system

Abstract

Provide a system. 【Solution means】 Means for receiving input data from a user and converting it into a standardized format, Means for analyzing the user's intention using natural language processing technology, Means for dynamically generating a response based on the analysis result and notifying the user, Means for storing inquiries and claim records in a database and updating the progress in real time, Means for providing an interface through which the user can check the progress, Means for monitoring the status of the data processing environment and issuing a warning to the administrator when an abnormality is detected, Means for proposing a solution when an abnormality is detected, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional property insurance business, it has often taken time and effort to respond to customer inquiries and handle claims. As a result, a decrease in customer satisfaction and a deterioration in business efficiency have been issues. In particular, the problem is that multi-language support and personalized information provision have not been sufficiently carried out, and the diverse needs of customers cannot be met.

Means for Solving the Problems

[0005] This invention provides a system that standardizes user input data and analyzes user intent using natural language processing technology. This system is characterized by dynamically generating responses based on the analysis results and notifying the user. Furthermore, it provides an interface that allows users to check the status at any time by recording inquiries and complaints in a database and updating progress in real time. In addition, it improves customer satisfaction and enhances operational efficiency by providing multilingual support and personalized information.

[0006] "User" refers to a customer or user who uses the system to make inquiries or complaints.

[0007] "Input data" refers to information that users provide to the system via phone, chat, email, etc.

[0008] "Standardization" refers to the process of converting data received from different formats or channels into a unified format.

[0009] "Natural language processing technology" refers to the technology that enables computers to understand and interpret human language.

[0010] "Intent analysis" refers to the process of understanding the meaning of user input data and identifying its purpose.

[0011] "Response generation" refers to the process of automatically creating a reply to the user based on the analyzed intent.

[0012] "Notification" refers to the act of sending a response message generated by the system to the user.

[0013] "Recording" refers to the act of saving inquiry and complaint data to a database.

[0014] "Progress updates" refers to the process of updating the status of inquiries and complaints in real time.

[0015] "Interface" refers to the operation screen and display device for the user to interact with the system.

[0016] "Multi-language support" refers to the function of supporting input and response in multiple languages.

[0017] "Personalization" refers to the process of providing customized information based on the attributes and past behaviors of individual users.

Brief Description of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention relates to a system for automating inquiry handling and claims processing in non-life insurance. This system provides a process for receiving user input, analyzing it using natural language processing techniques, and generating and notifying the user of intent-based responses. The system primarily functions as follows:

[0040] First, users make insurance inquiries or complaints through channels such as phone, chat, or email. This input data is received by the server in real time and converted into a unified data format.

[0041] Next, the server analyzes the received text data using natural language processing techniques. This process helps understand the user's intent and needs, and based on that, identifies a processing category (e.g., new contract, claims processing, information inquiry).

[0042] The server then dynamically generates a response based on the analysis results. This response is structured according to a template and designed to contain the most relevant information and instructions for the user. The response can be multilingual, and the information is provided in the language selected by the user.

[0043] Furthermore, the server manages the progress of inquiries and complaints in real time by recording the information in a database. Users can check the status of their inquiries or complaints at any time using a dedicated interface.

[0044] As a concrete example, consider a case where a user uses chat to claim compensation for damages caused by windstorms. The server immediately analyzes the message, quickly generates a response with necessary procedural information and instructions on how to submit documents, and simultaneously records the claim in the database. The user can use a progress tracking tool to check the status of insurance payments in real time, allowing them to proceed with the process with peace of mind.

[0045] This system can improve both customer satisfaction and operational efficiency. By having the server execute automated processes, the time required for procedures is significantly reduced, and human resources can be saved.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users submit inquiries or complaints using communication channels such as phone, chat, and email.

[0049] Step 2:

[0050] To analyze the data received from users by the server, voice data is converted to text using speech recognition technology, while chat and email data are directly imported as text data.

[0051] Step 3:

[0052] The server formats the data in a standardized format and prepares it for passing to the natural language processing (NLP) module.

[0053] Step 4:

[0054] The server uses an NLP module to analyze user messages and understand their intent and requests. This allows it to identify which category the inquiry or complaint belongs to (e.g., new contract, complaint handling, information request).

[0055] Step 5:

[0056] The server selects the appropriate response template and dynamically generates a message. The message is designed to include information and next steps based on the user's intent.

[0057] Step 6:

[0058] The server generates multilingual messages based on the user's language settings, ensuring they are easily understood by the user.

[0059] Step 7:

[0060] The server sends the generated response to the user and simultaneously records the details of the complaint or inquiry in the database.

[0061] Step 8:

[0062] The server manages the progress of inquiries and complaints in a database, enabling it to provide users with real-time updates.

[0063] Step 9:

[0064] Users can use their devices to view the current status of inquiries and complaints through a progress tracking tool. This allows them to check the situation at any time and take the next steps with confidence.

[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] Conventional inquiry handling systems struggle to efficiently process diverse forms of user input, resulting in time-consuming response generation and progress tracking. Furthermore, insufficient multilingual support and personalized information provision hinder the improvement of the user experience.

[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 information from a user via a communication device and converting it into a unified information format, means for determining the user's intent using natural language processing technology, and means for adaptively constructing a response based on the analysis results and providing it to the user. This enables high-speed and accurate response generation and progress management, as well as improved user experience through multilingual support.

[0070] A "communication device" is a device that receives information from a user and transmits that information to a server.

[0071] A "unified information format" is a format in which input data received in different formats is converted into a consistent format, such as JSON.

[0072] "Natural language processing technology" is the technology that enables computers to understand, analyze, and process human language.

[0073] "Means of determining intent" refers to the process of identifying the purpose and request from the content of a user's inquiry.

[0074] "Means of constructing a response" refers to the process of creating a message based on analysis results to provide users with appropriate information and instructions.

[0075] "Providing to the user" means the act of communicating the generated response to the user.

[0076] A "storage device" is a data storage device used to store a history of inquiries and requests.

[0077] A "display device" is a visual device that provides an interface for users to check the progress of information.

[0078] A "generation module" is a software component that supports natural language processing techniques and generates flexible and natural-sounding text.

[0079] "Multilingual support" refers to the ability to process and provide information in multiple languages.

[0080] "Personalized information" refers to data that has been customized according to the user's specific needs and circumstances.

[0081] This invention is a system for automating insurance-related inquiries and claims processing. Specifically, a communication device receives data input from the user, and a server converts this data into a unified information format. The server uses natural language processing technology to accurately analyze the user's intent. In this process, a common natural language processing library (e.g., spaCy or NLTK) is used as the analysis engine to tokenize the input text and perform grammatical structure analysis.

[0082] Based on the analysis results, the server uses a generation module to adaptively generate the most appropriate response for the user. This response is created in a template format and includes multilingual support, allowing information to be provided in the user's chosen language. A multilingual framework is used to support output in English, Japanese, and other necessary languages.

[0083] Furthermore, the server records received inquiries and claims in storage and updates progress information immediately as needed. A relational database management system is used for data management, systematically storing inquiry details and status. Users can check progress in real time using a display device provided through their terminal. This display device is developed via a web browser or mobile app and provides an intuitive interface.

[0084] For example, if a user uses the chat function to file a claim for damages due to wind damage, the server immediately analyzes the message, quickly generates and provides the user with the necessary procedural information and instructions on how to submit documents, and records the claim in the database. An example of a prompt message a user might use is, "I would like to file a claim for damages due to wind damage. How should I proceed?" This system enables fast and personalized responses, allowing users to proceed with the process with confidence.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users use communication devices to input inquiry and complaint information. Input methods vary, including chat and email, and this data is sent from the device in string format. The server receives this input data and converts it in real time into a standardized information format (e.g., JSON format). This data conversion ensures that the input data is in a well-organized format that facilitates subsequent processing.

[0088] Step 2:

[0089] The server passes the converted data to a natural language processing engine. Here, the server uses natural language processing techniques to analyze the text data and determine the user's intent. For example, it breaks down the input text into tokens and identifies parts of speech to classify the type of inquiry the user intends to make (e.g., complaint handling, new contract, etc.). This analysis result is then used as the basis for the next step.

[0090] Step 3:

[0091] The server generates a response using a generative AI model based on the analysis results. Depending on the intent information received as input, it inserts appropriate information from a template to construct a user-facing response. The template includes multilingual data, and the response is formatted according to the user's selected language. The final generated response is in text format and contains precise instructions and information for the user.

[0092] Step 4:

[0093] The server sends the generated response to the user and simultaneously records the inquiry or complaint details in the database. The recorded data includes the inquiry category, user information, and response content. This stored record is used for progress management and handling subsequent inquiries. Furthermore, the progress status is updated in real time, and users can check it at any time via a dedicated display device. For example, SQL is used for database management to ensure scalability and manageability.

[0094] (Application Example 1)

[0095] 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."

[0096] The need for anomaly detection and rapid response in data processing environments is increasing. However, existing systems primarily rely on manual monitoring, leading to challenges such as the depletion of human resources and delays in response. Furthermore, the need for multilingual support and personalization necessitates means that methods to improve usability are required.

[0097] 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.

[0098] In this invention, the server includes means for receiving input data from a user and converting it into a standardized format, means for analyzing the user's intent using natural language processing technology, means for dynamically generating a response based on the analysis results and notifying the user, means for monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected, and means for proposing a solution when an anomaly is detected. This enables automated anomaly monitoring and rapid response in the data processing environment.

[0099] "User input data" refers to information provided by users, including content related to inquiries and complaints.

[0100] "Means of converting to a standardized format" refers to methods of organizing input data into a unified data format.

[0101] "Natural language processing technology" is a technology that analyzes human language and understands its intent and content.

[0102] "A means of dynamically generating a response based on analysis results and notifying the user" refers to a method of creating an appropriate response based on information obtained through analysis and informing the user.

[0103] A "data processing environment" refers to the place or system where digital data is generated, processed, and stored.

[0104] "A means of monitoring the situation and alerting administrators when an anomaly is detected" refers to a method of constantly checking the status within the system and notifying the responsible person if an anomaly occurs.

[0105] "Means of proposing solutions" refers to methods of presenting improvement plans or countermeasures in response to detected anomalies.

[0106] In an embodiment of this invention, the server first receives input data from the user. The user can send inquiries or complaints from a smartphone or computer terminal, and this data is converted into a standardized data format by the server. Next, the server uses natural language processing technology to analyze this input data and understand the user's intent.

[0107] This process utilizes software libraries such as NLTK and scikit-learn. This allows the server to generate appropriate responses to user requests and notify the user of that information. Dedicated software for anomaly detection is also used to monitor the data processing environment, and the server issues alerts to administrators as needed. Furthermore, it can suggest solutions when an anomaly is detected.

[0108] For example, if a server detects an abnormal temperature in a specific piece of physical equipment within a data center, that information is notified to the administrator in real time. Along with a warning message, suggestions for checking the cooling system and load balancing are provided.

[0109] By inputting prompts such as, "Report the load status in the data center and suggest specific actions to take if an anomaly is detected," the generated AI model can obtain more specific and reliable countermeasures. In this way, the system improves operational efficiency through automated responses while simultaneously enabling rapid response when an anomaly occurs.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server receives input data from users. This input is text data sent from smartphones or computer terminals. The received data is converted into a standardized format. This conversion includes unifying character encodings and removing unnecessary characters to facilitate processing of the data in a uniform format.

[0113] Step 2:

[0114] The server analyzes standardized data using natural language processing techniques. It tokenizes the input data and performs analysis based on grammar and semantics. This process involves tokenizing the data using the nltk library and classifying intent using scikit-learn. The input is tokenized data, and the output is class labels indicating the user's intent or request.

