system

Abstract

We provide the system. [Solution] A means of receiving questions entered by the user via a communication terminal, A means of analyzing the received question using a natural language processing engine, A means for extracting multiple solutions from a knowledge database based on the analysis results, A means of selecting an appropriate solution based on user attribute information, A means of presenting the selected solution to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When users face technical problems, there is a problem that it takes time to solve them and they feel stressed. Conventional support systems lack efficiency and often cannot provide users with quick and appropriate solutions. The present invention aims to improve the speed of solving such technical problems and enhance the user experience.

Means for Solving the Problems

[0005] This invention provides a system that receives questions entered from a user's communication terminal and analyzes them through a natural language processing engine. Based on the analysis results, it extracts multiple solutions from a knowledge database and further selects the optimal solution considering the user's attribute information. The selected solution is appropriately presented to the user, supporting a process of quickly resolving technical problems. This makes it possible for users without specialized knowledge to easily receive technical support.

[0006] A "user" is an individual or organization that utilizes an information system and seeks technical questions or solutions to problems.

[0007] "Communication terminals" refer to various electronic devices used by users to input and receive information, and include computers, smartphones, tablets, and other similar devices.

[0008] A "natural language processing engine" is a software technology that analyzes text data entered by a user and understands or interprets its content.

[0009] "Analysis results" refer to information obtained by analyzing user input using a natural language processing engine, clarifying its content and intent.

[0010] A "knowledge database" is a data storage system that accumulates solutions and information related to technical problems.

[0011] A "solution" refers to a specific method or procedure for addressing a particular technical problem.

[0012] "User attribute information" refers to information about the user's device, including data such as the type of device used and the operating system.

[0013] "Selecting the appropriate solution" is the process of choosing the most effective and efficient method for the user from among multiple solutions extracted from a knowledge database.

[0014] "Presentation" refers to the act of showing or informing the user of selected information or solutions. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention provides a system that offers rapid and accurate solutions to technical support questions submitted by users via a communication terminal. The details of its embodiments are described below.

[0037] Users access the chat interface using communication devices such as smartphones or personal computers. Through this interface, users input technical problems in text format and send them to the system.

[0038] This input is sent to the server as digital data by the terminal. The server analyzes the received input using a natural language processing engine to identify the problem the user is facing. For example, if a user reports a problem such as "I can't connect to Wi-Fi," the server analyzes this information, extracts keywords such as "Wi-Fi" and "cannot connect," and understands the context.

[0039] Next, the server consults a knowledge database to extract solutions for similar problems. This database contains past case studies and common troubleshooting procedures, which the server uses to generate solutions. For example, "restarting the router" or "reviewing Wi-Fi settings" might be extracted as solutions.

[0040] Furthermore, the server selects the optimal solution by considering user attribute information, namely the type of device being used and its operating system information. For example, for a user using Windows 10, a Windows-specific configuration verification procedure will be suggested.

[0041] Ultimately, the server sends the selected solution to the terminal and displays it clearly to the user. The user then follows the suggested steps to resolve the problem. In this way, the system allows users to resolve technical problems easily and efficiently. Furthermore, since the system operates 24 hours a day, technical support services are available at any time.

[0042] For example, if a user reports a problem such as "the printer is not printing," the server analyzes the keywords "printer" and "not printing" and suggests solutions appropriate to the situation, such as reinstalling the driver or checking the cable connection. This allows for a consistent user experience and problem resolution.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input technical problems as text using a chat interface from their communication terminal. For example, "The printer isn't printing."

[0046] Step 2:

[0047] The terminal sends user input to the server. Along with this input, the terminal's device information and operating system information are also sent.

[0048] Step 3:

[0049] The server analyzes the received user input using a natural language processing engine. This analysis extracts keywords from the text (for example, "printer" or "do not print") to identify the content of the problem.

[0050] Step 4:

[0051] The server consults a knowledge database based on the analysis and extracts relevant solutions from past cases. General troubleshooting procedures are also considered.

[0052] Step 5:

[0053] The server selects the most suitable solution from the extracted options based on user attribute information. Solutions are filtered according to the user's device type and operating system.

[0054] Step 6:

[0055] The server sends the selected solution to the terminal. The terminal receives this information and displays the steps to the user in an easy-to-understand manner.

[0056] Step 7:

[0057] The user attempts to resolve the issue themselves by following the provided steps. The user solves the technical problem by performing the operations sequentially.

[0058] (Example 1)

[0059] 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."

[0060] In today's information society, users face a variety of technical problems every day. Solving these problems quickly and efficiently is crucial for improving the convenience of users' lives and work. However, traditional systems required users to go through multiple steps, making the problem-solving process cumbersome and inefficient. This resulted in a poor user experience and prolonged problem-solving times.

[0061] 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.

[0062] In this invention, the server includes means for receiving inquiries entered by the user via an information terminal, means for analyzing the received inquiries with a natural language processing engine and extracting keywords to identify the problem, means for searching a database for multiple solutions based on the analysis results, means for selecting the optimal solution based on user characteristic information, and means for displaying the selected solution to the user. This enables the user to solve technical problems more quickly and easily.

[0063] A "user" is someone who makes inquiries to the system via an information terminal.

[0064] An "information terminal" is a device used by users to input inquiries and communicate with a system, and includes smartphones, personal computers, and other similar devices.

[0065] An "inquiry" refers to a technical problem or question sent to the system by a user via an information terminal.

[0066] A "server" is a central computing device that analyzes inquiries received from users, generates solutions, and provides them to the users.

[0067] A "natural language processing engine" is a software component that analyzes user inquiries to extract keywords and identify problems.

[0068] A "keyword" is an important word or phrase identified during the analysis process of a query, which indicates the characteristics of the problem.

[0069] A "database" is a collection of information that stores past examples and solutions, and is used to search for solutions based on analysis results.

[0070] "User characteristic information" refers to attribute data that includes information about the type of device the user is using and the operating system.

[0071] A "solution" is a suggestion or procedure to resolve a problem identified in response to a user inquiry.

[0072] "Display" refers to a method of communicating the selected solution to the user in an easy-to-understand manner, and is typically done through the screen of an information terminal or similar device.

[0073] This invention is a system that provides rapid and efficient support when a user requests technical assistance via an information terminal. The user uses an information terminal such as a smartphone or personal computer to connect to the system via a chat interface. When the user inputs a technical problem in text format, the information terminal sends this input data to the server.

[0074] The server analyzes the received digital data using a natural language processing engine. This engine extracts keywords and identifies the user's problem. Specifically, for common problems such as "Wi-Fi cannot connect," it extracts relevant keywords and understands the appropriate context. This analysis utilizes advanced generative AI models to achieve highly accurate problem understanding.

[0075] Once the analysis is complete, the server consults a knowledge database, searching for multiple solutions based on similar past cases and troubleshooting procedures. This database contains solutions to common problems and past success stories, which the server uses to quickly provide effective solutions.

[0076] Furthermore, the server takes user characteristics information into account. This includes the type of device the user is using and information about their operating system, and this data helps to customize the solution. For example, a user using Windows 10 will be offered platform-specific instructions.

[0077] Finally, the server sends the selected solution back to the information terminal, converting it into a format that can be displayed to the user. This allows the user to obtain clear and easy-to-follow instructions and quickly resolve the problem.

[0078] As a concrete example, consider a case where a user reports a problem where "the printer is not printing." In this case, the server analyzes the keywords "printer" and "not printing" and suggests appropriate solutions such as reinstalling the driver or checking the cable connection. Through this process, the user can quickly resolve the problem and operate more smoothly.

[0079] The expected prompt message would be something like, "My printer won't start printing. Do I need to reinstall the driver?" The system will then automatically analyze the entered information and suggest the best solution.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user accesses the chat interface via an information terminal. Here, the user enters technical problems, such as "Wi-Fi connection failed," in text format and presses the send button. This input text is treated as a query to the system. The output is the query in digital data format.

[0083] Step 2:

[0084] The terminal sends the user's text input as digital data to the server. Here, the data is converted to digital format, and a connection to the server is established based on the transmission protocol. The output is the digital data sent to the server.

[0085] Step 3:

[0086] The server analyzes the received digital data using a natural language processing engine. Specifically, it extracts keywords such as "Wi-Fi" and "cannot connect" through text analysis and identifies the problem. The input is digital data from the user, and the output is a list of analyzed keywords and the results of the problem identification.

[0087] Step 4:

[0088] The server references a knowledge database based on the analysis results. The database contains past problem-solving examples, and the server searches for solutions to similar problems. The input is a list of keywords, and the output is multiple potential solutions.

[0089] Step 5:

[0090] The server uses user characteristic information to select the optimal solution. This characteristic information includes the type of device being used and operating system information, and the solution is customized based on this information. The input is the candidate solution and user characteristic information, and the output is the selected optimal solution.

[0091] Step 6:

[0092] The server sends the selected solution as digital data to the terminal. This process involves data format conversion based on the transmission protocol. The input is the selected solution, and the output is the digital data sent to the terminal.

[0093] Step 7:

[0094] The terminal converts the received digital data into a format that is easy for the user to understand and displays it on the chat interface. The user then uses the displayed information to perform problem-solving steps. The input is digital data from the server, and the output is the solution displayed to the user. This process makes problem-solving easier for the user.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern society, cybersecurity threats are increasing, and users face various security risks on a daily basis. In this environment, it is crucial that users can respond to security issues quickly and accurately. However, traditional methods often take a long time to identify security problems and provide appropriate solutions, and there is a need to improve this.

[0098] 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.

[0099] In this invention, the server includes means for receiving security-related problems entered by a user via a communication device, means for analyzing the received problems using natural language processing technology, and means for extracting multiple solutions from a knowledge base based on the analysis results. This enables the user to quickly and effectively identify and address security risks.

[0100] A "user" refers to an individual or organization that reports security-related issues to an information system via a communication device.

[0101] "Communication devices" are electronic devices used by users to input information and send it to a server, and include smartphones and smart glasses.

[0102] "Security-related issues" refer to cybersecurity threats or anomalies such as unauthorized access, phishing emails, and virus infections.

[0103] "Natural language processing technology" is a computer technology that analyzes text data received from users and understands its context.

[0104] A "knowledge base" is a database that stores past cases and best practices that are useful for solving security problems.

[0105] "User attribute information" refers to information about the type of communication device and operating system used by the user.

[0106] "Solutions" refer to specific countermeasures and preventative measures proposed for security-related issues.

[0107] A "server" is a computer system that analyzes received information and performs computational processing to provide solutions.

[0108] The system for realizing this invention consists of a communication device, a server, and a knowledge-based database. Its primary purpose is to provide a platform for quickly and effectively resolving security-related issues faced by users.

[0109] Users use communication devices such as smartphones or smart glasses to input security-related issues in text format. The communication device converts this text data into a digital format and sends it to the server.

[0110] The server uses a natural language processing engine (e.g., Google® Cloud Natural Language API) to analyze the received text data. Based on the keywords and contextual information identified through the analysis, the server understands the nature of the problem the user is facing.

