can use their devices to send inquiries or complaints regarding property insurance via phone, chat, or email.

[0561] Step 2:

[0562] The terminal converts the received inquiry into the appropriate digital format and sends it to the server.

[0563] Step 3:

[0564] The server inputs the received query into a natural language processing engine, which then analyzes the text. This analysis identifies the user's intent and the information they are requesting.

[0565] Step 4:

[0566] Simultaneously, the server passes queries to the emotion engine, which analyzes the user's emotional state. For example, it determines whether the user is expressing stress or dissatisfaction.

[0567] Step 5:

[0568] The server integrates the analysis results from its natural language processing engine and emotion engine, inputting them into an artificial intelligence model to generate appropriate responses. This includes generating personalized messages based on the user's emotions.

[0569] Step 6:

[0570] The server provides the generated solution through the communication method used by the user. In this case, it will be sent as a text message if using chat, or as an email if using email.

[0571] Step 7:

[0572] The user receives a response from the server via their device and reviews the response. They can then pursue further inquiries or complaints as needed.

[0573] Step 8:

[0574] The server provides users with a progress tracking tool. This tool allows users to check the processing status of their inquiries and complaints in real time.

[0575] Step 9:

[0576] If the emotion engine detects high levels of stress or dissatisfaction, the server automatically escalates the issue. The escalated issue is then handed over to human staff.

[0577] Step 10:

[0578] The server assigns escalated cases to the appropriate personnel through the staffing scheduling system, enabling efficient responses.

[0579] Step 11:

[0580] Users receive communication from human staff via their device, continue the conversation as needed, and receive support to resolve their problems.

[0581] (Example 2)

[0582] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] Responding to customer inquiries requires appropriate and prompt responses that take emotions into consideration. However, conventional systems fail to fully utilize emotional information, resulting in insufficient quality of service for individual customers. Furthermore, the lack of transparency in progress tracking can lead to customer dissatisfaction. In addition, there are situations where human judgment is required, and the inability to appropriately escalate these situations presents a challenge.

[0584] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0585] In this invention, the server includes means of using a device that receives inquiries from customers via communication means, means of using a device equipped with natural language processing capabilities for analyzing the content of the inquiries, and means of using a device that analyzes the user's emotional state and reveals emotional information. This makes it possible to generate personalized responses that take into account the customer's emotions, and is expected to improve customer satisfaction.

[0586] "Customer communication methods" refer to the means by which users send inquiries or information using methods such as telephone, chat, or email.

[0587] "Natural language processing functionality for analyzing inquiry content" refers to a technology that linguistically analyzes received text data and extracts meaning and intent from its content.

[0588] A "device for analyzing a user's emotional state" is a system that identifies a user's emotions from text and other inputs, and reveals their psychological state.

[0589] A "generative model" is an artificial intelligence technology that uses analyzed information to create the most appropriate countermeasures or responses.

[0590] A "progress confirmation device" is an interface or tool that allows users to check the processing status of inquiries and contracts in real time.

[0591] "Means for implementing changes in priorities" refers to a function that automatically adjusts the processing order of tasks and inquiries according to the situation, and facilitates human assistance when manual judgment is required.

[0592] This invention is a system that improves customer satisfaction by quickly and accurately handling customer inquiries and complaints. This system is based on interactions between a server, terminals, and users.

[0593] Users submit insurance inquiries and claims to their devices via communication methods such as phone, chat, or email. The device records this information as digital data and transmits it to a server in a secure manner. The device used for this process is typically an internet-connected computer or smartphone.

[0594] The server is equipped with a natural language processing engine to process the received data. This engine identifies the user's intent by analyzing the syntax and meaning of the received text. It also uses an emotion engine to analyze the user's emotions from the text, detecting psychological states such as anxiety and anger. This processing utilizes dedicated analysis software installed on the server.

[0595] After analysis, the server uses a generative AI model to generate the optimal response based on the results of natural language processing and sentiment analysis. The generated message is crafted to take into account the individual user's emotions. The generated message is delivered to the user via their device using the communication method of the user's choice.

[0596] Furthermore, the server provides users with a tool to check the progress of their queries. This tool is implemented as a web interface or mobile app, allowing users to check the progress in real time.

[0597] As a concrete example, when a user reports an accident via chat, the server analyzes the text and uses an emotion engine to detect that the user is feeling anxious. The generative AI model then creates a reassuring response and provides information quickly.

[0598] An example of a prompt message that can be input into the AI ​​model is, "Analyze the user's inquiry and sentiment, and generate the most appropriate automated response message."

[0599] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0600] Step 1:

[0601] Users submit insurance inquiries and claims using their devices. In this process, users send messages via phone, chat, or email. Input is the text message entered by the user. Output is the digital data received by the device.