[0115] Step 3:

[0116] The server dynamically generates responses based on the analysis results. It is required to create appropriate responses from the analyzed intent information. By combining template responses, it generates response sentences containing personalized information. The input is the intent label, and the output is a text response notified to the user. This response is displayed in the user's chosen interface, such as a smartphone notification or the device's chat screen.

[0117] Step 4:

[0118] The server monitors the data processing environment and detects anomalies. It monitors system metrics such as CPU usage and memory consumption, and issues warnings when anomalies are detected. This involves activating sensors and monitoring systems that automatically send notifications when thresholds are exceeded. The input is real-time data of system metrics, and the output is a warning sent to the administrator when an anomaly occurs. This notification content also includes solutions proposed using generative AI models.

[0119] 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.

[0120] This invention is a system designed to improve the efficiency of handling inquiries and claims in non-life insurance, and incorporates an emotion engine. The system aims to recognize user emotions and enhance the user experience.

[0121] First, users submit inquiries or complaints via phone, chat, email, etc. This input data is received by the server and converted and parsed into text using natural language processing. This is where the emotion engine comes in. The server uses the emotion engine as part of its NLP module to identify the user's emotions from the input data. This emotion analysis allows the server to understand the user's situation, such as whether they are happy, confused, or angry.

[0122] Based on the analysis results, the server dynamically generates a response. This response is tailored to the user's emotions and provides appropriate assistance. Furthermore, the generated response is multilingual and includes personalized information as needed. By providing emotionally sensitive responses, users can have a better experience, leading to increased satisfaction.

[0123] This system can record user inquiries and complaints in a database and update their progress in real time. Users can check the current status at any time using a progress tracking tool on their device.

[0124] For example, if a user submits a complaint about damaged goods, the emotion engine recognizes this dissatisfaction. The server generates a calm and prompt response that takes care to soothe the user's anger and clearly explains the compensation process. This increases the user's confidence in the service they receive and helps resolve their dissatisfaction.

[0125] This invention thus improves customer satisfaction while simultaneously automating and streamlining business processes. Emotion-intensive response generation contributes to the creation of highly competitive information systems for future businesses.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users send questions and complaints to the system via phone, chat, email, etc.

[0129] Step 2:

[0130] The server converts received messages into text data using speech recognition and text analysis modules. This text data is then standardized into a consistent format.

[0131] Step 3:

[0132] The server uses natural language processing (NLP) techniques to analyze the content of user messages and attempt to understand what the user intends. This analysis identifies the type of inquiry or complaint.

[0133] Step 4:

[0134] The server uses an emotion engine to recognize emotions from user messages. For example, it determines whether the user is angry, confused, or happy based on the vocabulary and context of the message.

[0135] Step 5:

[0136] The server combines analysis results and sentiment recognition results to generate the most appropriate response. The response is sensitive to the user's emotions and includes information and instructions to help solve the problem. This response is generated in multiple languages, and personalized information is provided to the user as needed.

[0137] Step 6:

[0138] The server sends the generated response to the user through the selected communication channel.

[0139] Step 7:

[0140] The server records user inquiries and complaints in a database and manages their progress and response status in real time. This ensures that the latest processing status is always maintained.

[0141] Step 8:

[0142] Users can use a progress tracking tool through their device to check the status and handling of their inquiries and complaints at any time. This feature allows users to experience transparency in the process and gain a sense of security.

[0143] (Example 2)

[0144] 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".

[0145] Conventional customer service systems often lacked the ability to provide appropriate responses that considered user emotions, resulting in decreased customer satisfaction. Furthermore, insufficient multilingual support and personalization of information made it difficult to meet diverse user needs. Therefore, there is a need to develop a system that accurately analyzes user intent and emotions, and dynamically generates appropriate responses based on that analysis, thereby improving customer satisfaction.

[0146] 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.

[0147] In this invention, the server includes means for receiving input information from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for dynamically generating a response based on the analysis results and emotion analysis, and notifying the user. This makes it possible to generate a variety of responses that take the user's emotions into consideration.

[0148] A "user" refers to an individual or legal entity that makes inquiries or requests for information from the system.

[0149] "Input information" refers to the data and requests that users provide to the system.

[0150] A "standardized format" refers to a format that converts various input formats into a consistent format.

[0151] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language.

[0152] "Intention" refers to the purpose or requirements that a user has for the system.

[0153] "Sentiment analysis" refers to the process of identifying and understanding a user's emotions from input information.

[0154] "Dynamic response generation" refers to creating an appropriate response in real time based on the input information.

[0155] "To notify" refers to the act of conveying information or messages to a user.

[0156] A "storage device" refers to a physical or virtual storage medium used to record data or information over the long term.

[0157] "Continuously updating" refers to keeping information and data up-to-date on a daily basis or in real time.

[0158] "Display means" refers to devices or interfaces used to visually present information or data to users.

[0159] "Responding to linguistic diversity" refers to the ability to enable the exchange of information between various languages.

[0160] "User-specific information" refers to customized data and messages related to a particular user.

[0161] This invention provides a system that efficiently handles user inquiries and complaints, thereby improving customer satisfaction. The system is primarily composed of users, servers, and terminals.

[0162] Users submit inquiries and complaints via phone, chat, email, etc. The server receives the input information based on these submissions. The server uses API gateways and data integration tools to convert the input information into a standardized format. This standardization of data formatting facilitates subsequent processing.

[0163] The server uses natural language processing techniques to analyze the user's intent and emotions based on the received data. This process utilizes Python's NLTK and spaCy, and Hugging Face's Transformers model for emotion analysis. These technologies enable a detailed understanding of the user's purpose and emotions.

[0164] After the analysis is complete, the server dynamically generates a response based on the analysis results and sentiment analysis. For example, OpenAI's GPT-4® can be used as the generation AI model. This makes it possible to generate responses that take the user's emotions into consideration in real time.

[0165] The device notifies the user of the generated response. The response is translated as needed using Google® Cloud Translation API or similar tools for multilingual support and provided in a format that is easy for the user to understand.

[0166] For example, if a user submits a travel insurance claim, the feedback text is parsed using a prompt like this: "Input text: I have a question about my travel insurance. I had an accident while traveling and would like to confirm if it is covered.\nTask: Identify the intent and sentiment of this text and generate an appropriate response message."

[0167] For example, if a user expresses anger about a damaged product, the server generates a response such as, "We apologize for the inconvenience. We will contact you regarding the product replacement process," and notifies the user of this information via their device. This process enables a swift and appropriate response that takes the user's feelings into consideration.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users submit inquiries and complaints via phone, chat, or email. This input reaches the server in various forms, such as text messages or audio data.

[0171] Step 2:

[0172] The server converts the received input information into a standardized format. This conversion is performed via an API gateway, unifying data in different formats into a single text format. This facilitates subsequent natural language processing. The output of this process is standardized text data.

[0173] Step 3:

[0174] The server analyzes standardized text data using natural language processing techniques. Specifically, it uses Python's NLTK and spaCy to extract the user's intent and subject from the text. As a result of this analysis, the server obtains output data that allows it to understand the user's specific requests.

[0175] Step 4:

[0176] The server performs sentiment analysis on the parsed text data. Here, it uses the Hugging Face Transformers model to detect the user's emotional state. For example, it can identify whether the user is feeling anxious or angry. The output is analytical data indicating the user's emotions.

[0177] Step 5:

[0178] The server generates dynamic responses using a generative AI model based on the analysis results and sentiment analysis data. Specifically, it uses OpenAI's GPT-4 to construct appropriate responses for the user. The output of this process is a response message to be sent to the user.

[0179] Step 6:

[0180] The server makes the generated response messages multilingual. If necessary, it uses the Google Cloud Translation API to translate messages into the user's language. This multilingual translation facilitates message comprehension and makes the service accessible to users who speak different languages.

[0181] Step 7:

[0182] The device notifies the user of the translated response. This notification is delivered as a message to the user's device, where the user can check it. Email and push notifications are often used to notify the user.

[0183] Step 8:

[0184] The server records all process data, query details, and generated responses in a storage device. It also continuously updates the progress status, allowing users to review this information later. This facilitates query history management and progress tracking.

[0185] Step 9:

[0186] Users can check the progress of their inquiries and complaints in real time through the display on their device. This display is tailored to the user's needs and presented in a visually easy-to-understand interface.

[0187] (Application Example 2)

[0188] 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".

[0189] In electronic payment services, accurately analyzing user emotions and responding accordingly is essential for promptly and appropriately addressing customer inquiries and complaints. Currently, services that consider customer emotions are insufficient, and improving customer satisfaction remains a challenge.

[0190] 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.

[0191] In this invention, the server includes means for receiving data from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for generating a dynamic response that takes the user's emotions into consideration based on the analysis results and notifying the user. This enables optimal responses based on the user's emotions, and is expected to improve customer satisfaction.

[0192] "User" refers to individual customers who use electronic payment services.

[0193] "Data" refers to all information entered by users, and includes various formats such as text, audio, and images.

[0194] A "standardized format" refers to a state in which various input data has been converted into a unified format.

[0195] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0196] "Sentiment analysis" refers to the technology that identifies a user's emotions and feelings from their input data.

[0197] A "dynamic response" refers to an optimal, situation-dependent reply generated in real time.

[0198] "Notification" refers to the act of a server communicating information to a user.

[0199] "Multilingual translation technology" refers to the technology that converts text expressed in one language into multiple other languages.

[0200] A "screen" refers to an interface through which a user visually obtains information.

[0201] A "server" refers to a central computer system that processes and manages data.

[0202] "Personalized information" refers to information that is customized according to the individual user's needs and circumstances.

[0203] This invention is a system that analyzes user emotions and generates optimized responses based on those emotions, with the aim of improving the customer experience in electronic payment services. This system is built through the interaction of the user, server, and terminal.

[0204] Users input inquiries or complaints regarding electronic payments using mobile devices such as smartphones or smart glasses. The terminal then sends the input data to a server, where it is converted into a standardized format and analyzed using natural language processing (NLP) technology. This NLP utilizes Google Cloud Natural Language API and spaCy. During the analysis, a sentiment analysis engine (e.g., IBM Watson® Tone Analyzer) identifies the user's emotions. Based on this sentiment analysis, the server generates an optimized, dynamic response that takes the user's feelings into consideration. This response is then translated into the appropriate language using multilingual translation technology (e.g., Google Translation API) and communicated to the user via the terminal.

[0205] For example, if a user expresses dissatisfaction with a payment error, the voice and text data obtained through the smart glasses are analyzed on the server, and a response such as, "We apologize, we will check immediately. Please try the following steps," is generated. This response also includes personalized information to alleviate the user's anxiety and encourage appropriate action.

[0206] By using a generative AI model to design user-specific prompts, the server can respond effectively and efficiently. This is expected to improve customer satisfaction.

[0207] Example prompt for the generating AI model: "The customer is confused by a payment error. Please generate the best support message in Japanese."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The terminal receives input data from the user. This data is typically text or audio data related to inquiries or complaints. Regardless of its format, the received data is transferred to the server.

[0211] Step 2:

[0212] The server converts the received data into a standardized format. Here, natural language processing techniques are used to convert it into text data. For example, audio data is converted into text data via speech recognition software, and all data is stored and processed as unified text information.