[0111] Based on the analysis results, the server extracts relevant solutions from its knowledge base. This knowledge base contains security best practices and past case studies. Furthermore, it considers information such as the user's communication device type and operating system to select the appropriate solution.

[0112] The selected solution is then sent back to the user's communication device and displayed on the screen. This allows the user to quickly identify and address security issues.

[0113] For example, if a user reports receiving a suspicious email, the server will associate it with a phishing email and provide a recommendation from its knowledge base to "delete the email and not open any attachments."

[0114] As an example of a prompt, in response to a user input such as, "What should I do if I receive a suspicious email?", the server can suggest appropriate preventative measures. This allows users to respond quickly and appropriately to security risks they face on a daily basis.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters security-related issues in text format on a communication terminal. The entered text is converted into digital data by the communication terminal and sent to the server. During this process, prompts are appropriately formatted and processed into data that the server can easily understand.

[0118] Step 2:

[0119] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to extract security-related keywords and context from the input data. This process identifies the specific nature of the problem. For example, the keyword "suspicious email" might be extracted.

[0120] Step 3:

[0121] Based on the analysis results, the server extracts relevant solutions from its knowledge base. It searches for similar cases in the database to determine the best course of action against phishing emails. This determination then selects appropriate preventative measures and response procedures for the user.

[0122] Step 4:

[0123] The server references user attribute information and ultimately selects the optimal solution based on the platform and operating system of the communication device the user is using. For example, if an ANDROID® device is being used, Android-specific instructions will be reflected.

[0124] Step 5:

[0125] The selected solution is sent from the server to the communication terminal and presented to the user. The terminal displays the received information on the screen, allowing the user to act accordingly. This enables the user to quickly implement the recommended action.

[0126] 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.

[0127] This invention provides a system that uses an emotion engine to respond quickly and appropriately to technical questions from users. This system uses a communication terminal to input the user's challenges and technical problems in natural language.

[0128] When a user submits a question using the device's chat interface, the device sends its input data, device information, and operating system information to the server. Crucially, the server uses a sentiment engine to extract emotions from the user's text. The sentiment engine identifies positive, negative, and neutral emotions through text analysis.

[0129] The server analyzes the received text using a natural language processing engine. This analysis identifies the nature of the problem and the user's emotions. Then, it consults a knowledge database to extract multiple solutions related to the problem.

[0130] The extracted solutions are optimized based on user attribute information and perceived emotions. Specifically, if a user expresses negative emotions, the server prioritizes presenting support messages and reassuring procedures that align with those emotions. In addition, additional support information is provided as needed.

[0131] For example, if a user enters a negative message such as, "I'm really fed up with this app crashing every time," the server uses an emotion engine to detect this "fed up" emotion. Then, in addition to the usual solutions, a reassuring message is displayed first, including phrases like, "Don't worry, this is a common problem and will be fixed soon," to stabilize the user's emotions.

[0132] Finally, the selected solution is sent to the terminal, and the user receives and executes clear instructions to resolve the problem. By using this system, users can resolve their technical problems efficiently and with peace of mind.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] Users send technical problems as text input using the chat interface of their communication device. For example, they might type, "The app keeps crashing and it's frustrating."

[0136] Step 2:

[0137] The terminal sends the entered text data to the server. This data also includes the user's device information and operating system information.

[0138] Step 3:

[0139] The server passes the received text to the emotion engine, which analyzes the user's emotions. Here, the emotion "frustrated" is identified as negative.

[0140] Step 4:

[0141] The server uses a natural language processing engine to analyze user input and extract keywords related to the problem (such as "the app crashed").

[0142] Step 5:

[0143] The server consults a knowledge database and generates multiple relevant solutions based on the extracted keywords.

[0144] Step 6:

[0145] The server optimizes solutions based on user attribute information and sentiment analysis results. Since negative emotions have been detected, solutions that include messages to reassure the user are prioritized.

[0146] Step 7:

[0147] The server sends the selected solution to the terminal. The terminal receives it and displays the instructions and messages to the user in an easy-to-understand manner.

[0148] Step 8:

[0149] The user follows the displayed instructions to resolve the technical issue. The server receives feedback from the user as needed and prepares to take further action.

[0150] (Example 2)

[0151] 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".

[0152] In modern technical support systems, solutions provided without considering the user's feelings can amplify their anxiety and stress. This can result in a poor user experience and hinder prompt and appropriate problem resolution. There is a need to provide effective methods to address these challenges and improve user satisfaction.

[0153] 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.

[0154] In this invention, the server includes means for receiving questions entered by the user via an information processing device, means for analyzing the emotions contained in the received questions, and means for analyzing the text data using a natural language processing engine. This makes it possible to present solutions that take the user's emotions into consideration. With this system, users can solve technical problems more quickly and appropriately while feeling at ease.

[0155] An "information processing device" is a general term for a device used by a user for input and equipped with communication capabilities.

[0156] "Means of receiving" refers to a method or mechanism for acquiring data transmitted from an information processing device.

[0157] "Means of analyzing emotions" refers to technologies and processes for identifying and classifying the emotions contained in text received from users.

[0158] "Means of analyzing text data using a natural language processing engine" refers to a method that uses natural language processing techniques to analyze input text data and understand its meaning and relationships.

[0159] A "knowledge base" is a database that aggregates information useful for solving technical problems, and it contains past cases and specialized knowledge.

[0160] "User attribute information" refers to information related to individual users, including information about the devices they use and their operating environment.

[0161] "Means of selection" refers to the process of selecting and proposing the optimal solution based on the analyzed information.

[0162] "Reassuring supplementary information" refers to information added to a solution to prevent users from feeling anxious or stressed.

[0163] A "collection of past cases" refers to a data set that compiles records and examples of problem-solving that have been collected to date.

[0164] This invention is a system for efficiently and reliably solving users' technical problems. Users input technical questions in natural language using a communication terminal, which acts as an information processing device. This terminal can be, for example, a smartphone or a computer, and includes hardware capable of internet connectivity.

[0165] When a user enters a question through the terminal's interface and submits it, the terminal sends that information to the server. The information sent includes the entered text data, the type of device being used, and operating system information. The server first passes the received text to a sentiment analysis engine to identify positive, negative, or neutral emotions.

[0166] Next, the server uses a natural language processing engine to analyze the user's question and identify the nature of the technical problem. For example, if the input is "This app crashes every time," sentiment analysis will identify negative emotions, and the content will be analyzed to include "crash" and "app."

[0167] Based on the analysis results, the server consults a knowledge base and extracts multiple solutions related to the problem. The extracted solutions are optimized based on user attribute information and sentiment analysis results, and information that reassures the user is added. For example, a message such as "Don't worry, this is a common problem and can be solved quickly" is added.

[0168] Ultimately, the server sends this information back to the terminal, allowing the user to proceed with problem resolution based on the received solutions. This process provides prompt and appropriate support, enabling users to address technical issues with confidence.

[0169] As an example of a prompt, giving the model instructions such as, "Provide solutions for when a user is dissatisfied with app crashes," can help provide more appropriate and emotion-sensitive solutions.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user inputs a technical question in natural language into the input interface of the communication terminal and sends it. The input is text data containing a specific problem, such as "This app crashes every time." At this time, the terminal captures the information and obtains device information and operating system information. As output, the user's input text and technical information are compiled and ready to be sent to the server.

[0173] Step 2:

[0174] The terminal sends user input text, device information, and operating system information to the server. The input includes the data prepared in the previous step and is sent to the server. As output, the server receives this data and prepares it for analysis.

[0175] Step 3:

[0176] The server passes the received text data to the sentiment engine for sentiment analysis. The input is text data, and the analysis identifies positive, negative, and neutral emotions. Specifically, it extracts negative emotions from the word "fed up." The output is the result of the sentiment analysis, which is used in the next processing step.

[0177] Step 4:

[0178] The server uses a natural language processing engine to analyze text data. The input consists of text and sentiment analysis results. The text analysis identifies the nature of the technical problem. Specifically, keywords such as "crash" and "app" are extracted. The output generates data on the analyzed technical problem.

[0179] Step 5:

[0180] The server extracts solutions from its knowledge base based on the analyzed information. The input includes data on the technical problem, which is used to search for relevant solutions. Specifically, relevant past cases and recommended procedures are retrieved. The output is a list of multiple solutions.

[0181] Step 6:

[0182] The server optimizes solutions based on user attribute information and sentiment analysis results. The input is a list of solutions, which are filtered according to the user's attributes and emotions. Specifically, messages that provide reassurance in response to negative emotions are selected. The output is an optimized solution.

[0183] Step 7:

[0184] The server sends the optimized solution to the terminal. The input includes the final selected solution. The specific operation of sending involves transmitting the data to the terminal over the network. The output is that the terminal receives the solution and is ready to display it to the user.

[0185] (Application Example 2)

[0186] 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".

[0187] When users encounter technical problems, traditional systems mechanically offer solutions without considering the user's feelings, resulting in a failure to adequately enhance user satisfaction. Because the same approach is used even when dealing with users experiencing negative emotions, it fails to provide a sense of security and trust, ultimately hindering improvements in the user experience.

[0188] 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.

[0189] In this invention, the server includes means for receiving technical questions entered by the user via a communication device, means for analyzing the received questions using a natural language processing device, means for extracting multiple solutions from a database based on the analysis results, means for analyzing the user's emotions using an emotion recognition engine, means for selecting the optimal solution based on user attribute information and emotion analysis results, and means for presenting the user with a support message corresponding to the selected solution and emotions. This makes it possible to provide the best possible response according to the user's emotions and improve the user experience.

[0190] A "communication device" is a device that allows a user to input technical questions and transmit them to a system.

[0191] A "natural language processing unit" is a computer system that analyzes text data received from a user and interprets its meaning.

[0192] A "database" is a system that stores past solutions and information, and is used to extract information for problem-solving based on analysis results.

[0193] An "emotion recognition engine" is software that analyzes the emotions contained in user input and identifies that emotional state.

[0194] "User attribute information" refers to information related to the type of equipment and operating system used by the user, and serves as basic data for making optimal suggestions to the user.

[0195] A "solution" is a method or procedure for solving a problem that the system proposes to the user based on the results of its analysis.

[0196] A "support message" is a communication message provided in response to the user's emotional state, designed to convey a sense of stability and trust.

[0197] The system implementing this invention aims to efficiently process users' technical questions and provide appropriate solutions based on sentiment analysis. Specific embodiments are described below.

[0198] When a user enters a technical question using a communication device, the data is sent to the server. The server uses a natural language processing unit (NLTK) to analyze the input text and identify the problem being addressed. This analysis uses Python programs and natural language processing libraries such as NLTK and spaCy.

[0199] For identified problems, the server extracts multiple solutions from the database. The database also includes past case data, enabling efficient information extraction.

[0200] Next, the server activates its emotion recognition engine to analyze the user's emotions from the input text. This analysis is performed using text analysis tools such as TextBlob and TENSORFLOW®.