[0602] Step 2:

[0603] The terminal records received messages as digital data and sends them to the server using a secure method, such as the HTTPS protocol. The input is the user's digital message received by the terminal, and the output is the data sent to the server.

[0604] Step 3:

[0605] The server uses a natural language processing engine to analyze incoming data. Specifically, it extracts keywords from the text and analyzes the context to understand the user's intent. The input is text data sent from the terminal, and the output is the analyzed intent and keywords.

[0606] Step 4:

[0607] The server uses an emotion engine to analyze emotions from user messages. Specifically, it detects emotional nuances from the user's writing style and expressions, and identifies states such as anxiety or anger. The input is text data, and the output is the emotion analysis result.

[0608] Step 5:

[0609] The server uses a generative AI model to generate optimal responses based on the results of natural language processing and sentiment analysis. Specifically, it automatically generates appropriate responses according to the identified intentions and emotions. The input is the result of the intention and emotion analysis, and the output is the generated response message.

[0610] Step 6:

[0611] The terminal receives generated messages from the server and delivers them to the user via a communication method selected by the user (e.g., chat or email). The input is the corresponding message sent from the server, and the output is the message displayed to the user.

[0612] Step 7:

[0613] The server provides users with processing progress through a progress tracking tool. This tool is accessible via a web interface or application and delivers real-time information to users. The input is the processing status of the query, and the output is the progress status displayed in the tool.

[0614] (Application Example 2)

[0615] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0616] Responding appropriately and promptly to customer inquiries and complaints is extremely important for businesses. However, traditional methods often fail to adequately consider the customer's emotional state and provide uniform responses, limiting the improvement of customer satisfaction. Furthermore, while human interaction is necessary, especially with emotionally sensitive customers, delays in making such judgments can lead to increased customer dissatisfaction.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0618] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine and an emotion analysis engine for analyzing the content of the inquiry and the customer's emotional state, and means for using an artificial intelligence model that generates an optimal response that takes the customer's emotional state into consideration based on the analysis results. This makes it possible to provide an optimal response that takes the customer's emotions into consideration, thereby improving customer satisfaction.

[0619] "Customer" refers to a consumer or legal entity that uses a company's services or products.

[0620] "Communication methods" refer to the ways in which customers interact with a company, such as telephone, email, and chat.

[0621] "Inquiry" refers to the act of a customer communicating their questions or problems to a company, or the content of such communication.

[0622] A "natural language processing engine" refers to a system that uses technology to analyze natural human language using machines.

[0623] An "emotion analysis engine" refers to a system that uses technology to extract emotional states from text data.

[0624] An "artificial intelligence model" refers to an algorithm or program that learns from large amounts of data and presents the optimal solution to a specific problem.

[0625] "Countermeasures" refer to appropriate solutions or methods of responding to inquiries.

[0626] A "progress tracking tool" refers to an interface or system that allows customers to check the processing status of their inquiries or contracts.

[0627] "Escalation" refers to the process of transferring a problem to a superior or a specialized team to receive a more expert response.

[0628] The system that realizes this invention includes a process of receiving customer inquiries, analyzing their emotions, and providing the most appropriate response. The server is central to the system and performs its functions in the following steps.

[0629] Users make inquiries using devices such as smartphones, via communication methods such as phone, chat, or email. This information is converted into a digital format by the device and sent to the server. The server analyzes the text using a natural language processing engine such as Google Cloud Natural Language API and understands the user's emotional state using an emotion engine such as Amazon Comprehend.

[0630] Based on the analysis results, the server uses artificial intelligence models such as Azure Machine Learning to generate optimal responses that take customer emotions into consideration. These responses are sent from the server to the terminal in real time for the user to receive. The system also provides a progress tracking tool that allows users to check the processing status of their inquiries in real time.

[0631] For example, if a user submits an inquiry such as "My recent transaction hasn't been approved," a natural language processing engine analyzes the content, and a sentiment analysis engine detects frustration. A generative AI model is then used to generate a quick solution, sending the user a message such as, "We'll investigate immediately. Please wait while we find the cause of the problem and take the best possible action." An example of a prompt to input into the generative AI model would be, "Please provide an appropriate response message for a user who is upset about a transaction error."

[0632] This system enables flexible and personalized responses tailored to each customer's individual emotional state.

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] Users use their devices to make inquiries via communication methods such as phone, chat, or email. The input is text data of the inquiry. This text data is converted into a digital format within the device and sent to the server as output.

[0636] Step 2:

[0637] The server receives the query text, which is then analyzed by a natural language processing engine. This process takes the query text as input and, through analysis, extracts output that reveals the user's intent and the information they are seeking. Specifically, the Google Cloud Natural Language API is used to perform the text analysis.

[0638] Step 3:

[0639] The server uses an emotion analysis engine to analyze the emotional state from the previously generated text data. In this step, the already analyzed text data is used as input, and data indicating the emotional state is output based on it. Amazon Comprehend is used to generate emotion tags such as anger, anxiety, and satisfaction.