[0213] Step 3:

[0214] The server further analyzes the standardized text data to identify the user's intent and emotions. This process utilizes the Google Cloud Natural Language API and IBM Watson Tone Analyzer to perform sentiment analysis on the input data. From the input text data, it outputs sentiment tags that match the user's emotional state.

[0215] Step 4:

[0216] The server generates a dynamic response optimized for the user's emotions based on the analysis results. Here, a customized response, set as a prompt, is generated using an appropriate generative AI model based on the sentiment analysis results. At this stage, the response may also be translated into the user's language using multilingual translation technology.

[0217] Step 5:

[0218] The server sends the generated response to the terminal, and the terminal notifies the user of that response. The user receives the displayed message and can decide on the next action. For example, a message such as "I will check immediately. See below for details." might appear on the screen.

[0219] 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.

[0220] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention relates to a system for automating inquiry handling and claims processing in non-life insurance. This system provides a process for receiving user input, analyzing it using natural language processing techniques, and generating and notifying the user of intent-based responses. The system primarily functions as follows:

[0236] First, users make insurance inquiries or complaints through channels such as phone, chat, or email. This input data is received by the server in real time and converted into a unified data format.

[0237] Next, the server analyzes the received text data using natural language processing techniques. This process helps understand the user's intent and needs, and based on that, identifies a processing category (e.g., new contract, claims processing, information inquiry).

[0238] The server then dynamically generates a response based on the analysis results. This response is structured according to a template and designed to contain the most relevant information and instructions for the user. The response can be multilingual, and the information is provided in the language selected by the user.

[0239] Furthermore, the server manages the progress of inquiries and complaints in real time by recording the information in a database. Users can check the status of their inquiries or complaints at any time using a dedicated interface.

[0240] As a concrete example, consider a case where a user uses chat to claim compensation for damages caused by windstorms. The server immediately analyzes the message, quickly generates a response with necessary procedural information and instructions on how to submit documents, and simultaneously records the claim in the database. The user can use a progress tracking tool to check the status of insurance payments in real time, allowing them to proceed with the process with peace of mind.

[0241] This system can improve both customer satisfaction and operational efficiency. By having the server execute automated processes, the time required for procedures is significantly reduced, and human resources can be saved.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users submit inquiries or complaints using communication channels such as phone, chat, and email.

[0245] Step 2:

[0246] To analyze the data received from users by the server, voice data is converted to text using speech recognition technology, while chat and email data are directly imported as text data.

[0247] Step 3:

[0248] The server formats the data in a standardized format and prepares it for passing to the natural language processing (NLP) module.

[0249] Step 4:

[0250] The server uses an NLP module to analyze user messages and understand their intent and requests. This allows it to identify which category the inquiry or complaint belongs to (e.g., new contract, complaint handling, information request).

[0251] Step 5:

[0252] The server selects the appropriate response template and dynamically generates a message. The message is designed to include information and next steps based on the user's intent.

[0253] Step 6:

[0254] The server generates multilingual messages based on the user's language settings, ensuring they are easily understood by the user.

[0255] Step 7:

[0256] The server sends the generated response to the user and simultaneously records the details of the complaint or inquiry in the database.

[0257] Step 8:

[0258] The server manages the progress of inquiries and complaints in a database, enabling it to provide users with real-time updates.

[0259] Step 9:

[0260] Users can use their devices to view the current status of inquiries and complaints through a progress tracking tool. This allows them to check the situation at any time and take the next steps with confidence.

[0261] (Example 1)

[0262] 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."

[0263] Conventional inquiry handling systems struggle to efficiently process diverse forms of user input, resulting in time-consuming response generation and progress tracking. Furthermore, insufficient multilingual support and personalized information provision hinder the improvement of the user experience.

[0264] 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.

[0265] In this invention, the server includes means for receiving information from a user via a communication device and converting it into a unified information format, means for determining the user's intent using natural language processing technology, and means for adaptively constructing a response based on the analysis results and providing it to the user. This enables high-speed and accurate response generation and progress management, as well as improved user experience through multilingual support.

[0266] A "communication device" is a device that receives information from a user and transmits that information to a server.

[0267] A "unified information format" is a format in which input data received in different formats is converted into a consistent format, such as JSON.

[0268] "Natural language processing technology" is the technology that enables computers to understand, analyze, and process human language.

[0269] "Means of determining intent" refers to the process of identifying the purpose and request from the content of a user's inquiry.

[0270] "Means of constructing a response" refers to the process of creating a message based on analysis results to provide users with appropriate information and instructions.

[0271] "Providing to the user" means the act of communicating the generated response to the user.

[0272] A "storage device" is a data storage device used to store a history of inquiries and requests.

[0273] A "display device" is a visual device that provides an interface for users to check the progress of information.

[0274] A "generation module" is a software component that supports natural language processing techniques and generates flexible and natural-sounding text.

[0275] "Multilingual support" refers to the ability to process and provide information in multiple languages.

[0276] "Personalized information" refers to data that has been customized according to the user's specific needs and circumstances.

[0277] This invention is a system for automating insurance-related inquiries and claims processing. Specifically, a communication device receives data input from the user, and a server converts this data into a unified information format. The server uses natural language processing technology to accurately analyze the user's intent. In this process, a common natural language processing library (e.g., spaCy or NLTK) is used as the analysis engine to tokenize the input text and perform grammatical structure analysis.

[0278] Based on the analysis results, the server uses a generation module to adaptively generate the most appropriate response for the user. This response is created in a template format and includes multilingual support, allowing information to be provided in the user's chosen language. A multilingual framework is used to support output in English, Japanese, and other necessary languages.

[0279] Furthermore, the server records received inquiries and claims in storage and updates progress information immediately as needed. A relational database management system is used for data management, systematically storing inquiry details and status. Users can check progress in real time using a display device provided through their terminal. This display device is developed via a web browser or mobile app and provides an intuitive interface.

[0280] For example, if a user uses the chat function to file a claim for damages due to wind damage, the server immediately analyzes the message, quickly generates and provides the user with the necessary procedural information and instructions on how to submit documents, and records the claim in the database. An example of a prompt message a user might use is, "I would like to file a claim for damages due to wind damage. How should I proceed?" This system enables fast and personalized responses, allowing users to proceed with the process with confidence.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] Users use communication devices to input inquiry and complaint information. Input methods vary, including chat and email, and this data is sent from the device in string format. The server receives this input data and converts it in real time into a standardized information format (e.g., JSON format). This data conversion ensures that the input data is in a well-organized format that facilitates subsequent processing.

[0284] Step 2:

[0285] The server passes the converted data to the natural language processing engine. Here, the server analyzes the text data using natural language processing technology to determine the user's intent. For example, by decomposing the input text into tokens and identifying the part of speech, the server classifies the type of query the user intends (e.g., claim processing, new contract, etc.). The result of this analysis is used as the basic data for the next step.

[0286] Step 3:

[0287] The server generates a response using the generated AI model based on the result of the analysis. According to the intent information received as input, appropriate information is inserted from the template to construct a response text for the user. At this time, the template contains data corresponding to multiple languages, and the response is formatted according to the language selected by the user. The finally generated response is in text form and contains accurate instructions and information for the user.

[0288] Step 4:

[0289] The server sends the generated response to the user and at the same time records the query and claim content in the database. The data to be recorded includes the query category, user information, response content, etc. This saved record is used for progress management and subsequent query handling. Also, the progress status is updated in real time, and the user can check it at any time through a dedicated display device. For example, SQL is used for database management to ensure scalability and manageability.

[0290] (Application Example 1)

[0291] 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".

[0292] The need for anomaly detection and rapid response in data processing environments is increasing. However, existing systems primarily rely on manual monitoring, leading to challenges such as the depletion of human resources and delays in response. Furthermore, the need for multilingual support and personalization necessitates means that methods to improve usability are required.

[0293] 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.

[0294] In this invention, the server includes means for receiving input data from a user and converting it into a standardized format, means for analyzing the user's intent using natural language processing technology, means for dynamically generating a response based on the analysis results and notifying the user, means for monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected, and means for proposing a solution when an anomaly is detected. This enables automated anomaly monitoring and rapid response in the data processing environment.

[0295] "User input data" refers to information provided by users, including content related to inquiries and complaints.

[0296] "Means of converting to a standardized format" refers to methods of organizing input data into a unified data format.

[0297] "Natural language processing technology" is a technology that analyzes human language and understands its intent and content.

[0298] "A means of dynamically generating a response based on analysis results and notifying the user" refers to a method of creating an appropriate response based on information obtained through analysis and informing the user.

[0299] A "data processing environment" refers to the place or system where digital data is generated, processed, and stored.

[0300] "A means of monitoring the situation and alerting administrators when an anomaly is detected" refers to a method of constantly checking the status within the system and notifying the responsible person if an anomaly occurs.

[0301] "Means of proposing solutions" refers to methods of presenting improvement plans or countermeasures in response to detected anomalies.

[0302] In an embodiment of this invention, the server first receives input data from the user. The user can send inquiries or complaints from a smartphone or computer terminal, and this data is converted into a standardized data format by the server. Next, the server uses natural language processing technology to analyze this input data and understand the user's intent.

[0303] This process utilizes software libraries such as NLTK and scikit-learn. This allows the server to generate appropriate responses to user requests and notify the user of that information. Dedicated software for anomaly detection is also used to monitor the data processing environment, and the server issues alerts to administrators as needed. Furthermore, it can suggest solutions when an anomaly is detected.

[0304] For example, if a server detects an abnormal temperature in a specific piece of physical equipment within a data center, that information is notified to the administrator in real time. Along with a warning message, suggestions for checking the cooling system and load balancing are provided.

[0305] By inputting prompts such as, "Report the load status in the data center and suggest specific actions to take if an anomaly is detected," the generated AI model can obtain more specific and reliable countermeasures. In this way, the system improves operational efficiency through automated responses while simultaneously enabling rapid response when an anomaly occurs.

[0306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0307] Step 1:

[0308] The server receives input data from the user. The input is text data transmitted from a smartphone or computer terminal. The received data is converted into a standardized format. This conversion includes unifying the character encoding and removing unnecessary characters so that the data can be easily processed in a uniform format.

[0309] Step 2:

[0310] The server analyzes the standardized data using natural language processing techniques. The input data is tokenized and analyzed based on grammar and semantics. In this process, the nltk library is used to tokenize the data, and scikit-learn is used to classify the intent. The input is the tokenized data, and the output is the class label indicating the user's intent or request.

[0311] Step 3:

[0312] The server dynamically generates a response based on the analysis result. It is required to create an appropriate response from the analyzed intent information. By combining template responses, a response text containing personalized information is generated. The input is the intent label, and the output is the text response notified to the user. This response is displayed on the interface selected by the user, such as a smartphone notification or the chat screen of the terminal.

[0313] Step 4:

[0314] The server monitors the data processing environment and performs anomaly detection. It monitors system metrics such as CPU usage and memory consumption, and issues a warning when an anomaly is recognized. This involves operating sensors and monitoring systems that automatically send notifications when a threshold is exceeded. The input is the real-time data of system metrics, and the output is the warning sent to the administrator when an anomaly occurs. The notification content also includes the solutions proposed using the generated AI model.

[0315] 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.

[0316] This invention is a system designed to improve the efficiency of handling inquiries and claims in non-life insurance, and incorporates an emotion engine. The system aims to recognize user emotions and enhance the user experience.

[0317] First, users submit inquiries or complaints via phone, chat, email, etc. This input data is received by the server and converted and parsed into text using natural language processing. This is where the emotion engine comes in. The server uses the emotion engine as part of its NLP module to identify the user's emotions from the input data. This emotion analysis allows the server to understand the user's situation, such as whether they are happy, confused, or angry.