[0201] The server then considers the user's attribute information and the results of the sentiment analysis to select the optimal solution. If the user is showing negative emotions, a support message is generated simultaneously to provide the user with a sense of reassurance.

[0202] For example, if a user asks a question such as, "I want to know why my order was canceled," the system uses an emotion recognition engine to read the underlying anxiety arising from that question and, accordingly, adds a supportive message such as, "We apologize for the inconvenience. You can find more details about your order cancellation here."

[0203] An example of a prompt for a generative AI model might be, "If the user is feeling uncomfortable, please suggest how to respond." This allows the server to present the AI ​​model with prompts that are relevant to the user's emotions, enabling it to create more appropriate responses.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user inputs a technical question via a communication device, and the terminal sends that text data to the server. The input is the user's question, and the output is the unparsed text data received by the server.

[0207] Step 2:

[0208] The server uses a natural language processing unit to analyze the received text data. This analysis reveals the intent behind the user's question and specific technical problems. The input is unanalyzed text data, and the output is structured data containing the problematic information.

[0209] Step 3:

[0210] The server extracts relevant solutions from the database based on structured data. The input is structured data related to the problem, and the output is a list containing multiple solutions. Database searches are performed by cross-referencing with past cases.

[0211] Step 4:

[0212] The server uses an emotion recognition engine to analyze the user's emotions from the question text. The input is unanalyzed text data, and the output is the user's emotional state (positive, negative, or neutral). Text analysis tools are used for emotion analysis.

[0213] Step 5:

[0214] The server selects the optimal solution based on user attribute information and emotional state. Inputs are user attribute information, emotional state, and a list of solutions; output is the optimized solution. Selection criteria include the user's environment (device, software, etc.).

[0215] Step 6:

[0216] The server generates and presents to the user a support message tailored to the selected solution and the user's emotional state. The input is the optimized solution and emotional state, while the output is a response message to the user. The support message incorporates content that addresses the user's emotional state.

[0217] Step 7:

[0218] The user receives the proposed solutions and support messages and takes the actions necessary to resolve the problem. The input is the response message, and the output is the user's actions and the progress of problem resolution. This completes the entire system cycle.

[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 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] The present invention provides a system that offers rapid and accurate solutions to technical support questions submitted by users via a communication terminal. The details of its embodiments are described below.

[0236] Users access the chat interface using communication devices such as smartphones or personal computers. Through this interface, users input technical problems in text format and send them to the system.

[0237] This input is sent to the server as digital data by the terminal. The server analyzes the received input using a natural language processing engine to identify the problem the user is facing. For example, if a user reports a problem such as "I can't connect to Wi-Fi," the server analyzes this information, extracts keywords such as "Wi-Fi" and "cannot connect," and understands the context.

[0238] Next, the server consults a knowledge database to extract solutions for similar problems. This database contains past case studies and common troubleshooting procedures, which the server uses to generate solutions. For example, "restarting the router" or "reviewing Wi-Fi settings" might be extracted as solutions.

[0239] Furthermore, the server selects the optimal solution by considering user attribute information, namely the type of device being used and its operating system information. For example, for a user using Windows 10, a Windows-specific configuration verification procedure will be suggested.

[0240] Ultimately, the server sends the selected solution to the terminal and displays it clearly to the user. The user then follows the suggested steps to resolve the problem. In this way, the system allows users to resolve technical problems easily and efficiently. Furthermore, since the system operates 24 hours a day, technical support services are available at any time.

[0241] For example, if a user reports a problem such as "the printer is not printing," the server analyzes the keywords "printer" and "not printing" and suggests solutions appropriate to the situation, such as reinstalling the driver or checking the cable connection. This allows for a consistent user experience and problem resolution.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users input technical problems as text using a chat interface from their communication terminal. For example, "The printer isn't printing."

[0245] Step 2:

[0246] The terminal sends user input to the server. Along with this input, the terminal's device information and operating system information are also sent.

[0247] Step 3:

[0248] The server analyzes the received user input using a natural language processing engine. This analysis extracts keywords from the text (for example, "printer" or "do not print") to identify the content of the problem.

[0249] Step 4:

[0250] The server consults a knowledge database based on the analysis and extracts relevant solutions from past cases. General troubleshooting procedures are also considered.

[0251] Step 5:

[0252] The server selects the most suitable solution from the extracted options based on user attribute information. Solutions are filtered according to the user's device type and operating system.

[0253] Step 6:

[0254] The server sends the selected solution to the terminal. The terminal receives this information and displays the steps to the user in an easy-to-understand manner.

[0255] Step 7:

[0256] The user attempts to resolve the issue themselves by following the provided steps. The user solves the technical problem by performing the operations sequentially.

[0257] (Example 1)

[0258] 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."

[0259] In today's information society, users face a variety of technical problems every day. Solving these problems quickly and efficiently is crucial for improving the convenience of users' lives and work. However, traditional systems required users to go through multiple steps, making the problem-solving process cumbersome and inefficient. This resulted in a poor user experience and prolonged problem-solving times.

[0260] 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.

[0261] In this invention, the server includes means for receiving inquiries entered by the user via an information terminal, means for analyzing the received inquiries with a natural language processing engine and extracting keywords to identify the problem, means for searching a database for multiple solutions based on the analysis results, means for selecting the optimal solution based on user characteristic information, and means for displaying the selected solution to the user. This enables the user to solve technical problems more quickly and easily.

[0262] A "user" is someone who makes inquiries to the system via an information terminal.

[0263] An "information terminal" is a device used by users to input inquiries and communicate with a system, and includes smartphones, personal computers, and other similar devices.

[0264] An "inquiry" refers to a technical problem or question sent to the system by a user via an information terminal.

[0265] A "server" is a central computing device that analyzes inquiries received from users, generates solutions, and provides them to the users.

[0266] A "natural language processing engine" is a software component that analyzes user inquiries to extract keywords and identify problems.

[0267] A "keyword" is an important word or phrase identified during the analysis process of a query, which indicates the characteristics of the problem.

[0268] A "database" is a collection of information that stores past examples and solutions, and is used to search for solutions based on analysis results.

[0269] "User characteristic information" refers to attribute data that includes information about the type of device the user is using and the operating system.

[0270] A "solution" is a suggestion or procedure to resolve a problem identified in response to a user inquiry.

[0271] "Display" refers to a method of communicating the selected solution to the user in an easy-to-understand manner, and is typically done through the screen of an information terminal or similar device.

[0272] This invention is a system that provides rapid and efficient support when a user requests technical assistance via an information terminal. The user uses an information terminal such as a smartphone or personal computer to connect to the system via a chat interface. When the user inputs a technical problem in text format, the information terminal sends this input data to the server.

[0273] The server analyzes the received digital data using a natural language processing engine. This engine extracts keywords and identifies the user's problem. Specifically, for common problems such as "Wi-Fi cannot connect," it extracts relevant keywords and understands the appropriate context. This analysis utilizes advanced generative AI models to achieve highly accurate problem understanding.

[0274] Once the analysis is complete, the server consults a knowledge database, searching for multiple solutions based on similar past cases and troubleshooting procedures. This database contains solutions to common problems and past success stories, which the server uses to quickly provide effective solutions.

[0275] Furthermore, the server takes user characteristics information into account. This includes the type of device the user is using and information about their operating system, and this data helps to customize the solution. For example, a user using Windows 10 will be offered platform-specific instructions.

[0276] Finally, the server sends the selected solution back to the information terminal, converting it into a format that can be displayed to the user. This allows the user to obtain clear and easy-to-follow instructions and quickly resolve the problem.

[0277] As a concrete example, consider a case where a user reports a problem where "the printer is not printing." In this case, the server analyzes the keywords "printer" and "not printing" and suggests appropriate solutions such as reinstalling the driver or checking the cable connection. Through this process, the user can quickly resolve the problem and operate more smoothly.

[0278] The expected prompt message would be something like, "My printer won't start printing. Do I need to reinstall the driver?" The system will then automatically analyze the entered information and suggest the best solution.

[0279] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0280] Step 1:

[0281] The user accesses the chat interface via the information terminal. Here, the user inputs a technical problem such as "Wi-Fi cannot be connected" in text format and presses the send button. This input text is treated as an inquiry to the system, and the output is an inquiry in digital data format.

[0282] Step 2:

[0283] The terminal transmits the user's text input to the server as digital data. Here, the conversion to digital data is performed, and a connection to the server is established based on the transmission protocol. The output is the digital data transmitted to the server.

[0284] Step 3:

[0285] The server analyzes the received digital data using a natural language processing engine. Specifically, keyword extraction such as "Wi-Fi" and "cannot be connected" and problem identification are performed by text analysis. The input is digital data from the user, and the output is the list of keywords after analysis and the problem identification result.

[0286] Step 4:

[0287] The server refers to the knowledge database based on the analysis result. The database stores past problem-solving cases and searches for solutions to similar problems. The input is the keyword list, and the output is a plurality of solution candidates.

[0288] Step 5:

[0289] The server uses user characteristic information to select the optimal solution. This characteristic information includes the type of device being used and operating system information, and the solution is customized based on this information. The input is the candidate solution and user characteristic information, and the output is the selected optimal solution.

[0290] Step 6:

[0291] The server sends the selected solution as digital data to the terminal. This process involves data format conversion based on the transmission protocol. The input is the selected solution, and the output is the digital data sent to the terminal.

[0292] Step 7:

[0293] The terminal converts the received digital data into a format that is easy for the user to understand and displays it on the chat interface. The user then uses the displayed information to perform problem-solving steps. The input is digital data from the server, and the output is the solution displayed to the user. This process makes problem-solving easier for the user.

[0294] (Application Example 1)

[0295] 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 glasses 214 will be referred to as the "terminal."

[0296] In modern society, cybersecurity threats are increasing, and users face various security risks on a daily basis. In this environment, it is crucial that users can respond to security issues quickly and accurately. However, traditional methods often take a long time to identify security problems and provide appropriate solutions, and there is a need to improve this.

[0297] 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.

[0298] In this invention, the server includes means for receiving security-related problems entered by a user via a communication device, means for analyzing the received problems using natural language processing technology, and means for extracting multiple solutions from a knowledge base based on the analysis results. This enables the user to quickly and effectively identify and address security risks.

[0299] A "user" refers to an individual or organization that reports security-related issues to an information system via a communication device.

[0300] "Communication devices" are electronic devices used by users to input information and send it to a server, and include smartphones and smart glasses.

[0301] "Security-related issues" refer to cybersecurity threats or anomalies such as unauthorized access, phishing emails, and virus infections.

[0302] "Natural language processing technology" is a computer technology that analyzes text data received from users and understands its context.

[0303] A "knowledge base" is a database that stores past cases and best practices that are useful for solving security problems.

[0304] "User attribute information" refers to information about the type of communication device and operating system used by the user.

[0305] "Solutions" refer to specific countermeasures and preventative measures proposed for security-related issues.

[0306] A "server" is a computer system that analyzes received information and performs computational processing to provide solutions.

[0307] The system for realizing this invention is composed of a communication device, a server, and a knowledge-based database. It mainly provides a platform for quickly and effectively solving security-related problems faced by users.