[0640] Step 4:

[0641] The server utilizes an artificial intelligence model to generate optimal responses by considering emotional state data and intentions. The input is user intention and emotional data, and the output is customized messages and procedures for customer interaction. Azure Machine Learning enables this process.

[0642] Step 5:

[0643] The server sends the generated countermeasures to the terminal and provides real-time notification to the user. The input for this step is the generated countermeasures, and the output is information that displays the countermeasures on the user's terminal.

[0644] Step 6:

[0645] The server uses a progress tracking tool to allow users to check the processing status of their queries in real time. In this step, query progress data is used as input and displayed to the user as output. Often, this is visualized in a web interface or application.

[0646] These steps, when coordinated, enable prompt and emotionally sensitive responses to customers.

[0647] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0648] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0649] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0653] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0654] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0655] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0656] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0657] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0658] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0659] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0660] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0661] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0662] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0663] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] This invention is a system for automating customer service processes in the field of non-life insurance. The system functions optimally through the interaction of servers, terminals, and users.

[0665] First, the user makes an inquiry via a communication method such as phone, chat, or email. The terminal receives this inquiry and sends it to the server in digital format. This receiving process supports a variety of communication channels and is designed to enhance customer convenience.

[0666] Next, the server passes the received query to the natural language processing engine. The natural language processing engine analyzes the user's intent and concerns, and based on this, determines the need for a response. The server inputs the analysis results into an artificial intelligence model, which generates the optimal response. The artificial intelligence model utilizes past response cases and customer data to make more accurate and faster decisions.

[0667] Subsequently, the server provides the generated countermeasures to the user via the terminal. At this time, the server notifies the user of the appropriate information using the selected communication method. This enables smooth, real-time customer support.

[0668] Furthermore, the server provides a progress tracking tool so that customers can check the progress of their contracts and complaints. Through this tool, users can check the progress of their inquiries and complaints in real time.

[0669] Furthermore, if the server recognizes a case that cannot be handled automatically by AI, it will escalate it to human staff. This escalation process ensures that inquiries and complaints requiring specialized attention can be addressed promptly.

[0670] As a concrete example, if a customer makes an insurance claim regarding a malfunctioning home appliance via chat, the server analyzes the content and an AI model proposes a solution such as arranging for a repair company. This allows the customer to proceed with the repair process quickly, improving customer satisfaction.

[0671] This system automates customer service processes in the property and casualty insurance industry, significantly improving processing efficiency and accuracy.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] Users use their devices to send inquiries or claims regarding property insurance via phone, chat, or email.

[0675] Step 2:

[0676] The terminal formats the received inquiry data, converts it to the appropriate format, and then sends it to the server.

[0677] Step 3:

[0678] The server passes the received query to a natural language processing engine, which analyzes the user's intent and content. This analysis clarifies the purpose of the query and the information being sought.

[0679] Step 4:

[0680] Based on the analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. In doing so, it refers to past similar inquiries and complaint history to derive the optimal solution.

[0681] Step 5:

[0682] The server provides the generated countermeasures through the communication method used by the user. Specifically, this may involve sending text messages via chat or sending replies via email.

[0683] Step 6:

[0684] Users receive responses from the server via their device and review the content. They can ask additional questions or make complaints as needed.

[0685] Step 7:

[0686] The server provides a progress tracking tool to help users check the status of their inquiries and complaints in real time.

[0687] Step 8:

[0688] If an automated response by AI is difficult, the server will escalate the issue to human staff based on pre-configured criteria.

[0689] Step 9:

[0690] The server assigns escalated cases to the appropriate staff using the personnel scheduling system.

[0691] Step 10:

[0692] Users are notified via their device that an escalation has taken place and can continue to communicate directly with human staff if necessary.

[0693] (Example 1)

[0694] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] In the customer service process, responding to diverse communication methods in real time and quickly and accurately analyzing inquiries is crucial for increasing customer satisfaction. Providing prompt solutions, monitoring progress, and escalating issues to human staff as needed are also essential. Current systems struggle to efficiently achieve these goals, and therefore, a solution is needed.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0697] In this invention, the server includes means for receiving inquiries from customers via various communication methods in real time and converting them into a digital format; means for passing the inquiries to a natural language processing engine to analyze the content and extract the type and urgency of the claim; and means for inputting the analysis results into an artificial intelligence model to generate the optimal response based on past cases and customer data. This enables customers to receive a quick and appropriate response.

[0698] "Diverse communication methods" refers to multiple ways for customers to make inquiries, such as phone calls, chats, and emails, meaning that customers can choose the method that is most convenient for them.

[0699] "Receiving in real time" means that when a customer inquiry is received, it is immediately processed as digital data.

[0700] "Converting to digital format" refers to converting queries in different formats into unified electronic data, making them analyzable by the system.