[0318] Based on the analysis results, the server dynamically generates a response. This response is tailored to the user's emotions and provides appropriate assistance. Furthermore, the generated response is multilingual and includes personalized information as needed. By providing emotionally sensitive responses, users can have a better experience, leading to increased satisfaction.

[0319] This system can record user inquiries and complaints in a database and update their progress in real time. Users can check the current status at any time using a progress tracking tool on their device.

[0320] For example, if a user submits a complaint about damaged goods, the emotion engine recognizes this dissatisfaction. The server generates a calm and prompt response that takes care to soothe the user's anger and clearly explains the compensation process. This increases the user's confidence in the service they receive and helps resolve their dissatisfaction.

[0321] This invention thus improves customer satisfaction while simultaneously automating and streamlining business processes. Emotion-intensive response generation contributes to the creation of highly competitive information systems for future businesses.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] Users send questions and complaints to the system via phone, chat, email, etc.

[0325] Step 2:

[0326] The server converts received messages into text data using speech recognition and text analysis modules. This text data is then standardized into a consistent format.

[0327] Step 3:

[0328] The server uses natural language processing (NLP) techniques to analyze the content of user messages and attempt to understand what the user intends. This analysis identifies the type of inquiry or complaint.

[0329] Step 4:

[0330] The server uses an emotion engine to recognize emotions from user messages. For example, it determines whether the user is angry, confused, or happy based on the vocabulary and context of the message.

[0331] Step 5:

[0332] The server combines analysis results and sentiment recognition results to generate the most appropriate response. The response is sensitive to the user's emotions and includes information and instructions to help solve the problem. This response is generated in multiple languages, and personalized information is provided to the user as needed.

[0333] Step 6:

[0334] The server sends the generated response to the user through the selected communication channel.

[0335] Step 7:

[0336] The server records user inquiries and complaints in a database and manages their progress and response status in real time. This ensures that the latest processing status is always maintained.

[0337] Step 8:

[0338] Users can use a progress tracking tool through their device to check the status and handling of their inquiries and complaints at any time. This feature allows users to experience transparency in the process and gain a sense of security.

[0339] (Example 2)

[0340] 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".

[0341] Conventional customer service systems often lacked the ability to provide appropriate responses that considered user emotions, resulting in decreased customer satisfaction. Furthermore, insufficient multilingual support and personalization of information made it difficult to meet diverse user needs. Therefore, there is a need to develop a system that accurately analyzes user intent and emotions, and dynamically generates appropriate responses based on that analysis, thereby improving customer satisfaction.

[0342] 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.

[0343] In this invention, the server includes means for receiving input information from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for dynamically generating a response based on the analysis results and emotion analysis, and notifying the user. This makes it possible to generate a variety of responses that take the user's emotions into consideration.

[0344] A "user" refers to an individual or legal entity that makes inquiries or requests for information from the system.

[0345] "Input information" refers to the data and requests that users provide to the system.

[0346] A "standardized format" refers to a format that converts various input formats into a consistent format.

[0347] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language.

[0348] "Intention" refers to the purpose or requirements that a user has for the system.

[0349] "Sentiment analysis" refers to the process of identifying and understanding a user's emotions from input information.

[0350] "Dynamic response generation" refers to creating an appropriate response in real time based on the input information.

[0351] "To notify" refers to the act of conveying information or messages to a user.

[0352] A "storage device" refers to a physical or virtual storage medium used to record data or information over the long term.

[0353] "Continuously updating" refers to keeping information and data up-to-date on a daily basis or in real time.

[0354] "Display means" refers to devices or interfaces used to visually present information or data to users.

[0355] "Responding to linguistic diversity" refers to the ability to enable the exchange of information between various languages.

[0356] "User-specific information" refers to customized data and messages related to a particular user.

[0357] This invention provides a system that efficiently handles user inquiries and complaints, thereby improving customer satisfaction. The system is primarily composed of users, servers, and terminals.

[0358] Users submit inquiries and complaints via phone, chat, email, etc. The server receives the input information based on these submissions. The server uses API gateways and data integration tools to convert the input information into a standardized format. This standardization of data formatting facilitates subsequent processing.

[0359] The server uses natural language processing techniques to analyze the user's intent and emotions based on the received data. This process utilizes Python's NLTK and spaCy, and Hugging Face's Transformers model for emotion analysis. These technologies enable a detailed understanding of the user's purpose and emotions.

[0360] After the analysis is complete, the server dynamically generates a response based on the analysis results and sentiment analysis. For example, OpenAI's GPT-4 can be used as the generation AI model. This makes it possible to generate responses that take the user's emotions into consideration in real time.

[0361] The device notifies the user of the generated response. The response is translated using the Google Cloud Translation API or similar tools as needed for multilingual support and provided to the user in an easily understandable format.

[0362] For example, if a user submits a travel insurance claim, the feedback text is parsed using a prompt like this: "Input text: I have a question about my travel insurance. I had an accident while traveling and would like to confirm if it is covered.\nTask: Identify the intent and sentiment of this text and generate an appropriate response message."

[0363] For example, if a user expresses anger about a damaged product, the server generates a response such as, "We apologize for the inconvenience. We will contact you regarding the product replacement process," and notifies the user of this information via their device. This process enables a swift and appropriate response that takes the user's feelings into consideration.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users submit inquiries and complaints via phone, chat, or email. This input reaches the server in various forms, such as text messages or audio data.

[0367] Step 2:

[0368] The server converts the received input information into a standardized format. This conversion is performed via an API gateway, unifying data in different formats into a single text format. This facilitates subsequent natural language processing. The output of this process is standardized text data.

[0369] Step 3:

[0370] The server analyzes standardized text data using natural language processing techniques. Specifically, it uses Python's NLTK and spaCy to extract the user's intent and subject from the text. As a result of this analysis, the server obtains output data that allows it to understand the user's specific requests.

[0371] Step 4:

[0372] The server performs sentiment analysis on the parsed text data. Here, it uses the Hugging Face Transformers model to detect the user's emotional state. For example, it can identify whether the user is feeling anxious or angry. The output is analytical data indicating the user's emotions.

[0373] Step 5:

[0374] The server generates dynamic responses using a generative AI model based on the analysis results and sentiment analysis data. Specifically, it uses OpenAI's GPT-4 to construct appropriate responses for the user. The output of this process is a response message to be sent to the user.

[0375] Step 6:

[0376] The server makes the generated response messages multilingual. If necessary, it uses the Google Cloud Translation API to translate messages into the user's language. This multilingual translation facilitates message comprehension and makes the service accessible to users who speak different languages.

[0377] Step 7:

[0378] The device notifies the user of the translated response. This notification is delivered as a message to the user's device, where the user can check it. Email and push notifications are often used to notify the user.

[0379] Step 8:

[0380] The server records all process data, query details, and generated responses in a storage device. It also continuously updates the progress status, allowing users to review this information later. This facilitates query history management and progress tracking.

[0381] Step 9:

[0382] Users can check the progress of their inquiries and complaints in real time through the display on their device. This display is tailored to the user's needs and presented in a visually easy-to-understand interface.

[0383] (Application Example 2)

[0384] 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."

[0385] In electronic payment services, accurately analyzing user emotions and responding accordingly is essential for promptly and appropriately addressing customer inquiries and complaints. Currently, services that consider customer emotions are insufficient, and improving customer satisfaction remains a challenge.

[0386] 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.

[0387] In this invention, the server includes means for receiving data from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for generating a dynamic response that takes the user's emotions into consideration based on the analysis results and notifying the user. This enables optimal responses based on the user's emotions, and is expected to improve customer satisfaction.

[0388] "User" refers to individual customers who use electronic payment services.

[0389] "Data" refers to all information entered by users, and includes various formats such as text, audio, and images.

[0390] A "standardized format" refers to a state in which various input data has been converted into a unified format.

[0391] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0392] "Sentiment analysis" refers to the technology that identifies a user's emotions and feelings from their input data.

[0393] A "dynamic response" refers to an optimal, situation-dependent reply generated in real time.

[0394] "Notification" refers to the act of a server communicating information to a user.

[0395] "Multilingual translation technology" refers to the technology that converts text expressed in one language into multiple other languages.

[0396] A "screen" refers to an interface through which a user visually obtains information.

[0397] A "server" refers to a central computer system that processes and manages data.

[0398] "Personalized information" refers to information that is customized according to the individual user's needs and circumstances.

[0399] This invention is a system that analyzes user emotions and generates optimized responses based on those emotions, with the aim of improving the customer experience in electronic payment services. This system is built through the interaction of the user, server, and terminal.

[0400] Users input inquiries or complaints regarding electronic payments using mobile devices such as smartphones or smart glasses. The terminal then sends the input data to a server, where it is converted into a standardized format and analyzed using natural language processing (NLP) technology. This NLP utilizes Google Cloud Natural Language API and spaCy. During the analysis, a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) identifies the user's emotions. Based on this sentiment analysis, the server generates a dynamic, optimized response that takes the user's feelings into consideration. This response is then translated into the appropriate language using multilingual translation technology (e.g., Google Translation API) and communicated to the user via the terminal.

[0401] For example, if a user expresses dissatisfaction with a payment error, the voice and text data obtained through the smart glasses are analyzed on the server, and a response such as, "We apologize, we will check immediately. Please try the following steps," is generated. This response also includes personalized information to alleviate the user's anxiety and encourage appropriate action.

[0402] By using a generative AI model to design user-specific prompts, the server can respond effectively and efficiently. This is expected to improve customer satisfaction.

[0403] Example prompt for the generating AI model: "The customer is confused by a payment error. Please generate the best support message in Japanese."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The terminal receives input data from the user. This data is typically text or audio data related to inquiries or complaints. Regardless of its format, the received data is transferred to the server.

[0407] Step 2:

[0408] The server converts the received data into a standardized format. Here, natural language processing techniques are used to convert it into text data. For example, audio data is converted into text data via speech recognition software, and all data is stored and processed as unified text information.

[0409] Step 3:

[0410] The server further analyzes the standardized text data to identify the user's intent and emotions. This process utilizes the Google Cloud Natural Language API and IBM Watson Tone Analyzer to perform sentiment analysis on the input data. From the input text data, it outputs sentiment tags that match the user's emotional state.

[0411] Step 4:

[0412] The server generates a dynamic response optimized for the user's emotions based on the analysis results. Here, a customized response, set as a prompt, is generated using an appropriate generative AI model based on the sentiment analysis results. At this stage, the response may also be translated into the user's language using multilingual translation technology.

[0413] Step 5:

[0414] The server sends the generated response to the terminal, and the terminal notifies the user of that response. The user receives the displayed message and can decide on the next action. For example, a message such as "I will check immediately. See below for details." might appear on the screen.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention relates to a system for automating inquiry handling and claims processing in non-life insurance. This system provides a process for receiving user input, analyzing it using natural language processing techniques, and generating and notifying the user of intent-based responses. The system primarily functions as follows:

[0432] First, users make insurance inquiries or complaints through channels such as phone, chat, or email. This input data is received by the server in real time and converted into a unified data format.

[0433] Next, the server analyzes the received text data using natural language processing techniques. This process helps understand the user's intent and needs, and based on that, identifies a processing category (e.g., new contract, claims processing, information inquiry).

[0434] The server then dynamically generates a response based on the analysis results. This response is structured according to a template and designed to contain the most relevant information and instructions for the user. The response can be multilingual, and the information is provided in the language selected by the user.