[0308] Users use a communication device such as a smartphone or smart glasses to input security-related problems in text form. The communication device converts this text data into digital form and transmits it to the server.

[0309] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received text data. Through the analysis, based on the identified keywords and context information, the server grasps the content of the problem faced by the user.

[0310] Based on the analysis results, the server extracts relevant solutions from the knowledge base. The knowledge base accumulates security best practices and past cases. Furthermore, considering information such as the type of the user's communication device and operating system, the server selects an appropriate solution.

[0311] The selected solution is transmitted back to the user's communication device and displayed on the screen. Thereby, the user can quickly obtain the identification of the security problem and the coping method.

[0312] As a specific example, when the user reports that they "received a suspicious email", the server associates it with "phishing email" and provides a recommended measure of "delete the email and do not open the attached file" from the knowledge base.

[0313] As an example of a prompt sentence, for the user's input of "What should I do when I receive a suspicious email?", the server can present appropriate preventive measures. Thereby, the user can quickly and appropriately respond to the security risks faced in daily life.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user enters security-related issues in text format on a communication terminal. The entered text is converted into digital data by the communication terminal and sent to the server. During this process, prompts are appropriately formatted and processed into data that the server can easily understand.

[0317] Step 2:

[0318] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to extract security-related keywords and context from the input data. This process identifies the specific nature of the problem. For example, the keyword "suspicious email" might be extracted.

[0319] Step 3:

[0320] Based on the analysis results, the server extracts relevant solutions from its knowledge base. It searches for similar cases in the database to determine the best course of action against phishing emails. This determination then selects appropriate preventative measures and response procedures for the user.

[0321] Step 4:

[0322] The server references user attribute information and ultimately selects the optimal solution based on the platform and operating system of the communication device the user is using. For example, if an Android device is being used, Android-specific instructions will be reflected.

[0323] Step 5:

[0324] The selected solution is sent from the server to the communication terminal and presented to the user. The terminal displays the received information on the screen, allowing the user to act accordingly. This enables the user to quickly implement the recommended action.

[0325] 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.

[0326] This invention provides a system that uses an emotion engine to respond quickly and appropriately to technical questions from users. This system uses a communication terminal to input the user's challenges and technical problems in natural language.

[0327] When a user submits a question using the device's chat interface, the device sends its input data, device information, and operating system information to the server. Crucially, the server uses a sentiment engine to extract emotions from the user's text. The sentiment engine identifies positive, negative, and neutral emotions through text analysis.

[0328] The server analyzes the received text using a natural language processing engine. This analysis identifies the nature of the problem and the user's emotions. Then, it consults a knowledge database to extract multiple solutions related to the problem.

[0329] The extracted solutions are optimized based on user attribute information and perceived emotions. Specifically, if a user expresses negative emotions, the server prioritizes presenting support messages and reassuring procedures that align with those emotions. In addition, additional support information is provided as needed.

[0330] For example, if a user enters a negative message such as, "I'm really fed up with this app crashing every time," the server uses an emotion engine to detect this "fed up" emotion. Then, in addition to the usual solutions, a reassuring message is displayed first, including phrases like, "Don't worry, this is a common problem and will be fixed soon," to stabilize the user's emotions.

[0331] Finally, the selected solution is sent to the terminal, and the user receives and executes clear instructions to resolve the problem. By using this system, users can resolve their technical problems efficiently and with peace of mind.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] Users send technical problems as text input using the chat interface of their communication device. For example, they might type, "The app keeps crashing and it's frustrating."

[0335] Step 2:

[0336] The terminal sends the entered text data to the server. This data also includes the user's device information and operating system information.

[0337] Step 3:

[0338] The server passes the received text to the emotion engine, which analyzes the user's emotions. Here, the emotion "frustrated" is identified as negative.

[0339] Step 4:

[0340] The server uses a natural language processing engine to analyze user input and extract keywords related to the problem (such as "the app crashed").

[0341] Step 5:

[0342] The server consults a knowledge database and generates multiple relevant solutions based on the extracted keywords.

[0343] Step 6:

[0344] The server optimizes solutions based on user attribute information and sentiment analysis results. Since negative emotions have been detected, solutions that include messages to reassure the user are prioritized.

[0345] Step 7:

[0346] The server sends the selected solution to the terminal. The terminal receives it and displays the instructions and messages to the user in an easy-to-understand manner.

[0347] Step 8:

[0348] The user follows the displayed instructions to resolve the technical issue. The server receives feedback from the user as needed and prepares to take further action.

[0349] (Example 2)

[0350] 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".

[0351] In modern technical support systems, solutions provided without considering the user's feelings can amplify their anxiety and stress. This can result in a poor user experience and hinder prompt and appropriate problem resolution. There is a need to provide effective methods to address these challenges and improve user satisfaction.

[0352] 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.

[0353] In this invention, the server includes means for receiving questions entered by the user via an information processing device, means for analyzing the emotions contained in the received questions, and means for analyzing the text data using a natural language processing engine. This makes it possible to present solutions that take the user's emotions into consideration. With this system, users can solve technical problems more quickly and appropriately while feeling at ease.

[0354] An "information processing device" is a general term for a device used by a user for input and equipped with communication capabilities.

[0355] "Means of receiving" refers to a method or mechanism for acquiring data transmitted from an information processing device.

[0356] "Means of analyzing emotions" refers to technologies and processes for identifying and classifying the emotions contained in text received from users.

[0357] "Means of analyzing text data using a natural language processing engine" refers to a method that uses natural language processing techniques to analyze input text data and understand its meaning and relationships.

[0358] A "knowledge base" is a database that aggregates information useful for solving technical problems, and it contains past cases and specialized knowledge.

[0359] "User attribute information" refers to information related to individual users, including information about the devices they use and their operating environment.

[0360] "Means of selection" refers to the process of selecting and proposing the optimal solution based on the analyzed information.

[0361] "Reassuring supplementary information" refers to information added to a solution to prevent users from feeling anxious or stressed.

[0362] A "collection of past cases" refers to a data set that compiles records and examples of problem-solving that have been collected to date.

[0363] This invention is a system for efficiently and reliably solving users' technical problems. Users input technical questions in natural language using a communication terminal, which acts as an information processing device. This terminal can be, for example, a smartphone or a computer, and includes hardware capable of internet connectivity.

[0364] When a user enters a question through the terminal's interface and submits it, the terminal sends that information to the server. The information sent includes the entered text data, the type of device being used, and operating system information. The server first passes the received text to a sentiment analysis engine to identify positive, negative, or neutral emotions.

[0365] Next, the server uses a natural language processing engine to analyze the user's question and identify the nature of the technical problem. For example, if the input is "This app crashes every time," sentiment analysis will identify negative emotions, and the content will be analyzed to include "crash" and "app."

[0366] Based on the analysis results, the server consults a knowledge base and extracts multiple solutions related to the problem. The extracted solutions are optimized based on user attribute information and sentiment analysis results, and information that reassures the user is added. For example, a message such as "Don't worry, this is a common problem and can be solved quickly" is added.

[0367] Ultimately, the server sends this information back to the terminal, allowing the user to proceed with problem resolution based on the received solutions. This process provides prompt and appropriate support, enabling users to address technical issues with confidence.

[0368] As an example of a prompt, giving the model instructions such as, "Provide solutions for when a user is dissatisfied with app crashes," can help provide more appropriate and emotion-sensitive solutions.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user inputs a technical question in natural language into the input interface of the communication terminal and sends it. The input is text data containing a specific problem, such as "This app crashes every time." At this time, the terminal captures the information and obtains device information and operating system information. As output, the user's input text and technical information are compiled and ready to be sent to the server.

[0372] Step 2:

[0373] The terminal sends user input text, device information, and operating system information to the server. The input includes the data prepared in the previous step and is sent to the server. As output, the server receives this data and prepares it for analysis.

[0374] Step 3:

[0375] The server passes the received text data to the sentiment engine for sentiment analysis. The input is text data, and the analysis identifies positive, negative, and neutral emotions. Specifically, it extracts negative emotions from the word "fed up." The output is the result of the sentiment analysis, which is used in the next processing step.

[0376] Step 4:

[0377] The server uses a natural language processing engine to analyze text data. The input consists of text and sentiment analysis results. The text analysis identifies the nature of the technical problem. Specifically, keywords such as "crash" and "app" are extracted. The output generates data on the analyzed technical problem.

[0378] Step 5:

[0379] The server extracts solutions from its knowledge base based on the analyzed information. The input includes data on the technical problem, which is used to search for relevant solutions. Specifically, relevant past cases and recommended procedures are retrieved. The output is a list of multiple solutions.

[0380] Step 6:

[0381] The server optimizes solutions based on user attribute information and sentiment analysis results. The input is a list of solutions, which are filtered according to the user's attributes and emotions. Specifically, messages that provide reassurance in response to negative emotions are selected. The output is an optimized solution.

[0382] Step 7:

[0383] The server sends the optimized solution to the terminal. The input includes the final selected solution. The specific operation of sending involves transmitting the data to the terminal over the network. The output is that the terminal receives the solution and is ready to display it to the user.

[0384] (Application Example 2)

[0385] 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."

[0386] When users encounter technical problems, traditional systems mechanically offer solutions without considering the user's feelings, resulting in a failure to adequately enhance user satisfaction. Because the same approach is used even when dealing with users experiencing negative emotions, it fails to provide a sense of security and trust, ultimately hindering improvements in the user experience.

[0387] 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.

[0388] In this invention, the server includes means for receiving technical questions entered by the user via a communication device, means for analyzing the received questions using a natural language processing device, means for extracting multiple solutions from a database based on the analysis results, means for analyzing the user's emotions using an emotion recognition engine, means for selecting the optimal solution based on user attribute information and emotion analysis results, and means for presenting the user with a support message corresponding to the selected solution and emotions. This makes it possible to provide the best possible response according to the user's emotions and improve the user experience.

[0389] A "communication device" is a device that allows a user to input technical questions and transmit them to a system.

[0390] A "natural language processing unit" is a computer system that analyzes text data received from a user and interprets its meaning.

[0391] A "database" is a system that stores past solutions and information, and is used to extract information for problem-solving based on analysis results.

[0392] An "emotion recognition engine" is software that analyzes the emotions contained in user input and identifies that emotional state.

[0393] "User attribute information" refers to information related to the type of equipment and operating system used by the user, and serves as basic data for making optimal suggestions to the user.

[0394] A "solution" is a method or procedure for solving a problem that the system proposes to the user based on the results of its analysis.

[0395] A "support message" is a communication message provided in response to the user's emotional state, designed to convey a sense of stability and trust.

[0396] The system implementing this invention aims to efficiently process users' technical questions and provide appropriate solutions based on sentiment analysis. Specific embodiments are described below.

[0397] When a user enters a technical question using a communication device, the data is sent to the server. The server uses a natural language processing unit (NLTK) to analyze the input text and identify the problem being addressed. This analysis uses Python programs and natural language processing libraries such as NLTK and spaCy.