[0701] A "natural language processing engine" refers to software that has the ability to analyze written or spoken human language and understand its meaning.

[0702] "Extracting the type and urgency of complaints" refers to determining the nature and priority of an inquiry based on its content, and then deciding on the next steps accordingly.

[0703] An "artificial intelligence model" refers to a trained program that references past data and case studies to derive the optimal solution based on the given information.

[0704] "Escalating to human staff" refers to the process of identifying cases that automated systems cannot handle and directing intervention by human experts.

[0705] The "progress tracking function" refers to a system feature that allows customers to check the current status of their inquiries or contracts in real time.

[0706] This invention is a system that automates customer service processes by utilizing various communication methods, and its implementation is possible through the interaction of servers, terminals, and users.

[0707] First, users can initiate an inquiry via phone, chat, or email. For example, a user can report an insurance claim using a smartphone chat app. These diverse communication methods allow users to make inquiries in the way that is most convenient for them.

[0708] The terminal converts received inquiries into a digital format and sends them to the server. This process is performed in real time, enabling a rapid response. The server receives this data and analyzes it using a natural language processing engine. The analysis extracts information such as the content of the inquiry, its urgency, and the type of complaint.

[0709] Next, the server inputs these analysis results into a generating AI model. This model refers to past datasets and generates the optimal course of action. For example, in the case of an insurance claim regarding a broken home appliance, the model can suggest arranging for a repair company. This kind of automation reduces response time and leads to improved customer satisfaction.

[0710] The generated countermeasures are notified to the user in real time via the device. For example, the user receives a push notification on their smartphone and can quickly take the next steps based on it.

[0711] Furthermore, the server includes a progress tracking function, allowing users to check the progress of their inquiries and complaints in real time. This enables customers to always know what stage their case is at.

[0712] If the AI ​​cannot handle the issue, the server uses a prompt message to escalate it to a human staff member. An example of a prompt message might be, "We were unable to arrange repairs for your appliance. Specialized staff assistance is required." This process allows for quick handling of cases requiring more specialized assistance.

[0713] In this way, this system achieves efficiency and automation of customer service processes through the coordination of servers, terminals, and users.

[0714] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0715] Step 1:

[0716] Users initiate inquiries to the system via phone, chat, or email, using devices such as smartphones or computers. The data entered is in text or voice format.

[0717] Step 2:

[0718] The terminal converts inquiries received from the user into a digital format. This process uses speech recognition and text analysis technologies to convert input data into standardized electronic data. The converted data is then sent to the server.

[0719] Step 3:

[0720] The server passes the digital data received from the terminal to the natural language processing engine. The engine analyzes the input data and extracts the claim type, urgency, and relevant details. The information extracted through data processing is used in the next step.

[0721] Step 4:

[0722] The server inputs the results analyzed by the natural language processing engine into an AI model. The AI ​​model refers to past datasets and generates the optimal countermeasures. Here, data calculations are used to output quick and accurate countermeasures.

[0723] Step 5:

[0724] The device notifies the user in real time of the countermeasures generated by the server. The user receives this information and can proceed to the next step. Output includes smartphone push notifications and email notifications.

[0725] Step 6:

[0726] The server displays the progress of user inquiries and complaints in real time through its progress tracking function. It outputs the data in a formatted format so that users can check the status of their own cases.

[0727] Step 7:

[0728] If the server cannot handle the issue automatically, it generates a prompt message and escalates it to human staff. When generating the prompt message, an AI model provides the necessary information to the specialist staff. This process ensures that advanced responses are provided quickly.

[0729] (Application Example 1)

[0730] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] In the current climate, where there is a demand for a system that can provide prompt and accurate responses to security-related inquiries, traditional methods make it difficult to perform advanced analysis, assess urgency, and escalate appropriately, resulting in a decline in customer satisfaction.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0733] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine to analyze the content of the inquiries, and means for using an artificial intelligence model to generate optimal countermeasures based on the analysis results. This enables real-time analysis of inquiries, prompt and accurate presentation of countermeasures, and automatic escalation to specialist staff as needed.

[0734] "Communication methods" refer to methods such as telephone, chat, and email that customers use to make inquiries to the server.

[0735] A "natural language processing engine" is software that includes technology for analyzing customer inquiries and understanding their meaning.

[0736] An "artificial intelligence model" is a program that generates the optimal course of action based on past datasets.

[0737] "Escalation" is a process in which the system automatically makes a decision and, if necessary, forwards the inquiry to a specialist.

[0738] A "historical dataset" is a collection of data that includes the history of past inquiries and responses, which is used by the system to learn and make more accurate decisions.

[0739] "Cloud computing technology" is a technology that provides computing resources via the internet and performs data storage and processing online.