[0435] Furthermore, the server manages the progress of inquiries and complaints in real time by recording the information in a database. Users can check the status of their inquiries or complaints at any time using a dedicated interface.

[0436] As a concrete example, consider a case where a user uses chat to claim compensation for damages caused by windstorms. The server immediately analyzes the message, quickly generates a response with necessary procedural information and instructions on how to submit documents, and simultaneously records the claim in the database. The user can use a progress tracking tool to check the status of insurance payments in real time, allowing them to proceed with the process with peace of mind.

[0437] This system can improve both customer satisfaction and operational efficiency. By having the server execute automated processes, the time required for procedures is significantly reduced, and human resources can be saved.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] Users submit inquiries or complaints using communication channels such as phone, chat, and email.

[0441] Step 2:

[0442] To analyze the data received from users by the server, voice data is converted to text using speech recognition technology, while chat and email data are directly imported as text data.

[0443] Step 3:

[0444] The server formats the data in a standardized format and prepares it for passing to the natural language processing (NLP) module.

[0445] Step 4:

[0446] The server uses an NLP module to analyze user messages and understand their intent and requests. This allows it to identify which category the inquiry or complaint belongs to (e.g., new contract, complaint handling, information request).

[0447] Step 5:

[0448] The server selects the appropriate response template and dynamically generates a message. The message is designed to include information and next steps based on the user's intent.

[0449] Step 6:

[0450] The server generates multilingual messages based on the user's language settings, ensuring they are easily understood by the user.

[0451] Step 7:

[0452] The server sends the generated response to the user and simultaneously records the details of the complaint or inquiry in the database.

[0453] Step 8:

[0454] The server manages the progress of inquiries and complaints in a database, enabling it to provide users with real-time updates.

[0455] Step 9:

[0456] Users can use their devices to view the current status of inquiries and complaints through a progress tracking tool. This allows them to check the situation at any time and take the next steps with confidence.

[0457] (Example 1)

[0458] 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."

[0459] Conventional inquiry handling systems struggle to efficiently process diverse forms of user input, resulting in time-consuming response generation and progress tracking. Furthermore, insufficient multilingual support and personalized information provision hinder the improvement of the user experience.

[0460] 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.

[0461] In this invention, the server includes means for receiving information from a user via a communication device and converting it into a unified information format, means for determining the user's intent using natural language processing technology, and means for adaptively constructing a response based on the analysis results and providing it to the user. This enables high-speed and accurate response generation and progress management, as well as improved user experience through multilingual support.

[0462] A "communication device" is a device that receives information from a user and transmits that information to a server.

[0463] A "unified information format" is a format in which input data received in different formats is converted into a consistent format, such as JSON.

[0464] "Natural language processing technology" is the technology that enables computers to understand, analyze, and process human language.

[0465] "Means of determining intent" refers to the process of identifying the purpose and request from the content of a user's inquiry.

[0466] "Means of constructing a response" refers to the process of creating a message based on analysis results to provide users with appropriate information and instructions.

[0467] "Providing to the user" means the act of communicating the generated response to the user.

[0468] A "storage device" is a data storage device used to store a history of inquiries and requests.

[0469] A "display device" is a visual device that provides an interface for users to check the progress of information.

[0470] A "generation module" is a software component that supports natural language processing techniques and generates flexible and natural-sounding text.

[0471] "Multilingual support" refers to the ability to process and provide information in multiple languages.

[0472] "Personalized information" refers to data that has been customized according to the user's specific needs and circumstances.

[0473] This invention is a system for automating insurance-related inquiries and claims processing. Specifically, a communication device receives data input from the user, and a server converts this data into a unified information format. The server uses natural language processing technology to accurately analyze the user's intent. In this process, a common natural language processing library (e.g., spaCy or NLTK) is used as the analysis engine to tokenize the input text and perform grammatical structure analysis.

[0474] Based on the analysis results, the server uses a generation module to adaptively generate the most appropriate response for the user. This response is created in a template format and includes multilingual support, allowing information to be provided in the user's chosen language. A multilingual framework is used to support output in English, Japanese, and other necessary languages.

[0475] Furthermore, the server records received inquiries and claims in storage and updates progress information immediately as needed. A relational database management system is used for data management, systematically storing inquiry details and status. Users can check progress in real time using a display device provided through their terminal. This display device is developed via a web browser or mobile app and provides an intuitive interface.

[0476] For example, if a user uses the chat function to file a claim for damages due to wind damage, the server immediately analyzes the message, quickly generates and provides the user with the necessary procedural information and instructions on how to submit documents, and records the claim in the database. An example of a prompt message a user might use is, "I would like to file a claim for damages due to wind damage. How should I proceed?" This system enables fast and personalized responses, allowing users to proceed with the process with confidence.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] Users use communication devices to input inquiry and complaint information. Input methods vary, including chat and email, and this data is sent from the device in string format. The server receives this input data and converts it in real time into a standardized information format (e.g., JSON format). This data conversion ensures that the input data is in a well-organized format that facilitates subsequent processing.

[0480] Step 2:

[0481] The server passes the converted data to a natural language processing engine. Here, the server uses natural language processing techniques to analyze the text data and determine the user's intent. For example, it breaks down the input text into tokens and identifies parts of speech to classify the type of inquiry the user intends to make (e.g., complaint handling, new contract, etc.). This analysis result is then used as the basis for the next step.

[0482] Step 3:

[0483] The server generates a response using a generative AI model based on the analysis results. Depending on the intent information received as input, it inserts appropriate information from a template to construct a user-facing response. The template includes multilingual data, and the response is formatted according to the user's selected language. The final generated response is in text format and contains precise instructions and information for the user.

[0484] Step 4:

[0485] The server sends the generated response to the user and simultaneously records the inquiry or complaint details in the database. The recorded data includes the inquiry category, user information, and response content. This stored record is used for progress management and handling subsequent inquiries. Furthermore, the progress status is updated in real time, and users can check it at any time via a dedicated display device. For example, SQL is used for database management to ensure scalability and manageability.

[0486] (Application Example 1)

[0487] 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."

[0488] The need for anomaly detection and rapid response in data processing environments is increasing. However, existing systems primarily rely on manual monitoring, leading to challenges such as the depletion of human resources and delays in response. Furthermore, the need for multilingual support and personalization necessitates means that methods to improve usability are required.

[0489] 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.

[0490] In this invention, the server includes means for receiving input data from a user and converting it into a standardized format, means for analyzing the user's intent using natural language processing technology, means for dynamically generating a response based on the analysis results and notifying the user, means for monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected, and means for proposing a solution when an anomaly is detected. This enables automated anomaly monitoring and rapid response in the data processing environment.

[0491] "User input data" refers to information provided by users, including content related to inquiries and complaints.

[0492] "Means of converting to a standardized format" refers to methods of organizing input data into a unified data format.

[0493] "Natural language processing technology" is a technology that analyzes human language and understands its intent and content.

[0494] "A means of dynamically generating a response based on analysis results and notifying the user" refers to a method of creating an appropriate response based on information obtained through analysis and informing the user.

[0495] A "data processing environment" refers to the place or system where digital data is generated, processed, and stored.

[0496] "A means of monitoring the situation and alerting administrators when an anomaly is detected" refers to a method of constantly checking the status within the system and notifying the responsible person if an anomaly occurs.

[0497] "Means of proposing solutions" refers to methods of presenting improvement plans or countermeasures in response to detected anomalies.

[0498] In an embodiment of this invention, the server first receives input data from the user. The user can send inquiries or complaints from a smartphone or computer terminal, and this data is converted into a standardized data format by the server. Next, the server uses natural language processing technology to analyze this input data and understand the user's intent.

[0499] This process utilizes software libraries such as NLTK and scikit-learn. This allows the server to generate appropriate responses to user requests and notify the user of that information. Dedicated software for anomaly detection is also used to monitor the data processing environment, and the server issues alerts to administrators as needed. Furthermore, it can suggest solutions when an anomaly is detected.

[0500] For example, if a server detects an abnormal temperature in a specific piece of physical equipment within a data center, that information is notified to the administrator in real time. Along with a warning message, suggestions for checking the cooling system and load balancing are provided.

[0501] By inputting prompts such as, "Report the load status in the data center and suggest specific actions to take if an anomaly is detected," the generated AI model can obtain more specific and reliable countermeasures. In this way, the system improves operational efficiency through automated responses while simultaneously enabling rapid response when an anomaly occurs.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server receives input data from users. This input is text data sent from smartphones or computer terminals. The received data is converted into a standardized format. This conversion includes unifying character encodings and removing unnecessary characters to facilitate processing of the data in a uniform format.

[0505] Step 2:

[0506] The server analyzes standardized data using natural language processing techniques. It tokenizes the input data and performs analysis based on grammar and semantics. This process involves tokenizing the data using the nltk library and classifying intent using scikit-learn. The input is tokenized data, and the output is class labels indicating the user's intent or request.

[0507] Step 3:

[0508] The server dynamically generates responses based on the analysis results. It is required to create appropriate responses from the analyzed intent information. By combining template responses, it generates response sentences containing personalized information. The input is the intent label, and the output is a text response notified to the user. This response is displayed in the user's chosen interface, such as a smartphone notification or the device's chat screen.

[0509] Step 4:

[0510] The server monitors the data processing environment and detects anomalies. It monitors system metrics such as CPU usage and memory consumption, and issues warnings when anomalies are detected. This involves activating sensors and monitoring systems that automatically send notifications when thresholds are exceeded. The input is real-time data of system metrics, and the output is a warning sent to the administrator when an anomaly occurs. This notification content also includes solutions proposed using generative AI models.

[0511] 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.

[0512] This invention is a system designed to improve the efficiency of handling inquiries and claims in non-life insurance, and incorporates an emotion engine. The system aims to recognize user emotions and enhance the user experience.

[0513] First, users submit inquiries or complaints via phone, chat, email, etc. This input data is received by the server and converted and parsed into text using natural language processing. This is where the emotion engine comes in. The server uses the emotion engine as part of its NLP module to identify the user's emotions from the input data. This emotion analysis allows the server to understand the user's situation, such as whether they are happy, confused, or angry.

[0514] Based on the analysis results, the server dynamically generates a response. This response is tailored to the user's emotions and provides appropriate assistance. Furthermore, the generated response is multilingual and includes personalized information as needed. By providing emotionally sensitive responses, users can have a better experience, leading to increased satisfaction.

[0515] This system can record user inquiries and complaints in a database and update their progress in real time. Users can check the current status at any time using a progress tracking tool on their device.

[0516] For example, if a user submits a complaint about damaged goods, the emotion engine recognizes this dissatisfaction. The server generates a calm and prompt response that takes care to soothe the user's anger and clearly explains the compensation process. This increases the user's confidence in the service they receive and helps resolve their dissatisfaction.

[0517] This invention thus improves customer satisfaction while simultaneously automating and streamlining business processes. Emotion-intensive response generation contributes to the creation of highly competitive information systems for future businesses.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users send questions and complaints to the system via phone, chat, email, etc.

[0521] Step 2:

[0522] The server converts received messages into text data using speech recognition and text analysis modules. This text data is then standardized into a consistent format.

[0523] Step 3:

[0524] The server uses natural language processing (NLP) techniques to analyze the content of user messages and attempt to understand what the user intends. This analysis identifies the type of inquiry or complaint.

[0525] Step 4:

[0526] The server uses an emotion engine to recognize emotions from user messages. For example, it determines whether the user is angry, confused, or happy based on the vocabulary and context of the message.