[0398] For identified problems, the server extracts multiple solutions from the database. The database also includes past case data, enabling efficient information extraction.

[0399] Next, the server activates its emotion recognition engine to analyze the user's emotions from the input text. This analysis is performed using text analysis tools such as TextBlob and TensorFlow.

[0400] The server then considers the user's attribute information and the results of the sentiment analysis to select the optimal solution. If the user is showing negative emotions, a support message is generated simultaneously to provide the user with a sense of reassurance.

[0401] For example, if a user asks a question such as, "I want to know why my order was canceled," the system uses an emotion recognition engine to read the underlying anxiety arising from that question and, accordingly, adds a supportive message such as, "We apologize for the inconvenience. You can find more details about your order cancellation here."

[0402] An example of a prompt for a generative AI model might be, "If the user is feeling uncomfortable, please suggest how to respond." This allows the server to present the AI ​​model with prompts that are relevant to the user's emotions, enabling it to create more appropriate responses.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The user inputs a technical question via a communication device, and the terminal sends that text data to the server. The input is the user's question, and the output is the unparsed text data received by the server.

[0406] Step 2:

[0407] The server uses a natural language processing unit to analyze the received text data. This analysis reveals the intent behind the user's question and specific technical problems. The input is unanalyzed text data, and the output is structured data containing the problematic information.

[0408] Step 3:

[0409] The server extracts relevant solutions from the database based on structured data. The input is structured data related to the problem, and the output is a list containing multiple solutions. Database searches are performed by cross-referencing with past cases.

[0410] Step 4:

[0411] The server uses an emotion recognition engine to analyze the user's emotions from the question text. The input is unanalyzed text data, and the output is the user's emotional state (positive, negative, or neutral). Text analysis tools are used for emotion analysis.

[0412] Step 5:

[0413] The server selects the optimal solution based on user attribute information and emotional state. Inputs are user attribute information, emotional state, and a list of solutions; output is the optimized solution. Selection criteria include the user's environment (device, software, etc.).

[0414] Step 6:

[0415] The server generates and presents to the user a support message tailored to the selected solution and the user's emotional state. The input is the optimized solution and emotional state, while the output is a response message to the user. The support message incorporates content that addresses the user's emotional state.

[0416] Step 7:

[0417] The user receives the proposed solutions and support messages and takes the actions necessary to resolve the problem. The input is the response message, and the output is the user's actions and the progress of problem resolution. This completes the entire system cycle.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] The present invention provides a system that offers rapid and accurate solutions to technical support questions submitted by users via a communication terminal. The details of its embodiments are described below.

[0435] Users access the chat interface using communication devices such as smartphones or personal computers. Through this interface, users input technical problems in text format and send them to the system.

[0436] This input is sent to the server as digital data by the terminal. The server analyzes the received input using a natural language processing engine to identify the problem the user is facing. For example, if a user reports a problem such as "I can't connect to Wi-Fi," the server analyzes this information, extracts keywords such as "Wi-Fi" and "cannot connect," and understands the context.

[0437] Next, the server consults a knowledge database to extract solutions for similar problems. This database contains past case studies and common troubleshooting procedures, which the server uses to generate solutions. For example, "restarting the router" or "reviewing Wi-Fi settings" might be extracted as solutions.

[0438] Furthermore, the server selects the optimal solution by considering user attribute information, namely the type of device being used and its operating system information. For example, for a user using Windows 10, a Windows-specific configuration verification procedure will be suggested.

[0439] Ultimately, the server sends the selected solution to the terminal and displays it clearly to the user. The user then follows the suggested steps to resolve the problem. In this way, the system allows users to resolve technical problems easily and efficiently. Furthermore, since the system operates 24 hours a day, technical support services are available at any time.

[0440] For example, if a user reports a problem such as "the printer is not printing," the server analyzes the keywords "printer" and "not printing" and suggests solutions appropriate to the situation, such as reinstalling the driver or checking the cable connection. This allows for a consistent user experience and problem resolution.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] Users input technical problems as text using a chat interface from their communication terminal. For example, "The printer isn't printing."

[0444] Step 2:

[0445] The terminal sends user input to the server. Along with this input, the terminal's device information and operating system information are also sent.

[0446] Step 3:

[0447] The server analyzes the received user input using a natural language processing engine. This analysis extracts keywords from the text (for example, "printer" or "do not print") to identify the content of the problem.

[0448] Step 4:

[0449] The server consults a knowledge database based on the analysis and extracts relevant solutions from past cases. General troubleshooting procedures are also considered.

[0450] Step 5:

[0451] The server selects the most suitable solution from the extracted options based on user attribute information. Solutions are filtered according to the user's device type and operating system.

[0452] Step 6:

[0453] The server sends the selected solution to the terminal. The terminal receives this information and displays the steps to the user in an easy-to-understand manner.

[0454] Step 7:

[0455] The user attempts to resolve the issue themselves by following the provided steps. The user solves the technical problem by performing the operations sequentially.

[0456] (Example 1)

[0457] 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."

[0458] In today's information society, users face a variety of technical problems every day. Solving these problems quickly and efficiently is crucial for improving the convenience of users' lives and work. However, traditional systems required users to go through multiple steps, making the problem-solving process cumbersome and inefficient. This resulted in a poor user experience and prolonged problem-solving times.

[0459] 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.

[0460] In this invention, the server includes means for receiving inquiries entered by the user via an information terminal, means for analyzing the received inquiries with a natural language processing engine and extracting keywords to identify the problem, means for searching a database for multiple solutions based on the analysis results, means for selecting the optimal solution based on user characteristic information, and means for displaying the selected solution to the user. This enables the user to solve technical problems more quickly and easily.

[0461] A "user" is someone who makes inquiries to the system via an information terminal.

[0462] An "information terminal" is a device used by users to input inquiries and communicate with a system, and includes smartphones, personal computers, and other similar devices.

[0463] An "inquiry" refers to a technical problem or question sent to the system by a user via an information terminal.

[0464] A "server" is a central computing device that analyzes inquiries received from users, generates solutions, and provides them to the users.

[0465] A "natural language processing engine" is a software component that analyzes user inquiries to extract keywords and identify problems.

[0466] A "keyword" is an important word or phrase identified during the analysis process of a query, which indicates the characteristics of the problem.

[0467] A "database" is a collection of information that stores past examples and solutions, and is used to search for solutions based on analysis results.

[0468] "User characteristic information" refers to attribute data that includes information about the type of device the user is using and the operating system.

[0469] A "solution" is a suggestion or procedure to resolve a problem identified in response to a user inquiry.

[0470] "Display" refers to a method of communicating the selected solution to the user in an easy-to-understand manner, and is typically done through the screen of an information terminal or similar device.

[0471] This invention is a system that provides rapid and efficient support when a user requests technical assistance via an information terminal. The user uses an information terminal such as a smartphone or personal computer to connect to the system via a chat interface. When the user inputs a technical problem in text format, the information terminal sends this input data to the server.

[0472] The server analyzes the received digital data using a natural language processing engine. This engine extracts keywords and identifies the user's problem. Specifically, for common problems such as "Wi-Fi cannot connect," it extracts relevant keywords and understands the appropriate context. This analysis utilizes advanced generative AI models to achieve highly accurate problem understanding.

[0473] Once the analysis is complete, the server consults a knowledge database, searching for multiple solutions based on similar past cases and troubleshooting procedures. This database contains solutions to common problems and past success stories, which the server uses to quickly provide effective solutions.

[0474] Furthermore, the server takes user characteristics information into account. This includes the type of device the user is using and information about their operating system, and this data helps to customize the solution. For example, a user using Windows 10 will be offered platform-specific instructions.

[0475] Finally, the server sends the selected solution back to the information terminal, converting it into a format that can be displayed to the user. This allows the user to obtain clear and easy-to-follow instructions and quickly resolve the problem.

[0476] As a concrete example, consider a case where a user reports a problem where "the printer is not printing." In this case, the server analyzes the keywords "printer" and "not printing" and suggests appropriate solutions such as reinstalling the driver or checking the cable connection. Through this process, the user can quickly resolve the problem and operate more smoothly.

[0477] The expected prompt message would be something like, "My printer won't start printing. Do I need to reinstall the driver?" The system will then automatically analyze the entered information and suggest the best solution.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user accesses the chat interface via an information terminal. Here, the user enters technical problems, such as "Wi-Fi connection failed," in text format and presses the send button. This input text is treated as a query to the system. The output is the query in digital data format.

[0481] Step 2:

[0482] The terminal sends the user's text input as digital data to the server. Here, the data is converted to digital format, and a connection to the server is established based on the transmission protocol. The output is the digital data sent to the server.

[0483] Step 3:

[0484] The server analyzes the received digital data using a natural language processing engine. Specifically, it extracts keywords such as "Wi-Fi" and "cannot connect" through text analysis and identifies the problem. The input is digital data from the user, and the output is a list of analyzed keywords and the results of the problem identification.

[0485] Step 4:

[0486] The server references a knowledge database based on the analysis results. The database contains past problem-solving examples, and the server searches for solutions to similar problems. The input is a list of keywords, and the output is multiple potential solutions.

[0487] Step 5:

[0488] The server uses user characteristic information to select the optimal solution. This characteristic information includes the type of device being used and operating system information, and the solution is customized based on this information. The input is the candidate solution and user characteristic information, and the output is the selected optimal solution.

[0489] Step 6:

[0490] The server sends the selected solution as digital data to the terminal. This process involves data format conversion based on the transmission protocol. The input is the selected solution, and the output is the digital data sent to the terminal.

[0491] Step 7:

[0492] The terminal converts the received digital data into a format that is easy for the user to understand and displays it on the chat interface. The user then uses the displayed information to perform problem-solving steps. The input is digital data from the server, and the output is the solution displayed to the user. This process makes problem-solving easier for the user.

[0493] (Application Example 1)

[0494] 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."

[0495] In modern society, cybersecurity threats are increasing, and users face various security risks on a daily basis. In this environment, it is crucial that users can respond to security issues quickly and accurately. However, traditional methods often take a long time to identify security problems and provide appropriate solutions, and there is a need to improve this.

[0496] 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.

[0497] In this invention, the server includes means for receiving security-related problems entered by a user via a communication device, means for analyzing the received problems using natural language processing technology, and means for extracting multiple solutions from a knowledge base based on the analysis results. This enables the user to quickly and effectively identify and address security risks.

[0498] A "user" refers to an individual or organization that reports security-related issues to an information system via a communication device.

[0499] "Communication devices" are electronic devices used by users to input information and send it to a server, and include smartphones and smart glasses.

[0500] "Security-related issues" refer to cybersecurity threats or anomalies such as unauthorized access, phishing emails, and virus infections.

[0501] "Natural language processing technology" is a computer technology that analyzes text data received from users and understands its context.

[0502] A "knowledge base" is a database that stores past cases and best practices that are useful for solving security problems.

[0503] "User attribute information" refers to information about the type of communication device and operating system used by the user.

[0504] "Solutions" refer to specific countermeasures and preventative measures proposed for security-related issues.