[0740] The server uses communication methods and a natural language processing engine to receive customer inquiries and analyze their content. Specifically, when a customer makes an inquiry via phone, chat, or email, the information is first sent to the server through the terminal. The server analyzes this information using natural language processing engines such as SpaCy or NLTK to understand the customer's intent and urgency.

[0741] Based on the analysis results, the server generates the optimal solution using an artificial intelligence model based on TensorFlow and provides it to the customer. If the generated solution is complex or difficult for the system to process, the escalation function will transfer it to specialist staff. This escalation is carried out quickly using cloud computing technology.

[0742] Furthermore, the server provides a progress tracking tool via Firebase, allowing customers to check the status of their inquiries in real time. This system enables customers to receive quick and accurate support for security-related issues, thereby improving customer satisfaction.

[0743] For example, if a user reports, "I've been seeing a lot of suspicious people around my house lately," the server will analyze the report and suggest countermeasures such as "increasing local patrols." It also has a process in place to request assistance from security experts if necessary. This process utilizes prompt messages.

[0744] An example of a prompt message is: "A new security incident has been reported. Based on the user's message, assess the urgency and generate the most appropriate response."

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] Users submit inquiries via phone, chat, or email. The inquiry content is converted into digital data by the device and sent to the server. The input is the user's inquiry, and the output is the digital data sent to the server.

[0748] Step 2:

[0749] The server sends the received digital data to a natural language processing engine (e.g., SpaCy). Here, the query is analyzed, and the user's intent and urgency are extracted. The input is digital data, and the output is the analyzed query intent and urgency data. Specifically, keyword extraction and intent tagging are performed.

[0750] Step 3:

[0751] The server inputs the analysis results into an artificial intelligence model (using TensorFlow) to generate the optimal solution. This model makes decisions by referring to past response datasets. The input is the analysis results data, and the output is the optimal solution. Specifically, the proposals are scored and their priorities are evaluated.

[0752] Step 4:

[0753] The server sends the generated solution to the terminal and provides it to the user. Real-time feedback is provided via the user's chosen communication method (chat, email, etc.). The input is the optimal solution, and the output is the feedback provided.

[0754] Step 5:

[0755] When the server recognizes an inquiry as difficult to handle, it uses cloud computing technology to automatically escalate it and hand it over to specialist staff. The input is the data of the complex inquiry, and the output is the escalated inquiry and necessary explanations. Specifically, task lists are generated and notifications are sent to the responsible personnel.

[0756] Step 6:

[0757] The server provides a progress tracking tool via Firebase, allowing users to check the progress of their inquiries in real time. The input is the query status data, and the output is the update information displayed to the user. Specifically, this includes updating the user interface and issuing notifications.

[0758] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0759] This invention is a system that automates the customer service process in non-life insurance and further recognizes user emotions. This system functions effectively through the interaction of a server, terminal, and user.

[0760] Users use their device to submit insurance inquiries and claims via phone, chat, or email. The device receives these inquiries and records the content when it sends them to the server in digital format.

[0761] The server passes the received query to the natural language processing engine and the emotion engine. At this stage, the natural language processing engine analyzes the text and extracts the user's intent and the information they are looking for. Simultaneously, the emotion engine analyzes the user's expressions to determine their emotions and reveal their emotional state. For example, if anger or anxiety is detected, the emotion engine provides that information.

[0762] Based on these analysis results, the server uses an artificial intelligence model to generate appropriate countermeasures. By incorporating user emotional information, more personalized responses become possible. For example, a message is generated for an angry customer, aiming for a quick and considerate response.

[0763] Subsequently, the generated countermeasures are provided via the user's chosen communication method (chat, email, etc.) through the device. This allows the user to receive a response in real time.

[0764] Furthermore, the server provides users with a progress tracking tool. This tool allows users to check the processing status of inquiries and complaints in real time, supporting their peace of mind.

[0765] Based on the analysis results of the emotion engine, the system incorporates a function that automatically escalates issues if the AI ​​cannot handle them. In particular, if the emotion engine detects high stress or dissatisfaction, escalation to human staff is facilitated as a countermeasure.

[0766] As a concrete example, suppose a user files a claim for damages due to an accident via chat, and the emotion engine detects the customer's anxiety. In this case, the server may generate and provide countermeasures, including more detailed information and a swift processing schedule, to reassure the user.

[0767] This system improves the efficiency and accuracy of customer service, while also enabling flexible responses that respond to user emotions, thereby increasing customer satisfaction.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] Users can use their devices to send inquiries or complaints regarding property insurance via phone, chat, or email.

[0771] Step 2:

[0772] The terminal converts the received inquiry into the appropriate digital format and sends it to the server.

[0773] Step 3:

[0774] The server inputs the received query into a natural language processing engine, which then analyzes the text. This analysis identifies the user's intent and the information they are requesting.