[0527] Step 5:

[0528] The server combines analysis results and sentiment recognition results to generate the most appropriate response. The response is sensitive to the user's emotions and includes information and instructions to help solve the problem. This response is generated in multiple languages, and personalized information is provided to the user as needed.

[0529] Step 6:

[0530] The server sends the generated response to the user through the selected communication channel.

[0531] Step 7:

[0532] The server records user inquiries and complaints in a database and manages their progress and response status in real time. This ensures that the latest processing status is always maintained.

[0533] Step 8:

[0534] Users can use a progress tracking tool through their device to check the status and handling of their inquiries and complaints at any time. This feature allows users to experience transparency in the process and gain a sense of security.

[0535] (Example 2)

[0536] 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."

[0537] Conventional customer service systems often lacked the ability to provide appropriate responses that considered user emotions, resulting in decreased customer satisfaction. Furthermore, insufficient multilingual support and personalization of information made it difficult to meet diverse user needs. Therefore, there is a need to develop a system that accurately analyzes user intent and emotions, and dynamically generates appropriate responses based on that analysis, thereby improving customer satisfaction.

[0538] 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.

[0539] In this invention, the server includes means for receiving input information from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for dynamically generating a response based on the analysis results and emotion analysis, and notifying the user. This makes it possible to generate a variety of responses that take the user's emotions into consideration.

[0540] A "user" refers to an individual or legal entity that makes inquiries or requests for information from the system.

[0541] "Input information" refers to the data and requests that users provide to the system.

[0542] A "standardized format" refers to a format that converts various input formats into a consistent format.

[0543] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language.

[0544] "Intention" refers to the purpose or requirements that a user has for the system.

[0545] "Sentiment analysis" refers to the process of identifying and understanding a user's emotions from input information.

[0546] "Dynamic response generation" refers to creating an appropriate response in real time based on the input information.

[0547] "To notify" refers to the act of conveying information or messages to a user.

[0548] A "storage device" refers to a physical or virtual storage medium used to record data or information over the long term.

[0549] "Continuously updating" refers to keeping information and data up-to-date on a daily basis or in real time.

[0550] "Display means" refers to devices or interfaces used to visually present information or data to users.

[0551] "Responding to linguistic diversity" refers to the ability to enable the exchange of information between various languages.

[0552] "User-specific information" refers to customized data and messages related to a particular user.

[0553] This invention provides a system that efficiently handles user inquiries and complaints, thereby improving customer satisfaction. The system is primarily composed of users, servers, and terminals.

[0554] Users submit inquiries and complaints via phone, chat, email, etc. The server receives the input information based on these submissions. The server uses API gateways and data integration tools to convert the input information into a standardized format. This standardization of data formatting facilitates subsequent processing.

[0555] The server uses natural language processing techniques to analyze the user's intent and emotions based on the received data. This process utilizes Python's NLTK and spaCy, and Hugging Face's Transformers model for emotion analysis. These technologies enable a detailed understanding of the user's purpose and emotions.

[0556] After the analysis is complete, the server dynamically generates a response based on the analysis results and sentiment analysis. For example, OpenAI's GPT-4 can be used as the generation AI model. This makes it possible to generate responses that take the user's emotions into consideration in real time.

[0557] The device notifies the user of the generated response. The response is translated using the Google Cloud Translation API or similar tools as needed for multilingual support and provided to the user in an easily understandable format.

[0558] For example, if a user submits a travel insurance claim, the feedback text is parsed using a prompt like this: "Input text: I have a question about my travel insurance. I had an accident while traveling and would like to confirm if it is covered.\nTask: Identify the intent and sentiment of this text and generate an appropriate response message."

[0559] For example, if a user expresses anger about a damaged product, the server generates a response such as, "We apologize for the inconvenience. We will contact you regarding the product replacement process," and notifies the user of this information via their device. This process enables a swift and appropriate response that takes the user's feelings into consideration.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] Users submit inquiries and complaints via phone, chat, or email. This input reaches the server in various forms, such as text messages or audio data.

[0563] Step 2:

[0564] The server converts the received input information into a standardized format. This conversion is performed via an API gateway, unifying data in different formats into a single text format. This facilitates subsequent natural language processing. The output of this process is standardized text data.

[0565] Step 3:

[0566] The server analyzes standardized text data using natural language processing techniques. Specifically, it uses Python's NLTK and spaCy to extract the user's intent and subject from the text. As a result of this analysis, the server obtains output data that allows it to understand the user's specific requests.

[0567] Step 4:

[0568] The server performs sentiment analysis on the parsed text data. Here, it uses the Hugging Face Transformers model to detect the user's emotional state. For example, it can identify whether the user is feeling anxious or angry. The output is analytical data indicating the user's emotions.

[0569] Step 5:

[0570] The server generates dynamic responses using a generative AI model based on the analysis results and sentiment analysis data. Specifically, it uses OpenAI's GPT-4 to construct appropriate responses for the user. The output of this process is a response message to be sent to the user.

[0571] Step 6:

[0572] The server makes the generated response messages multilingual. If necessary, it uses the Google Cloud Translation API to translate messages into the user's language. This multilingual translation facilitates message comprehension and makes the service accessible to users who speak different languages.

[0573] Step 7:

[0574] The device notifies the user of the translated response. This notification is delivered as a message to the user's device, where the user can check it. Email and push notifications are often used to notify the user.

[0575] Step 8:

[0576] The server records all process data, query details, and generated responses in a storage device. It also continuously updates the progress status, allowing users to review this information later. This facilitates query history management and progress tracking.

[0577] Step 9:

[0578] Users can check the progress of their inquiries and complaints in real time through the display on their device. This display is tailored to the user's needs and presented in a visually easy-to-understand interface.

[0579] (Application Example 2)

[0580] 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."

[0581] In electronic payment services, accurately analyzing user emotions and responding accordingly is essential for promptly and appropriately addressing customer inquiries and complaints. Currently, services that consider customer emotions are insufficient, and improving customer satisfaction remains a challenge.

[0582] 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.

[0583] In this invention, the server includes means for receiving data from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for generating a dynamic response that takes the user's emotions into consideration based on the analysis results and notifying the user. This enables optimal responses based on the user's emotions, and is expected to improve customer satisfaction.

[0584] "User" refers to individual customers who use electronic payment services.

[0585] "Data" refers to all information entered by users, and includes various formats such as text, audio, and images.

[0586] A "standardized format" refers to a state in which various input data has been converted into a unified format.

[0587] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0588] "Sentiment analysis" refers to the technology that identifies a user's emotions and feelings from their input data.

[0589] A "dynamic response" refers to an optimal, situation-dependent reply generated in real time.

[0590] "Notification" refers to the act of a server communicating information to a user.

[0591] "Multilingual translation technology" refers to the technology that converts text expressed in one language into multiple other languages.

[0592] A "screen" refers to an interface through which a user visually obtains information.

[0593] A "server" refers to a central computer system that processes and manages data.

[0594] "Personalized information" refers to information that is customized according to the individual user's needs and circumstances.

[0595] This invention is a system that analyzes user emotions and generates optimized responses based on those emotions, with the aim of improving the customer experience in electronic payment services. This system is built through the interaction of the user, server, and terminal.

[0596] Users input inquiries or complaints regarding electronic payments using mobile devices such as smartphones or smart glasses. The terminal then sends the input data to a server, where it is converted into a standardized format and analyzed using natural language processing (NLP) technology. This NLP utilizes Google Cloud Natural Language API and spaCy. During the analysis, a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) identifies the user's emotions. Based on this sentiment analysis, the server generates a dynamic, optimized response that takes the user's feelings into consideration. This response is then translated into the appropriate language using multilingual translation technology (e.g., Google Translation API) and communicated to the user via the terminal.

[0597] For example, if a user expresses dissatisfaction with a payment error, the voice and text data obtained through the smart glasses are analyzed on the server, and a response such as, "We apologize, we will check immediately. Please try the following steps," is generated. This response also includes personalized information to alleviate the user's anxiety and encourage appropriate action.

[0598] By using a generative AI model to design user-specific prompts, the server can respond effectively and efficiently. This is expected to improve customer satisfaction.

[0599] Example prompt for the generating AI model: "The customer is confused by a payment error. Please generate the best support message in Japanese."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The terminal receives input data from the user. This data is typically text or audio data related to inquiries or complaints. Regardless of its format, the received data is transferred to the server.

[0603] Step 2:

[0604] The server converts the received data into a standardized format. Here, natural language processing techniques are used to convert it into text data. For example, audio data is converted into text data via speech recognition software, and all data is stored and processed as unified text information.

[0605] Step 3:

[0606] The server further analyzes the standardized text data to identify the user's intent and emotions. This process utilizes the Google Cloud Natural Language API and IBM Watson Tone Analyzer to perform sentiment analysis on the input data. From the input text data, it outputs sentiment tags that match the user's emotional state.

[0607] Step 4:

[0608] The server generates a dynamic response optimized for the user's emotions based on the analysis results. Here, a customized response, set as a prompt, is generated using an appropriate generative AI model based on the sentiment analysis results. At this stage, the response may also be translated into the user's language using multilingual translation technology.

[0609] Step 5:

[0610] The server sends the generated response to the terminal, and the terminal notifies the user of that response. The user receives the displayed message and can decide on the next action. For example, a message such as "I will check immediately. See below for details." might appear on the screen.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention relates to a system for automating inquiry handling and claims processing in non-life insurance. This system provides a process for receiving user input, analyzing it using natural language processing techniques, and generating and notifying the user of intent-based responses. The system primarily functions as follows:

[0629] First, users make insurance inquiries or complaints through channels such as phone, chat, or email. This input data is received by the server in real time and converted into a unified data format.

[0630] Next, the server analyzes the received text data using natural language processing techniques. This process helps understand the user's intent and needs, and based on that, identifies a processing category (e.g., new contract, claims processing, information inquiry).

[0631] The server then dynamically generates a response based on the analysis results. This response is structured according to a template and designed to contain the most relevant information and instructions for the user. The response can be multilingual, and the information is provided in the language selected by the user.

[0632] Furthermore, the server manages the progress of inquiries and complaints in real time by recording the information in a database. Users can check the status of their inquiries or complaints at any time using a dedicated interface.

[0633] As a concrete example, consider a case where a user uses chat to claim compensation for damages caused by windstorms. The server immediately analyzes the message, quickly generates a response with necessary procedural information and instructions on how to submit documents, and simultaneously records the claim in the database. The user can use a progress tracking tool to check the status of insurance payments in real time, allowing them to proceed with the process with peace of mind.

[0634] This system can improve both customer satisfaction and operational efficiency. By having the server execute automated processes, the time required for procedures is significantly reduced, and human resources can be saved.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] Users submit inquiries or complaints using communication channels such as phone, chat, and email.

[0638] Step 2:

[0639] To analyze the data received from users by the server, voice data is converted to text using speech recognition technology, while chat and email data are directly imported as text data.

[0640] Step 3:

[0641] The server formats the data in a standardized format and prepares it for passing to the natural language processing (NLP) module.

[0642] Step 4:

[0643] The server uses an NLP module to analyze user messages and understand their intent and requests. This allows it to identify which category the inquiry or complaint belongs to (e.g., new contract, complaint handling, information request).

[0644] Step 5:

[0645] The server selects the appropriate response template and dynamically generates a message. The message is designed to include information and next steps based on the user's intent.

[0646] Step 6:

[0647] The server generates multilingual messages based on the user's language settings, ensuring they are easily understood by the user.