[0505] A "server" is a computer system that analyzes received information and performs computational processing to provide solutions.

[0506] The system for realizing this invention consists of a communication device, a server, and a knowledge-based database. Its primary purpose is to provide a platform for quickly and effectively resolving security-related issues faced by users.

[0507] Users use communication devices such as smartphones or smart glasses to input security-related issues in text format. The communication device converts this text data into a digital format and sends it to the server.

[0508] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received text data. Based on the keywords and contextual information identified through the analysis, it understands the nature of the problem the user is facing.

[0509] Based on the analysis results, the server extracts relevant solutions from its knowledge base. This knowledge base contains security best practices and past case studies. Furthermore, it considers information such as the user's communication device type and operating system to select the appropriate solution.

[0510] The selected solution is then sent back to the user's communication device and displayed on the screen. This allows the user to quickly identify and address security issues.

[0511] For example, if a user reports receiving a suspicious email, the server will associate it with a phishing email and provide a recommendation from its knowledge base to "delete the email and not open any attachments."

[0512] As an example of a prompt, in response to a user input such as, "What should I do if I receive a suspicious email?", the server can suggest appropriate preventative measures. This allows users to respond quickly and appropriately to security risks they face on a daily basis.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user enters security-related issues in text format on a communication terminal. The entered text is converted into digital data by the communication terminal and sent to the server. During this process, prompts are appropriately formatted and processed into data that the server can easily understand.

[0516] Step 2:

[0517] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to extract security-related keywords and context from the input data. This process identifies the specific nature of the problem. For example, the keyword "suspicious email" might be extracted.

[0518] Step 3:

[0519] Based on the analysis results, the server extracts relevant solutions from its knowledge base. It searches for similar cases in the database to determine the best course of action against phishing emails. This determination then selects appropriate preventative measures and response procedures for the user.

[0520] Step 4:

[0521] The server references user attribute information and ultimately selects the optimal solution based on the platform and operating system of the communication device the user is using. For example, if an Android device is being used, Android-specific instructions will be reflected.

[0522] Step 5:

[0523] The selected solution is sent from the server to the communication terminal and presented to the user. The terminal displays the received information on the screen, allowing the user to act accordingly. This enables the user to quickly implement the recommended action.

[0524] 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.

[0525] This invention provides a system that uses an emotion engine to respond quickly and appropriately to technical questions from users. This system uses a communication terminal to input the user's challenges and technical problems in natural language.

[0526] When a user submits a question using the device's chat interface, the device sends its input data, device information, and operating system information to the server. Crucially, the server uses a sentiment engine to extract emotions from the user's text. The sentiment engine identifies positive, negative, and neutral emotions through text analysis.

[0527] The server analyzes the received text using a natural language processing engine. This analysis identifies the nature of the problem and the user's emotions. Then, it consults a knowledge database to extract multiple solutions related to the problem.

[0528] The extracted solutions are optimized based on user attribute information and perceived emotions. Specifically, if a user expresses negative emotions, the server prioritizes presenting support messages and reassuring procedures that align with those emotions. In addition, additional support information is provided as needed.

[0529] For example, if a user enters a negative message such as, "I'm really fed up with this app crashing every time," the server uses an emotion engine to detect this "fed up" emotion. Then, in addition to the usual solutions, a reassuring message is displayed first, including phrases like, "Don't worry, this is a common problem and will be fixed soon," to stabilize the user's emotions.

[0530] Finally, the selected solution is sent to the terminal, and the user receives and executes clear instructions to resolve the problem. By using this system, users can resolve their technical problems efficiently and with peace of mind.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] Users send technical problems as text input using the chat interface of their communication device. For example, they might type, "The app keeps crashing and it's frustrating."

[0534] Step 2:

[0535] The terminal sends the entered text data to the server. This data also includes the user's device information and operating system information.

[0536] Step 3:

[0537] The server passes the received text to the emotion engine, which analyzes the user's emotions. Here, the emotion "frustrated" is identified as negative.

[0538] Step 4:

[0539] The server uses a natural language processing engine to analyze user input and extract keywords related to the problem (such as "the app crashed").

[0540] Step 5:

[0541] The server consults a knowledge database and generates multiple relevant solutions based on the extracted keywords.

[0542] Step 6:

[0543] The server optimizes solutions based on user attribute information and sentiment analysis results. Since negative emotions have been detected, solutions that include messages to reassure the user are prioritized.

[0544] Step 7:

[0545] The server sends the selected solution to the terminal. The terminal receives it and displays the instructions and messages to the user in an easy-to-understand manner.

[0546] Step 8:

[0547] The user follows the displayed instructions to resolve the technical issue. The server receives feedback from the user as needed and prepares to take further action.

[0548] (Example 2)

[0549] 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."

[0550] In modern technical support systems, solutions provided without considering the user's feelings can amplify their anxiety and stress. This can result in a poor user experience and hinder prompt and appropriate problem resolution. There is a need to provide effective methods to address these challenges and improve user satisfaction.

[0551] 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.

[0552] In this invention, the server includes means for receiving questions entered by the user via an information processing device, means for analyzing the emotions contained in the received questions, and means for analyzing the text data using a natural language processing engine. This makes it possible to present solutions that take the user's emotions into consideration. With this system, users can solve technical problems more quickly and appropriately while feeling at ease.

[0553] An "information processing device" is a general term for a device used by a user for input and equipped with communication capabilities.

[0554] "Means of receiving" refers to a method or mechanism for acquiring data transmitted from an information processing device.

[0555] "Means of analyzing emotions" refers to technologies and processes for identifying and classifying the emotions contained in text received from users.

[0556] "Means of analyzing text data using a natural language processing engine" refers to a method that uses natural language processing techniques to analyze input text data and understand its meaning and relationships.

[0557] A "knowledge base" is a database that aggregates information useful for solving technical problems, and it contains past cases and specialized knowledge.

[0558] "User attribute information" refers to information related to individual users, including information about the devices they use and their operating environment.

[0559] "Means of selection" refers to the process of selecting and proposing the optimal solution based on the analyzed information.

[0560] "Reassuring supplementary information" refers to information added to a solution to prevent users from feeling anxious or stressed.

[0561] A "collection of past cases" refers to a data set that compiles records and examples of problem-solving that have been collected to date.

[0562] This invention is a system for efficiently and reliably solving users' technical problems. Users input technical questions in natural language using a communication terminal, which acts as an information processing device. This terminal can be, for example, a smartphone or a computer, and includes hardware capable of internet connectivity.

[0563] When a user enters a question through the terminal's interface and submits it, the terminal sends that information to the server. The information sent includes the entered text data, the type of device being used, and operating system information. The server first passes the received text to a sentiment analysis engine to identify positive, negative, or neutral emotions.

[0564] Next, the server uses a natural language processing engine to analyze the user's question and identify the nature of the technical problem. For example, if the input is "This app crashes every time," sentiment analysis will identify negative emotions, and the content will be analyzed to include "crash" and "app."

[0565] Based on the analysis results, the server consults a knowledge base and extracts multiple solutions related to the problem. The extracted solutions are optimized based on user attribute information and sentiment analysis results, and information that reassures the user is added. For example, a message such as "Don't worry, this is a common problem and can be solved quickly" is added.

[0566] Ultimately, the server sends this information back to the terminal, allowing the user to proceed with problem resolution based on the received solutions. This process provides prompt and appropriate support, enabling users to address technical issues with confidence.

[0567] As an example of a prompt, giving the model instructions such as, "Provide solutions for when a user is dissatisfied with app crashes," can help provide more appropriate and emotion-sensitive solutions.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The user inputs a technical question in natural language into the input interface of the communication terminal and sends it. The input is text data containing a specific problem, such as "This app crashes every time." At this time, the terminal captures the information and obtains device information and operating system information. As output, the user's input text and technical information are compiled and ready to be sent to the server.

[0571] Step 2:

[0572] The terminal sends user input text, device information, and operating system information to the server. The input includes the data prepared in the previous step and is sent to the server. As output, the server receives this data and prepares it for analysis.

[0573] Step 3:

[0574] The server passes the received text data to the sentiment engine for sentiment analysis. The input is text data, and the analysis identifies positive, negative, and neutral emotions. Specifically, it extracts negative emotions from the word "fed up." The output is the result of the sentiment analysis, which is used in the next processing step.

[0575] Step 4:

[0576] The server uses a natural language processing engine to analyze text data. The input consists of text and sentiment analysis results. The text analysis identifies the nature of the technical problem. Specifically, keywords such as "crash" and "app" are extracted. The output generates data on the analyzed technical problem.

[0577] Step 5:

[0578] The server extracts solutions from its knowledge base based on the analyzed information. The input includes data on the technical problem, which is used to search for relevant solutions. Specifically, relevant past cases and recommended procedures are retrieved. The output is a list of multiple solutions.

[0579] Step 6:

[0580] The server optimizes solutions based on user attribute information and sentiment analysis results. The input is a list of solutions, which are filtered according to the user's attributes and emotions. Specifically, messages that provide reassurance in response to negative emotions are selected. The output is an optimized solution.

[0581] Step 7:

[0582] The server sends the optimized solution to the terminal. The input includes the final selected solution. The specific operation of sending involves transmitting the data to the terminal over the network. The output is that the terminal receives the solution and is ready to display it to the user.

[0583] (Application Example 2)

[0584] 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."

[0585] When users encounter technical problems, traditional systems mechanically offer solutions without considering the user's feelings, resulting in a failure to adequately enhance user satisfaction. Because the same approach is used even when dealing with users experiencing negative emotions, it fails to provide a sense of security and trust, ultimately hindering improvements in the user experience.

[0586] 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.

[0587] In this invention, the server includes means for receiving technical questions entered by the user via a communication device, means for analyzing the received questions using a natural language processing device, means for extracting multiple solutions from a database based on the analysis results, means for analyzing the user's emotions using an emotion recognition engine, means for selecting the optimal solution based on user attribute information and emotion analysis results, and means for presenting the user with a support message corresponding to the selected solution and emotions. This makes it possible to provide the best possible response according to the user's emotions and improve the user experience.

[0588] A "communication device" is a device that allows a user to input technical questions and transmit them to a system.

[0589] A "natural language processing unit" is a computer system that analyzes text data received from a user and interprets its meaning.

[0590] A "database" is a system that stores past solutions and information, and is used to extract information for problem-solving based on analysis results.

[0591] An "emotion recognition engine" is software that analyzes the emotions contained in user input and identifies that emotional state.

[0592] "User attribute information" refers to information related to the type of equipment and operating system used by the user, and serves as basic data for making optimal suggestions to the user.

[0593] A "solution" is a method or procedure for solving a problem that the system proposes to the user based on the results of its analysis.

[0594] A "support message" is a communication message provided in response to the user's emotional state, designed to convey a sense of stability and trust.

[0595] The system implementing this invention aims to efficiently process users' technical questions and provide appropriate solutions based on sentiment analysis. Specific embodiments are described below.