[0775] Step 4:

[0776] Simultaneously, the server passes queries to the emotion engine, which analyzes the user's emotional state. For example, it determines whether the user is expressing stress or dissatisfaction.

[0777] Step 5:

[0778] The server integrates the analysis results from its natural language processing engine and emotion engine, inputting them into an artificial intelligence model to generate appropriate responses. This includes generating personalized messages based on the user's emotions.

[0779] Step 6:

[0780] The server provides the generated solution through the communication method used by the user. In this case, it will be sent as a text message if using chat, or as an email if using email.

[0781] Step 7:

[0782] The user receives a response from the server via their device and reviews the response. They can then pursue further inquiries or complaints as needed.

[0783] Step 8:

[0784] The server provides users with a progress tracking tool. This tool allows users to check the processing status of their inquiries and complaints in real time.

[0785] Step 9:

[0786] If the emotion engine detects high levels of stress or dissatisfaction, the server automatically escalates the issue. The escalated issue is then handed over to human staff.

[0787] Step 10:

[0788] The server assigns escalated cases to the appropriate personnel through the staffing scheduling system, enabling efficient responses.

[0789] Step 11:

[0790] Users receive communication from human staff via their device, continue the conversation as needed, and receive support to resolve their problems.

[0791] (Example 2)

[0792] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0793] Responding to customer inquiries requires appropriate and prompt responses that take emotions into consideration. However, conventional systems fail to fully utilize emotional information, resulting in insufficient quality of service for individual customers. Furthermore, the lack of transparency in progress tracking can lead to customer dissatisfaction. In addition, there are situations where human judgment is required, and the inability to appropriately escalate these situations presents a challenge.

[0794] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0795] In this invention, the server includes means of using a device that receives inquiries from customers via communication means, means of using a device equipped with natural language processing capabilities for analyzing the content of the inquiries, and means of using a device that analyzes the user's emotional state and reveals emotional information. This makes it possible to generate personalized responses that take into account the customer's emotions, and is expected to improve customer satisfaction.

[0796] "Customer communication methods" refer to the means by which users send inquiries or information using methods such as telephone, chat, or email.

[0797] "Natural language processing functionality for analyzing inquiry content" refers to a technology that linguistically analyzes received text data and extracts meaning and intent from its content.

[0798] A "device for analyzing a user's emotional state" is a system that identifies a user's emotions from text and other inputs, and reveals their psychological state.

[0799] A "generative model" is an artificial intelligence technology that uses analyzed information to create the most appropriate countermeasures or responses.

[0800] A "progress confirmation device" is an interface or tool that allows users to check the processing status of inquiries and contracts in real time.

[0801] "Means for implementing changes in priorities" refers to a function that automatically adjusts the processing order of tasks and inquiries according to the situation, and facilitates human assistance when manual judgment is required.

[0802] This invention is a system that improves customer satisfaction by quickly and accurately handling customer inquiries and complaints. This system is based on interactions between a server, terminals, and users.

[0803] Users submit insurance inquiries and claims to their devices via communication methods such as phone, chat, or email. The device records this information as digital data and transmits it to a server in a secure manner. The device used for this process is typically an internet-connected computer or smartphone.

[0804] The server is equipped with a natural language processing engine to process the received data. This engine identifies the user's intent by analyzing the syntax and meaning of the received text. It also uses an emotion engine to analyze the user's emotions from the text, detecting psychological states such as anxiety and anger. This processing utilizes dedicated analysis software installed on the server.

[0805] After analysis, the server uses a generative AI model to generate the optimal response based on the results of natural language processing and sentiment analysis. The generated message is crafted to take into account the individual user's emotions. The generated message is delivered to the user via their device using the communication method of the user's choice.

[0806] Furthermore, the server provides users with a tool to check the progress of their queries. This tool is implemented as a web interface or mobile app, allowing users to check the progress in real time.

[0807] As a concrete example, when a user reports an accident via chat, the server analyzes the text and uses an emotion engine to detect that the user is feeling anxious. The generative AI model then creates a reassuring response and provides information quickly.

[0808] An example of a prompt message that can be input into the AI ​​model is, "Analyze the user's inquiry and sentiment, and generate the most appropriate automated response message."

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] Users submit insurance inquiries and claims using their devices. In this process, users send messages via phone, chat, or email. Input is the text message entered by the user. Output is the digital data received by the device.

[0812] Step 2:

[0813] The terminal records received messages as digital data and sends them to the server using a secure method, such as the HTTPS protocol. The input is the user's digital message received by the terminal, and the output is the data sent to the server.

[0814] Step 3:

[0815] The server uses a natural language processing engine to analyze incoming data. Specifically, it extracts keywords from the text and analyzes the context to understand the user's intent. The input is text data sent from the terminal, and the output is the analyzed intent and keywords.