[0648] Step 7:

[0649] The server sends the generated response to the user and simultaneously records the details of the complaint or inquiry in the database.

[0650] Step 8:

[0651] The server manages the progress of inquiries and complaints in a database, enabling it to provide users with real-time updates.

[0652] Step 9:

[0653] Users can use their devices to view the current status of inquiries and complaints through a progress tracking tool. This allows them to check the situation at any time and take the next steps with confidence.

[0654] (Example 1)

[0655] 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".

[0656] Conventional inquiry handling systems struggle to efficiently process diverse forms of user input, resulting in time-consuming response generation and progress tracking. Furthermore, insufficient multilingual support and personalized information provision hinder the improvement of the user experience.

[0657] 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.

[0658] In this invention, the server includes means for receiving information from a user via a communication device and converting it into a unified information format, means for determining the user's intent using natural language processing technology, and means for adaptively constructing a response based on the analysis results and providing it to the user. This enables high-speed and accurate response generation and progress management, as well as improved user experience through multilingual support.

[0659] A "communication device" is a device that receives information from a user and transmits that information to a server.

[0660] A "unified information format" is a format in which input data received in different formats is converted into a consistent format, such as JSON.

[0661] "Natural language processing technology" is the technology that enables computers to understand, analyze, and process human language.

[0662] "Means of determining intent" refers to the process of identifying the purpose and request from the content of a user's inquiry.

[0663] "Means of constructing a response" refers to the process of creating a message based on analysis results to provide users with appropriate information and instructions.

[0664] "Providing to the user" means the act of communicating the generated response to the user.

[0665] A "storage device" is a data storage device used to store a history of inquiries and requests.

[0666] A "display device" is a visual device that provides an interface for users to check the progress of information.

[0667] A "generation module" is a software component that supports natural language processing techniques and generates flexible and natural-sounding text.

[0668] "Multilingual support" refers to the ability to process and provide information in multiple languages.

[0669] "Personalized information" refers to data that has been customized according to the user's specific needs and circumstances.

[0670] This invention is a system for automating insurance-related inquiries and claims processing. Specifically, a communication device receives data input from the user, and a server converts this data into a unified information format. The server uses natural language processing technology to accurately analyze the user's intent. In this process, a common natural language processing library (e.g., spaCy or NLTK) is used as the analysis engine to tokenize the input text and perform grammatical structure analysis.

[0671] Based on the analysis results, the server uses a generation module to adaptively generate the most appropriate response for the user. This response is created in a template format and includes multilingual support, allowing information to be provided in the user's chosen language. A multilingual framework is used to support output in English, Japanese, and other necessary languages.

[0672] Furthermore, the server records received inquiries and claims in storage and updates progress information immediately as needed. A relational database management system is used for data management, systematically storing inquiry details and status. Users can check progress in real time using a display device provided through their terminal. This display device is developed via a web browser or mobile app and provides an intuitive interface.

[0673] For example, if a user uses the chat function to file a claim for damages due to wind damage, the server immediately analyzes the message, quickly generates and provides the user with the necessary procedural information and instructions on how to submit documents, and records the claim in the database. An example of a prompt message a user might use is, "I would like to file a claim for damages due to wind damage. How should I proceed?" This system enables fast and personalized responses, allowing users to proceed with the process with confidence.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] Users use communication devices to input inquiry and complaint information. Input methods vary, including chat and email, and this data is sent from the device in string format. The server receives this input data and converts it in real time into a standardized information format (e.g., JSON format). This data conversion ensures that the input data is in a well-organized format that facilitates subsequent processing.

[0677] Step 2:

[0678] The server passes the converted data to a natural language processing engine. Here, the server uses natural language processing techniques to analyze the text data and determine the user's intent. For example, it breaks down the input text into tokens and identifies parts of speech to classify the type of inquiry the user intends to make (e.g., complaint handling, new contract, etc.). This analysis result is then used as the basis for the next step.

[0679] Step 3:

[0680] The server generates a response using a generative AI model based on the analysis results. Depending on the intent information received as input, it inserts appropriate information from a template to construct a user-facing response. The template includes multilingual data, and the response is formatted according to the user's selected language. The final generated response is in text format and contains precise instructions and information for the user.

[0681] Step 4:

[0682] The server sends the generated response to the user and simultaneously records the inquiry or complaint details in the database. The recorded data includes the inquiry category, user information, and response content. This stored record is used for progress management and handling subsequent inquiries. Furthermore, the progress status is updated in real time, and users can check it at any time via a dedicated display device. For example, SQL is used for database management to ensure scalability and manageability.

[0683] (Application Example 1)

[0684] 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".

[0685] The need for anomaly detection and rapid response in data processing environments is increasing. However, existing systems primarily rely on manual monitoring, leading to challenges such as the depletion of human resources and delays in response. Furthermore, the need for multilingual support and personalization necessitates means that methods to improve usability are required.

[0686] 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.

[0687] In this invention, the server includes means for receiving input data from a user and converting it into a standardized format, means for analyzing the user's intent using natural language processing technology, means for dynamically generating a response based on the analysis results and notifying the user, means for monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected, and means for proposing a solution when an anomaly is detected. This enables automated anomaly monitoring and rapid response in the data processing environment.

[0688] "User input data" refers to information provided by users, including content related to inquiries and complaints.

[0689] "Means of converting to a standardized format" refers to methods of organizing input data into a unified data format.

[0690] "Natural language processing technology" is a technology that analyzes human language and understands its intent and content.

[0691] "A means of dynamically generating a response based on analysis results and notifying the user" refers to a method of creating an appropriate response based on information obtained through analysis and informing the user.

[0692] A "data processing environment" refers to the place or system where digital data is generated, processed, and stored.

[0693] "A means of monitoring the situation and alerting administrators when an anomaly is detected" refers to a method of constantly checking the status within the system and notifying the responsible person if an anomaly occurs.

[0694] "Means of proposing solutions" refers to methods of presenting improvement plans or countermeasures in response to detected anomalies.

[0695] In an embodiment of this invention, the server first receives input data from the user. The user can send inquiries or complaints from a smartphone or computer terminal, and this data is converted into a standardized data format by the server. Next, the server uses natural language processing technology to analyze this input data and understand the user's intent.

[0696] This process utilizes software libraries such as NLTK and scikit-learn. This allows the server to generate appropriate responses to user requests and notify the user of that information. Dedicated software for anomaly detection is also used to monitor the data processing environment, and the server issues alerts to administrators as needed. Furthermore, it can suggest solutions when an anomaly is detected.

[0697] For example, if a server detects an abnormal temperature in a specific piece of physical equipment within a data center, that information is notified to the administrator in real time. Along with a warning message, suggestions for checking the cooling system and load balancing are provided.

[0698] By inputting prompts such as, "Report the load status in the data center and suggest specific actions to take if an anomaly is detected," the generated AI model can obtain more specific and reliable countermeasures. In this way, the system improves operational efficiency through automated responses while simultaneously enabling rapid response when an anomaly occurs.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The server receives input data from users. This input is text data sent from smartphones or computer terminals. The received data is converted into a standardized format. This conversion includes unifying character encodings and removing unnecessary characters to facilitate processing of the data in a uniform format.

[0702] Step 2:

[0703] The server analyzes standardized data using natural language processing techniques. It tokenizes the input data and performs analysis based on grammar and semantics. This process involves tokenizing the data using the nltk library and classifying intent using scikit-learn. The input is tokenized data, and the output is class labels indicating the user's intent or request.

[0704] Step 3:

[0705] The server dynamically generates responses based on the analysis results. It is required to create appropriate responses from the analyzed intent information. By combining template responses, it generates response sentences containing personalized information. The input is the intent label, and the output is a text response notified to the user. This response is displayed in the user's chosen interface, such as a smartphone notification or the device's chat screen.

[0706] Step 4:

[0707] The server monitors the data processing environment and detects anomalies. It monitors system metrics such as CPU usage and memory consumption, and issues warnings when anomalies are detected. This involves activating sensors and monitoring systems that automatically send notifications when thresholds are exceeded. The input is real-time data of system metrics, and the output is a warning sent to the administrator when an anomaly occurs. This notification content also includes solutions proposed using generative AI models.

[0708] 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.

[0709] This invention is a system designed to improve the efficiency of handling inquiries and claims in non-life insurance, and incorporates an emotion engine. The system aims to recognize user emotions and enhance the user experience.

[0710] First, users submit inquiries or complaints via phone, chat, email, etc. This input data is received by the server and converted and parsed into text using natural language processing. This is where the emotion engine comes in. The server uses the emotion engine as part of its NLP module to identify the user's emotions from the input data. This emotion analysis allows the server to understand the user's situation, such as whether they are happy, confused, or angry.

[0711] Based on the analysis results, the server dynamically generates a response. This response is tailored to the user's emotions and provides appropriate assistance. Furthermore, the generated response is multilingual and includes personalized information as needed. By providing emotionally sensitive responses, users can have a better experience, leading to increased satisfaction.

[0712] This system can record user inquiries and complaints in a database and update their progress in real time. Users can check the current status at any time using a progress tracking tool on their device.

[0713] For example, if a user submits a complaint about damaged goods, the emotion engine recognizes this dissatisfaction. The server generates a calm and prompt response that takes care to soothe the user's anger and clearly explains the compensation process. This increases the user's confidence in the service they receive and helps resolve their dissatisfaction.

[0714] This invention thus improves customer satisfaction while simultaneously automating and streamlining business processes. Emotion-intensive response generation contributes to the creation of highly competitive information systems for future businesses.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] Users send questions and complaints to the system via phone, chat, email, etc.

[0718] Step 2:

[0719] The server converts received messages into text data using speech recognition and text analysis modules. This text data is then standardized into a consistent format.

[0720] Step 3:

[0721] The server uses natural language processing (NLP) techniques to analyze the content of user messages and attempt to understand what the user intends. This analysis identifies the type of inquiry or complaint.

[0722] Step 4:

[0723] The server uses an emotion engine to recognize emotions from user messages. For example, it determines whether the user is angry, confused, or happy based on the vocabulary and context of the message.

[0724] Step 5:

[0725] The server combines analysis results and sentiment recognition results to generate the most appropriate response. The response is sensitive to the user's emotions and includes information and instructions to help solve the problem. This response is generated in multiple languages, and personalized information is provided to the user as needed.

[0726] Step 6:

[0727] The server sends the generated response to the user through the selected communication channel.

[0728] Step 7:

[0729] The server records user inquiries and complaints in a database and manages their progress and response status in real time. This ensures that the latest processing status is always maintained.

[0730] Step 8:

[0731] Users can use a progress tracking tool through their device to check the status and handling of their inquiries and complaints at any time. This feature allows users to experience transparency in the process and gain a sense of security.

[0732] (Example 2)

[0733] 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".

[0734] Conventional customer service systems often lacked the ability to provide appropriate responses that considered user emotions, resulting in decreased customer satisfaction. Furthermore, insufficient multilingual support and personalization of information made it difficult to meet diverse user needs. Therefore, there is a need to develop a system that accurately analyzes user intent and emotions, and dynamically generates appropriate responses based on that analysis, thereby improving customer satisfaction.

[0735] 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.

[0736] In this invention, the server includes means for receiving input information from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for dynamically generating a response based on the analysis results and emotion analysis, and notifying the user. This makes it possible to generate a variety of responses that take the user's emotions into consideration.

[0737] A "user" refers to an individual or legal entity that makes inquiries or requests for information from the system.

[0738] "Input information" refers to the data and requests that users provide to the system.