[0596] When a user enters a technical question using a communication device, the data is sent to the server. The server uses a natural language processing unit (NLTK) to analyze the input text and identify the problem being addressed. This analysis uses Python programs and natural language processing libraries such as NLTK and spaCy.

[0597] For identified problems, the server extracts multiple solutions from the database. The database also includes past case data, enabling efficient information extraction.

[0598] Next, the server activates its emotion recognition engine to analyze the user's emotions from the input text. This analysis is performed using text analysis tools such as TextBlob and TensorFlow.

[0599] The server then considers the user's attribute information and the results of the sentiment analysis to select the optimal solution. If the user is showing negative emotions, a support message is generated simultaneously to provide the user with a sense of reassurance.

[0600] For example, if a user asks a question such as, "I want to know why my order was canceled," the system uses an emotion recognition engine to read the underlying anxiety arising from that question and, accordingly, adds a supportive message such as, "We apologize for the inconvenience. You can find more details about your order cancellation here."

[0601] An example of a prompt for a generative AI model might be, "If the user is feeling uncomfortable, please suggest how to respond." This allows the server to present the AI ​​model with prompts that are relevant to the user's emotions, enabling it to create more appropriate responses.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The user inputs a technical question via a communication device, and the terminal sends that text data to the server. The input is the user's question, and the output is the unparsed text data received by the server.

[0605] Step 2:

[0606] The server uses a natural language processing unit to analyze the received text data. This analysis reveals the intent behind the user's question and specific technical problems. The input is unanalyzed text data, and the output is structured data containing the problematic information.

[0607] Step 3:

[0608] The server extracts relevant solutions from the database based on structured data. The input is structured data related to the problem, and the output is a list containing multiple solutions. Database searches are performed by cross-referencing with past cases.

[0609] Step 4:

[0610] The server uses an emotion recognition engine to analyze the user's emotions from the question text. The input is unanalyzed text data, and the output is the user's emotional state (positive, negative, or neutral). Text analysis tools are used for emotion analysis.

[0611] Step 5:

[0612] The server selects the optimal solution based on user attribute information and emotional state. Inputs are user attribute information, emotional state, and a list of solutions; output is the optimized solution. Selection criteria include the user's environment (device, software, etc.).

[0613] Step 6:

[0614] The server generates and presents to the user a support message tailored to the selected solution and the user's emotional state. The input is the optimized solution and emotional state, while the output is a response message to the user. The support message incorporates content that addresses the user's emotional state.

[0615] Step 7:

[0616] The user receives the proposed solutions and support messages and takes the actions necessary to resolve the problem. The input is the response message, and the output is the user's actions and the progress of problem resolution. This completes the entire system cycle.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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".

[0634] The present invention provides a system that offers rapid and accurate solutions to technical support questions submitted by users via a communication terminal. The details of its embodiments are described below.

[0635] Users access the chat interface using communication devices such as smartphones or personal computers. Through this interface, users input technical problems in text format and send them to the system.

[0636] This input is sent to the server as digital data by the terminal. The server analyzes the received input using a natural language processing engine to identify the problem the user is facing. For example, if a user reports a problem such as "I can't connect to Wi-Fi," the server analyzes this information, extracts keywords such as "Wi-Fi" and "cannot connect," and understands the context.

[0637] Next, the server consults a knowledge database to extract solutions for similar problems. This database contains past case studies and common troubleshooting procedures, which the server uses to generate solutions. For example, "restarting the router" or "reviewing Wi-Fi settings" might be extracted as solutions.

[0638] Furthermore, the server selects the optimal solution by considering user attribute information, namely the type of device being used and its operating system information. For example, for a user using Windows 10, a Windows-specific configuration verification procedure will be suggested.

[0639] Ultimately, the server sends the selected solution to the terminal and displays it clearly to the user. The user then follows the suggested steps to resolve the problem. In this way, the system allows users to resolve technical problems easily and efficiently. Furthermore, since the system operates 24 hours a day, technical support services are available at any time.

[0640] For example, if a user reports a problem such as "the printer is not printing," the server analyzes the keywords "printer" and "not printing" and suggests solutions appropriate to the situation, such as reinstalling the driver or checking the cable connection. This allows for a consistent user experience and problem resolution.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Users input technical problems as text using a chat interface from their communication terminal. For example, "The printer isn't printing."

[0644] Step 2:

[0645] The terminal sends user input to the server. Along with this input, the terminal's device information and operating system information are also sent.

[0646] Step 3:

[0647] The server analyzes the received user input using a natural language processing engine. This analysis extracts keywords from the text (for example, "printer" or "do not print") to identify the content of the problem.

[0648] Step 4:

[0649] The server consults a knowledge database based on the analysis and extracts relevant solutions from past cases. General troubleshooting procedures are also considered.

[0650] Step 5:

[0651] The server selects the most suitable solution from the extracted options based on user attribute information. Solutions are filtered according to the user's device type and operating system.

[0652] Step 6:

[0653] The server sends the selected solution to the terminal. The terminal receives this information and displays the steps to the user in an easy-to-understand manner.

[0654] Step 7:

[0655] The user attempts to resolve the issue themselves by following the provided steps. The user solves the technical problem by performing the operations sequentially.

[0656] (Example 1)

[0657] 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".

[0658] In today's information society, users face a variety of technical problems every day. Solving these problems quickly and efficiently is crucial for improving the convenience of users' lives and work. However, traditional systems required users to go through multiple steps, making the problem-solving process cumbersome and inefficient. This resulted in a poor user experience and prolonged problem-solving times.

[0659] 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.

[0660] In this invention, the server includes means for receiving inquiries entered by the user via an information terminal, means for analyzing the received inquiries with a natural language processing engine and extracting keywords to identify the problem, means for searching a database for multiple solutions based on the analysis results, means for selecting the optimal solution based on user characteristic information, and means for displaying the selected solution to the user. This enables the user to solve technical problems more quickly and easily.

[0661] A "user" is someone who makes inquiries to the system via an information terminal.

[0662] An "information terminal" is a device used by users to input inquiries and communicate with a system, and includes smartphones, personal computers, and other similar devices.

[0663] An "inquiry" refers to a technical problem or question sent to the system by a user via an information terminal.

[0664] A "server" is a central computing device that analyzes inquiries received from users, generates solutions, and provides them to the users.

[0665] A "natural language processing engine" is a software component that analyzes user inquiries to extract keywords and identify problems.

[0666] A "keyword" is an important word or phrase identified during the analysis process of a query, which indicates the characteristics of the problem.

[0667] A "database" is a collection of information that stores past examples and solutions, and is used to search for solutions based on analysis results.

[0668] "User characteristic information" refers to attribute data that includes information about the type of device the user is using and the operating system.

[0669] A "solution" is a suggestion or procedure to resolve a problem identified in response to a user inquiry.

[0670] "Display" refers to a method of communicating the selected solution to the user in an easy-to-understand manner, and is typically done through the screen of an information terminal or similar device.

[0671] This invention is a system that provides rapid and efficient support when a user requests technical assistance via an information terminal. The user uses an information terminal such as a smartphone or personal computer to connect to the system via a chat interface. When the user inputs a technical problem in text format, the information terminal sends this input data to the server.

[0672] The server analyzes the received digital data using a natural language processing engine. This engine extracts keywords and identifies the user's problem. Specifically, for common problems such as "Wi-Fi cannot connect," it extracts relevant keywords and understands the appropriate context. This analysis utilizes advanced generative AI models to achieve highly accurate problem understanding.

[0673] Once the analysis is complete, the server consults a knowledge database, searching for multiple solutions based on similar past cases and troubleshooting procedures. This database contains solutions to common problems and past success stories, which the server uses to quickly provide effective solutions.

[0674] Furthermore, the server takes user characteristics information into account. This includes the type of device the user is using and information about their operating system, and this data helps to customize the solution. For example, a user using Windows 10 will be offered platform-specific instructions.

[0675] Finally, the server sends the selected solution back to the information terminal, converting it into a format that can be displayed to the user. This allows the user to obtain clear and easy-to-follow instructions and quickly resolve the problem.

[0676] As a concrete example, consider a case where a user reports a problem where "the printer is not printing." In this case, the server analyzes the keywords "printer" and "not printing" and suggests appropriate solutions such as reinstalling the driver or checking the cable connection. Through this process, the user can quickly resolve the problem and operate more smoothly.

[0677] The expected prompt message would be something like, "My printer won't start printing. Do I need to reinstall the driver?" The system will then automatically analyze the entered information and suggest the best solution.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user accesses the chat interface via an information terminal. Here, the user enters technical problems, such as "Wi-Fi connection failed," in text format and presses the send button. This input text is treated as a query to the system. The output is the query in digital data format.

[0681] Step 2:

[0682] The terminal sends the user's text input as digital data to the server. Here, the data is converted to digital format, and a connection to the server is established based on the transmission protocol. The output is the digital data sent to the server.

[0683] Step 3:

[0684] The server analyzes the received digital data using a natural language processing engine. Specifically, it extracts keywords such as "Wi-Fi" and "cannot connect" through text analysis and identifies the problem. The input is digital data from the user, and the output is a list of analyzed keywords and the results of the problem identification.

[0685] Step 4:

[0686] The server references a knowledge database based on the analysis results. The database contains past problem-solving examples, and the server searches for solutions to similar problems. The input is a list of keywords, and the output is multiple potential solutions.

[0687] Step 5:

[0688] The server uses user characteristic information to select the optimal solution. This characteristic information includes the type of device being used and operating system information, and the solution is customized based on this information. The input is the candidate solution and user characteristic information, and the output is the selected optimal solution.

[0689] Step 6:

[0690] The server sends the selected solution as digital data to the terminal. This process involves data format conversion based on the transmission protocol. The input is the selected solution, and the output is the digital data sent to the terminal.

[0691] Step 7:

[0692] The terminal converts the received digital data into a format that is easy for the user to understand and displays it on the chat interface. The user then uses the displayed information to perform problem-solving steps. The input is digital data from the server, and the output is the solution displayed to the user. This process makes problem-solving easier for the user.

[0693] (Application Example 1)

[0694] 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".

[0695] In modern society, cybersecurity threats are increasing, and users face various security risks on a daily basis. In this environment, it is crucial that users can respond to security issues quickly and accurately. However, traditional methods often take a long time to identify security problems and provide appropriate solutions, and there is a need to improve this.

[0696] 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.

[0697] In this invention, the server includes means for receiving security-related problems entered by a user via a communication device, means for analyzing the received problems using natural language processing technology, and means for extracting multiple solutions from a knowledge base based on the analysis results. This enables the user to quickly and effectively identify and address security risks.

[0698] A "user" refers to an individual or organization that reports security-related issues to an information system via a communication device.

[0699] "Communication devices" are electronic devices used by users to input information and send it to a server, and include smartphones and smart glasses.

[0700] "Security-related issues" refer to cybersecurity threats or anomalies such as unauthorized access, phishing emails, and virus infections.

[0701] "Natural language processing technology" is a computer technology that analyzes text data received from users and understands its context.

[0702] A "knowledge base" is a database that stores past cases and best practices that are useful for solving security problems.