[0816] Step 4:

[0817] The server uses an emotion engine to analyze emotions from user messages. Specifically, it detects emotional nuances from the user's writing style and expressions, and identifies states such as anxiety or anger. The input is text data, and the output is the emotion analysis result.

[0818] Step 5:

[0819] The server uses a generative AI model to generate optimal responses based on the results of natural language processing and sentiment analysis. Specifically, it automatically generates appropriate responses according to the identified intentions and emotions. The input is the result of the intention and emotion analysis, and the output is the generated response message.

[0820] Step 6:

[0821] The terminal receives generated messages from the server and delivers them to the user via a communication method selected by the user (e.g., chat or email). The input is the corresponding message sent from the server, and the output is the message displayed to the user.

[0822] Step 7:

[0823] The server provides users with processing progress through a progress tracking tool. This tool is accessible via a web interface or application and delivers real-time information to users. The input is the processing status of the query, and the output is the progress status displayed in the tool.

[0824] (Application Example 2)

[0825] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0826] Responding appropriately and promptly to customer inquiries and complaints is extremely important for businesses. However, traditional methods often fail to adequately consider the customer's emotional state and provide uniform responses, limiting the improvement of customer satisfaction. Furthermore, while human interaction is necessary, especially with emotionally sensitive customers, delays in making such judgments can lead to increased customer dissatisfaction.

[0827] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0828] In this invention, the server includes means for receiving inquiries from customers via communication means, means for using a natural language processing engine and an emotion analysis engine for analyzing the content of the inquiry and the customer's emotional state, and means for using an artificial intelligence model that generates an optimal response that takes the customer's emotional state into consideration based on the analysis results. This makes it possible to provide an optimal response that takes the customer's emotions into consideration, thereby improving customer satisfaction.

[0829] "Customer" refers to a consumer or legal entity that uses a company's services or products.

[0830] "Communication methods" refer to the ways in which customers interact with a company, such as telephone, email, and chat.

[0831] "Inquiry" refers to the act of a customer communicating their questions or problems to a company, or the content of such communication.

[0832] A "natural language processing engine" refers to a system that uses technology to analyze natural human language using machines.

[0833] An "emotion analysis engine" refers to a system that uses technology to extract emotional states from text data.

[0834] An "artificial intelligence model" refers to an algorithm or program that learns from large amounts of data and presents the optimal solution to a specific problem.

[0835] "Countermeasures" refer to appropriate solutions or methods of responding to inquiries.

[0836] A "progress tracking tool" refers to an interface or system that allows customers to check the processing status of their inquiries or contracts.

[0837] "Escalation" refers to the process of transferring a problem to a superior or a specialized team to receive a more expert response.

[0838] The system that realizes this invention includes a process of receiving customer inquiries, analyzing their emotions, and providing the most appropriate response. The server is central to the system and performs its functions in the following steps.

[0839] Users make inquiries using devices such as smartphones, via communication methods such as phone, chat, or email. This information is converted into a digital format by the device and sent to the server. The server analyzes the text using a natural language processing engine such as Google Cloud Natural Language API and understands the user's emotional state using an emotion engine such as Amazon Comprehend.

[0840] Based on the analysis results, the server uses artificial intelligence models such as Azure Machine Learning to generate optimal responses that take customer emotions into consideration. These responses are sent from the server to the terminal in real time for the user to receive. The system also provides a progress tracking tool that allows users to check the processing status of their inquiries in real time.

[0841] For example, if a user submits an inquiry such as "My recent transaction hasn't been approved," a natural language processing engine analyzes the content, and a sentiment analysis engine detects frustration. A generative AI model is then used to generate a quick solution, sending the user a message such as, "We'll investigate immediately. Please wait while we find the cause of the problem and take the best possible action." An example of a prompt to input into the generative AI model would be, "Please provide an appropriate response message for a user who is upset about a transaction error."

[0842] This system enables flexible and personalized responses tailored to each customer's individual emotional state.

[0843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0844] Step 1:

[0845] Users use their devices to make inquiries via communication methods such as phone, chat, or email. The input is text data of the inquiry. This text data is converted into a digital format within the device and sent to the server as output.

[0846] Step 2:

[0847] The server receives the query text, which is then analyzed by a natural language processing engine. This process takes the query text as input and, through analysis, extracts output that reveals the user's intent and the information they are seeking. Specifically, the Google Cloud Natural Language API is used to perform the text analysis.

[0848] Step 3:

[0849] The server uses an emotion analysis engine to analyze the emotional state from the previously generated text data. In this step, the already analyzed text data is used as input, and data indicating the emotional state is output based on it. Amazon Comprehend is used to generate emotion tags such as anger, anxiety, and satisfaction.

[0850] Step 4:

[0851] The server utilizes an artificial intelligence model to generate optimal responses by considering emotional state data and intentions. The input is user intention and emotional data, and the output is customized messages and procedures for customer interaction. Azure Machine Learning enables this process.