[0739] A "standardized format" refers to a format that converts various input formats into a consistent format.

[0740] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language.

[0741] "Intention" refers to the purpose or requirements that a user has for the system.

[0742] "Sentiment analysis" refers to the process of identifying and understanding a user's emotions from input information.

[0743] "Dynamic response generation" refers to creating an appropriate response in real time based on the input information.

[0744] "To notify" refers to the act of conveying information or messages to a user.

[0745] A "storage device" refers to a physical or virtual storage medium used to record data or information over the long term.

[0746] "Continuously updating" refers to keeping information and data up-to-date on a daily basis or in real time.

[0747] "Display means" refers to devices or interfaces used to visually present information or data to users.

[0748] "Responding to linguistic diversity" refers to the ability to enable the exchange of information between various languages.

[0749] "User-specific information" refers to customized data and messages related to a particular user.

[0750] This invention provides a system that efficiently handles user inquiries and complaints, thereby improving customer satisfaction. The system is primarily composed of users, servers, and terminals.

[0751] Users submit inquiries and complaints via phone, chat, email, etc. The server receives the input information based on these submissions. The server uses API gateways and data integration tools to convert the input information into a standardized format. This standardization of data formatting facilitates subsequent processing.

[0752] The server uses natural language processing techniques to analyze the user's intent and emotions based on the received data. This process utilizes Python's NLTK and spaCy, and Hugging Face's Transformers model for emotion analysis. These technologies enable a detailed understanding of the user's purpose and emotions.

[0753] After the analysis is complete, the server dynamically generates a response based on the analysis results and sentiment analysis. For example, OpenAI's GPT-4 can be used as the generation AI model. This makes it possible to generate responses that take the user's emotions into consideration in real time.

[0754] The device notifies the user of the generated response. The response is translated using the Google Cloud Translation API or similar tools as needed for multilingual support and provided to the user in an easily understandable format.

[0755] For example, if a user submits a travel insurance claim, the feedback text is parsed using a prompt like this: "Input text: I have a question about my travel insurance. I had an accident while traveling and would like to confirm if it is covered.\nTask: Identify the intent and sentiment of this text and generate an appropriate response message."

[0756] For example, if a user expresses anger about a damaged product, the server generates a response such as, "We apologize for the inconvenience. We will contact you regarding the product replacement process," and notifies the user of this information via their device. This process enables a swift and appropriate response that takes the user's feelings into consideration.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] Users submit inquiries and complaints via phone, chat, or email. This input reaches the server in various forms, such as text messages or audio data.

[0760] Step 2:

[0761] The server converts the received input information into a standardized format. This conversion is performed via an API gateway, unifying data in different formats into a single text format. This facilitates subsequent natural language processing. The output of this process is standardized text data.

[0762] Step 3:

[0763] The server analyzes standardized text data using natural language processing techniques. Specifically, it uses Python's NLTK and spaCy to extract the user's intent and subject from the text. As a result of this analysis, the server obtains output data that allows it to understand the user's specific requests.

[0764] Step 4:

[0765] The server performs sentiment analysis on the parsed text data. Here, it uses the Hugging Face Transformers model to detect the user's emotional state. For example, it can identify whether the user is feeling anxious or angry. The output is analytical data indicating the user's emotions.

[0766] Step 5:

[0767] The server generates dynamic responses using a generative AI model based on the analysis results and sentiment analysis data. Specifically, it uses OpenAI's GPT-4 to construct appropriate responses for the user. The output of this process is a response message to be sent to the user.

[0768] Step 6:

[0769] The server makes the generated response messages multilingual. If necessary, it uses the Google Cloud Translation API to translate messages into the user's language. This multilingual translation facilitates message comprehension and makes the service accessible to users who speak different languages.

[0770] Step 7:

[0771] The device notifies the user of the translated response. This notification is delivered as a message to the user's device, where the user can check it. Email and push notifications are often used to notify the user.

[0772] Step 8:

[0773] The server records all process data, query details, and generated responses in a storage device. It also continuously updates the progress status, allowing users to review this information later. This facilitates query history management and progress tracking.

[0774] Step 9:

[0775] Users can check the progress of their inquiries and complaints in real time through the display on their device. This display is tailored to the user's needs and presented in a visually easy-to-understand interface.

[0776] (Application Example 2)

[0777] 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".

[0778] In electronic payment services, accurately analyzing user emotions and responding accordingly is essential for promptly and appropriately addressing customer inquiries and complaints. Currently, services that consider customer emotions are insufficient, and improving customer satisfaction remains a challenge.

[0779] 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.

[0780] In this invention, the server includes means for receiving data from a user and converting it into a standardized format, means for analyzing the user's intentions and emotions using natural language processing technology, and means for generating a dynamic response that takes the user's emotions into consideration based on the analysis results and notifying the user. This enables optimal responses based on the user's emotions, and is expected to improve customer satisfaction.

[0781] "User" refers to individual customers who use electronic payment services.

[0782] "Data" refers to all information entered by users, and includes various formats such as text, audio, and images.

[0783] A "standardized format" refers to a state in which various input data has been converted into a unified format.

[0784] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0785] "Sentiment analysis" refers to the technology that identifies a user's emotions and feelings from their input data.

[0786] A "dynamic response" refers to an optimal, situation-dependent reply generated in real time.

[0787] "Notification" refers to the act of a server communicating information to a user.

[0788] "Multilingual translation technology" refers to the technology that converts text expressed in one language into multiple other languages.

[0789] A "screen" refers to an interface through which a user visually obtains information.

[0790] A "server" refers to a central computer system that processes and manages data.

[0791] "Personalized information" refers to information that is customized according to the individual user's needs and circumstances.

[0792] This invention is a system that analyzes user emotions and generates optimized responses based on those emotions, with the aim of improving the customer experience in electronic payment services. This system is built through the interaction of the user, server, and terminal.

[0793] Users input inquiries or complaints regarding electronic payments using mobile devices such as smartphones or smart glasses. The terminal then sends the input data to a server, where it is converted into a standardized format and analyzed using natural language processing (NLP) technology. This NLP utilizes Google Cloud Natural Language API and spaCy. During the analysis, a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) identifies the user's emotions. Based on this sentiment analysis, the server generates a dynamic, optimized response that takes the user's feelings into consideration. This response is then translated into the appropriate language using multilingual translation technology (e.g., Google Translation API) and communicated to the user via the terminal.

[0794] For example, if a user expresses dissatisfaction with a payment error, the voice and text data obtained through the smart glasses are analyzed on the server, and a response such as, "We apologize, we will check immediately. Please try the following steps," is generated. This response also includes personalized information to alleviate the user's anxiety and encourage appropriate action.

[0795] By using a generative AI model to design user-specific prompts, the server can respond effectively and efficiently. This is expected to improve customer satisfaction.

[0796] Example prompt for the generating AI model: "The customer is confused by a payment error. Please generate the best support message in Japanese."

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The terminal receives input data from the user. This data is typically text or audio data related to inquiries or complaints. Regardless of its format, the received data is transferred to the server.

[0800] Step 2:

[0801] The server converts the received data into a standardized format. Here, natural language processing techniques are used to convert it into text data. For example, audio data is converted into text data via speech recognition software, and all data is stored and processed as unified text information.

[0802] Step 3:

[0803] The server further analyzes the standardized text data to identify the user's intent and emotions. This process utilizes the Google Cloud Natural Language API and IBM Watson Tone Analyzer to perform sentiment analysis on the input data. From the input text data, it outputs sentiment tags that match the user's emotional state.

[0804] Step 4:

[0805] The server generates a dynamic response optimized for the user's emotions based on the analysis results. Here, a customized response, set as a prompt, is generated using an appropriate generative AI model based on the sentiment analysis results. At this stage, the response may also be translated into the user's language using multilingual translation technology.

[0806] Step 5:

[0807] The server sends the generated response to the terminal, and the terminal notifies the user of that response. The user receives the displayed message and can decide on the next action. For example, a message such as "I will check immediately. See below for details." might appear on the screen.

[0808] 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.

[0809] 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.

[0810] 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 robot 414.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A means of receiving input data from a user and converting it into a standardized format,

[0832] A means of analyzing user intent using natural language processing technology,

[0833] A means for dynamically generating a response based on the analysis results and notifying the user,

[0834] A means of saving records of inquiries and complaints in a database and updating progress in real time,

[0835] A system that includes means of providing an interface that allows users to check their progress.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the aforementioned natural language processing technology enables multilingual support.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized in that the response generation means provides personalized information.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of receiving information from users via a communication device and converting it into a unified information format,

[0843] A means of determining the user's intent using natural language processing technology,

[0844] A means of adaptively constructing a response based on the analysis results and providing it to the user,

[0845] A means of recording the history of inquiries and requests in a storage device and updating the progress in real time,

[0846] A means for providing a display device that allows users to check the progress,

[0847] A system that uses a generation module to support natural language processing technology and includes means for generating flexible text that goes beyond templates.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein the aforementioned natural language processing technology enables multilingual support and provides information in the language selected by the user.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that the response building means provides individualized information and responds devotedly according to the input information.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A means of receiving input data from a user and converting it into a standardized format,

[0855] A means of analyzing user intent using natural language processing technology,

[0856] A means for dynamically generating a response based on the analysis results and notifying the user,

[0857] A means of saving records of inquiries and complaints in a database and updating progress in real time,

[0858] A means of providing an interface that allows users to check their progress,

[0859] A means of monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected,

[0860] A means of proposing a solution when an anomaly is detected,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, wherein the aforementioned natural language processing technology enables multilingual support.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the response generation means provides personalized information.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A means of receiving user input information and converting it into a standardized format,

[0869] A means of analyzing user intent and emotions using natural language processing technology,

[0870] A means for dynamically generating a response based on analysis results and sentiment analysis and notifying the user,

[0871] A means of storing records of inquiries and complaints in a storage device and updating the progress sequentially,

[0872] A system that includes means for providing a display mechanism that allows the user to check the progress.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the aforementioned natural language processing technology enables the system to handle linguistic diversity.

[0875] (Claim 3)

[0876] The system according to claim 1, characterized in that the response generation means provides individual user information.

[0877] "Application example 2 when combining with an emotional engine"

[0878] (Claim 1)

[0879] A means of receiving data from users and converting it into a standardized format,

[0880] A means of analyzing user intent and emotions using natural language processing technology,

[0881] A means to generate a dynamic response that takes the user's emotions into consideration based on the analysis results and notify the user,

[0882] A means of storing records of inquiries and complaints in a storage device and updating their progress in real time,

[0883] A means of providing a screen where users can check their progress,

[0884] A system that includes means for converting responses into various languages ​​and notifying them using multilingual translation technology.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the aforementioned natural language processing technology has an emotion analysis function and selects an appropriate response based thereon.

[0887] (Claim 3)

[0888] The system according to claim 1, characterized in that the response generation means attaches personalized information according to the situation, and provides an appropriate response based on emotion. [Explanation of Symbols]

[0889] 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 input data from a user and converting it into a standardized format, A means of analyzing user intent using natural language processing technology, A means for dynamically generating a response based on the analysis results and notifying the user, A means of saving records of inquiries and complaints in a database and updating progress in real time, A means of providing an interface that allows users to check their progress, A means of monitoring the status of the data processing environment and issuing a warning to the administrator when an anomaly is detected, A means of proposing a solution when an anomaly is detected, A system that includes this.

2. The system according to claim 1, wherein the aforementioned natural language processing technology enables multilingual support.

3. The system according to claim 1, characterized in that the response generation means provides personalized information.

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