[0852] Step 5:

[0853] The server sends the generated countermeasures to the terminal and provides real-time notification to the user. The input for this step is the generated countermeasures, and the output is information that displays the countermeasures on the user's terminal.

[0854] Step 6:

[0855] The server uses a progress tracking tool to allow users to check the processing status of their queries in real time. In this step, query progress data is used as input and displayed to the user as output. Often, this is visualized in a web interface or application.

[0856] These steps, when coordinated, enable prompt and emotionally sensitive responses to customers.

[0857] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0858] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0859] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0860] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0861] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0862] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0863] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0864] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0865] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0866] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0867] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0868] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0869] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0870] 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.

[0871] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0872] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0873] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0874] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0875] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0876] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0877] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] A means of receiving inquiries from customers via communication methods,

[0881] A means using a natural language processing engine to analyze the content of the aforementioned inquiry,

[0882] A means using an artificial intelligence model that generates the optimal countermeasure based on the aforementioned analysis results,

[0883] Means for providing the generated countermeasures to the customer,

[0884] A means of providing customers with a progress tracking tool to check the progress of contracts and inquiries,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, wherein an artificial intelligence model determines countermeasures by referring to past datasets.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising means for automatically performing escalation when the system requires human intervention.

[0890] "Example 1"

[0891] (Claim 1)

[0892] A means of receiving inquiries from customers via various communication methods in real time and converting them into a digital format,

[0893] A means of passing the aforementioned inquiry to a natural language processing engine to analyze its content and extract the type and urgency of the claim,

[0894] A means for inputting the aforementioned analysis results into an artificial intelligence model and generating optimal countermeasures based on past cases and customer data,

[0895] A means of notifying the customer of the generated countermeasures in real time via the selected communication method,

[0896] A means of providing a progress tracking function that allows customers to check the progress of their inquiries and contracts in real time,

[0897] A means of recognizing cases that are difficult to handle automatically and escalating them to human staff,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein an artificial intelligence model refers to past datasets to determine a course of action and generates it as a prompt statement.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the system comprises means for automatically escalating inquiries concerning a specific area of ​​expertise to human staff.

[0903] "Application Example 1"

[0904] (Claim 1)

[0905] A means of receiving inquiries from customers via communication methods,

[0906] A means using a natural language processing engine to analyze the content of the aforementioned inquiry,

[0907] A means using an artificial intelligence model that generates the optimal countermeasure based on the aforementioned analysis results,

[0908] Means for providing the generated countermeasures to the customer,

[0909] A means of providing customers with a progress tracking tool to check the progress of contracts and inquiries,

[0910] A means to assess the urgency and, if necessary, escalate the issue to specialist staff.

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, in which an artificial intelligence model refers to past datasets to determine countermeasures and provides a comprehensive response to security-related inquiries.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising means for using cloud computing technology to provide appropriate information in real time, even in situations where different human interaction is required.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] A means of receiving inquiries from customers via communication means,

[0919] A means using a device equipped with natural language processing capabilities for analyzing the content of the aforementioned inquiry,

[0920] A method that uses a device to analyze the user's emotional state and reveal emotional information,

[0921] A means that uses a generative model to generate the optimal countermeasure based on the aforementioned analysis results,

[0922] A means using a device that provides the generated countermeasures to the user,

[0923] A means of providing a progress confirmation device that allows users to check the progress of contracts and inquiries,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, wherein the generative model determines countermeasures by referring to a past information set.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for automatically changing the priority when the system requires manual response.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] A means of receiving inquiries from customers via communication methods,

[0932] A means using a natural language processing engine and an emotion analysis engine to analyze the content of the aforementioned inquiry and the customer's emotional state,

[0933] A method using an artificial intelligence model that generates the optimal response considering the customer's emotional state based on the aforementioned analysis results,

[0934] A means of providing the customer with the generated countermeasures and notifying them in real time,

[0935] A means of providing progress tracking tools that allow customers to check the progress of contracts and inquiries and gain peace of mind,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, in which an artificial intelligence model refers to past datasets to determine a customized response that corresponds to the customer's emotions.

[0939] (Claim 3)

[0940] The system according to claim 1, further comprising means for automatically escalating when the emotion analysis engine detects high stress or dissatisfaction, thereby facilitating human intervention. [Explanation of symbols]

[0941] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving inquiries from customers via communication methods, A means using a natural language processing engine to analyze the content of the aforementioned inquiry, A means using an artificial intelligence model that generates the optimal countermeasure based on the aforementioned analysis results, Means for providing the generated countermeasures to the customer, A means of providing customers with a progress tracking tool to check the progress of contracts and inquiries, A system that includes this.

2. The system according to claim 1, wherein an artificial intelligence model determines countermeasures by referring to past datasets.

3. The system according to claim 1, further comprising means for automatically performing escalation when the system requires human intervention.

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