[0703] "User attribute information" refers to information about the type of communication device and operating system used by the user.

[0704] "Solutions" refer to specific countermeasures and preventative measures proposed for security-related issues.

[0705] A "server" is a computer system that analyzes received information and performs computational processing to provide solutions.

[0706] The system for realizing this invention consists of a communication device, a server, and a knowledge-based database. Its primary purpose is to provide a platform for quickly and effectively resolving security-related issues faced by users.

[0707] Users use communication devices such as smartphones or smart glasses to input security-related issues in text format. The communication device converts this text data into a digital format and sends it to the server.

[0708] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received text data. Based on the keywords and contextual information identified through the analysis, it understands the nature of the problem the user is facing.

[0709] Based on the analysis results, the server extracts relevant solutions from its knowledge base. This knowledge base contains security best practices and past case studies. Furthermore, it considers information such as the user's communication device type and operating system to select the appropriate solution.

[0710] The selected solution is then sent back to the user's communication device and displayed on the screen. This allows the user to quickly identify and address security issues.

[0711] For example, if a user reports receiving a suspicious email, the server will associate it with a phishing email and provide a recommendation from its knowledge base to "delete the email and not open any attachments."

[0712] As an example of a prompt, in response to a user input such as, "What should I do if I receive a suspicious email?", the server can suggest appropriate preventative measures. This allows users to respond quickly and appropriately to security risks they face on a daily basis.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user enters security-related issues in text format on a communication terminal. The entered text is converted into digital data by the communication terminal and sent to the server. During this process, prompts are appropriately formatted and processed into data that the server can easily understand.

[0716] Step 2:

[0717] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to extract security-related keywords and context from the input data. This process identifies the specific nature of the problem. For example, the keyword "suspicious email" might be extracted.

[0718] Step 3:

[0719] Based on the analysis results, the server extracts relevant solutions from its knowledge base. It searches for similar cases in the database to determine the best course of action against phishing emails. This determination then selects appropriate preventative measures and response procedures for the user.

[0720] Step 4:

[0721] The server references user attribute information and ultimately selects the optimal solution based on the platform and operating system of the communication device the user is using. For example, if an Android device is being used, Android-specific instructions will be reflected.

[0722] Step 5:

[0723] The selected solution is sent from the server to the communication terminal and presented to the user. The terminal displays the received information on the screen, allowing the user to act accordingly. This enables the user to quickly implement the recommended action.

[0724] 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.

[0725] This invention provides a system that uses an emotion engine to respond quickly and appropriately to technical questions from users. This system uses a communication terminal to input the user's challenges and technical problems in natural language.

[0726] When a user submits a question using the device's chat interface, the device sends its input data, device information, and operating system information to the server. Crucially, the server uses a sentiment engine to extract emotions from the user's text. The sentiment engine identifies positive, negative, and neutral emotions through text analysis.

[0727] The server analyzes the received text using a natural language processing engine. This analysis identifies the nature of the problem and the user's emotions. Then, it consults a knowledge database to extract multiple solutions related to the problem.

[0728] The extracted solutions are optimized based on user attribute information and perceived emotions. Specifically, if a user expresses negative emotions, the server prioritizes presenting support messages and reassuring procedures that align with those emotions. In addition, additional support information is provided as needed.

[0729] For example, if a user enters a negative message such as, "I'm really fed up with this app crashing every time," the server uses an emotion engine to detect this "fed up" emotion. Then, in addition to the usual solutions, a reassuring message is displayed first, including phrases like, "Don't worry, this is a common problem and will be fixed soon," to stabilize the user's emotions.

[0730] Finally, the selected solution is sent to the terminal, and the user receives and executes clear instructions to resolve the problem. By using this system, users can resolve their technical problems efficiently and with peace of mind.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] Users send technical problems as text input using the chat interface of their communication device. For example, they might type, "The app keeps crashing and it's frustrating."

[0734] Step 2:

[0735] The terminal sends the entered text data to the server. This data also includes the user's device information and operating system information.

[0736] Step 3:

[0737] The server passes the received text to the emotion engine, which analyzes the user's emotions. Here, the emotion "frustrated" is identified as negative.

[0738] Step 4:

[0739] The server uses a natural language processing engine to analyze user input and extract keywords related to the problem (such as "the app crashed").

[0740] Step 5:

[0741] The server consults a knowledge database and generates multiple relevant solutions based on the extracted keywords.

[0742] Step 6:

[0743] The server optimizes solutions based on user attribute information and sentiment analysis results. Since negative emotions have been detected, solutions that include messages to reassure the user are prioritized.

[0744] Step 7:

[0745] The server sends the selected solution to the terminal. The terminal receives it and displays the instructions and messages to the user in an easy-to-understand manner.

[0746] Step 8:

[0747] The user follows the displayed instructions to resolve the technical issue. The server receives feedback from the user as needed and prepares to take further action.

[0748] (Example 2)

[0749] 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".

[0750] In modern technical support systems, solutions provided without considering the user's feelings can amplify their anxiety and stress. This can result in a poor user experience and hinder prompt and appropriate problem resolution. There is a need to provide effective methods to address these challenges and improve user satisfaction.

[0751] 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.

[0752] In this invention, the server includes means for receiving questions entered by the user via an information processing device, means for analyzing the emotions contained in the received questions, and means for analyzing the text data using a natural language processing engine. This makes it possible to present solutions that take the user's emotions into consideration. With this system, users can solve technical problems more quickly and appropriately while feeling at ease.

[0753] An "information processing device" is a general term for a device used by a user for input and equipped with communication capabilities.

[0754] "Means of receiving" refers to a method or mechanism for acquiring data transmitted from an information processing device.

[0755] "Means of analyzing emotions" refers to technologies and processes for identifying and classifying the emotions contained in text received from users.

[0756] "Means of analyzing text data using a natural language processing engine" refers to a method that uses natural language processing techniques to analyze input text data and understand its meaning and relationships.

[0757] A "knowledge base" is a database that aggregates information useful for solving technical problems, and it contains past cases and specialized knowledge.

[0758] "User attribute information" refers to information related to individual users, including information about the devices they use and their operating environment.

[0759] "Means of selection" refers to the process of selecting and proposing the optimal solution based on the analyzed information.

[0760] "Reassuring supplementary information" refers to information added to a solution to prevent users from feeling anxious or stressed.

[0761] A "collection of past cases" refers to a data set that compiles records and examples of problem-solving that have been collected to date.

[0762] This invention is a system for efficiently and reliably solving users' technical problems. Users input technical questions in natural language using a communication terminal, which acts as an information processing device. This terminal can be, for example, a smartphone or a computer, and includes hardware capable of internet connectivity.

[0763] When a user enters a question through the terminal's interface and submits it, the terminal sends that information to the server. The information sent includes the entered text data, the type of device being used, and operating system information. The server first passes the received text to a sentiment analysis engine to identify positive, negative, or neutral emotions.

[0764] Next, the server uses a natural language processing engine to analyze the user's question and identify the nature of the technical problem. For example, if the input is "This app crashes every time," sentiment analysis will identify negative emotions, and the content will be analyzed to include "crash" and "app."

[0765] Based on the analysis results, the server consults a knowledge base and extracts multiple solutions related to the problem. The extracted solutions are optimized based on user attribute information and sentiment analysis results, and information that reassures the user is added. For example, a message such as "Don't worry, this is a common problem and can be solved quickly" is added.

[0766] Ultimately, the server sends this information back to the terminal, allowing the user to proceed with problem resolution based on the received solutions. This process provides prompt and appropriate support, enabling users to address technical issues with confidence.

[0767] As an example of a prompt, giving the model instructions such as, "Provide solutions for when a user is dissatisfied with app crashes," can help provide more appropriate and emotion-sensitive solutions.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The user inputs a technical question in natural language into the input interface of the communication terminal and sends it. The input is text data containing a specific problem, such as "This app crashes every time." At this time, the terminal captures the information and obtains device information and operating system information. As output, the user's input text and technical information are compiled and ready to be sent to the server.

[0771] Step 2:

[0772] The terminal sends user input text, device information, and operating system information to the server. The input includes the data prepared in the previous step and is sent to the server. As output, the server receives this data and prepares it for analysis.

[0773] Step 3:

[0774] The server passes the received text data to the sentiment engine for sentiment analysis. The input is text data, and the analysis identifies positive, negative, and neutral emotions. Specifically, it extracts negative emotions from the word "fed up." The output is the result of the sentiment analysis, which is used in the next processing step.

[0775] Step 4:

[0776] The server uses a natural language processing engine to analyze text data. The input consists of text and sentiment analysis results. The text analysis identifies the nature of the technical problem. Specifically, keywords such as "crash" and "app" are extracted. The output generates data on the analyzed technical problem.

[0777] Step 5:

[0778] The server extracts solutions from its knowledge base based on the analyzed information. The input includes data on the technical problem, which is used to search for relevant solutions. Specifically, relevant past cases and recommended procedures are retrieved. The output is a list of multiple solutions.

[0779] Step 6:

[0780] The server optimizes solutions based on user attribute information and sentiment analysis results. The input is a list of solutions, which are filtered according to the user's attributes and emotions. Specifically, messages that provide reassurance in response to negative emotions are selected. The output is an optimized solution.

[0781] Step 7:

[0782] The server sends the optimized solution to the terminal. The input includes the final selected solution. The specific operation of sending involves transmitting the data to the terminal over the network. The output is that the terminal receives the solution and is ready to display it to the user.

[0783] (Application Example 2)

[0784] 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".

[0785] When users encounter technical problems, traditional systems mechanically offer solutions without considering the user's feelings, resulting in a failure to adequately enhance user satisfaction. Because the same approach is used even when dealing with users experiencing negative emotions, it fails to provide a sense of security and trust, ultimately hindering improvements in the user experience.

[0786] 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.

[0787] In this invention, the server includes means for receiving technical questions entered by the user via a communication device, means for analyzing the received questions using a natural language processing device, means for extracting multiple solutions from a database based on the analysis results, means for analyzing the user's emotions using an emotion recognition engine, means for selecting the optimal solution based on user attribute information and emotion analysis results, and means for presenting the user with a support message corresponding to the selected solution and emotions. This makes it possible to provide the best possible response according to the user's emotions and improve the user experience.

[0788] A "communication device" is a device that allows a user to input technical questions and transmit them to a system.

[0789] A "natural language processing unit" is a computer system that analyzes text data received from a user and interprets its meaning.

[0790] A "database" is a system that stores past solutions and information, and is used to extract information for problem-solving based on analysis results.

[0791] An "emotion recognition engine" is software that analyzes the emotions contained in user input and identifies that emotional state.

[0792] "User attribute information" refers to information related to the type of equipment and operating system used by the user, and serves as basic data for making optimal suggestions to the user.

[0793] A "solution" is a method or procedure for solving a problem that the system proposes to the user based on the results of its analysis.

[0794] A "support message" is a communication message provided in response to the user's emotional state, designed to convey a sense of stability and trust.

[0795] The system implementing this invention aims to efficiently process users' technical questions and provide appropriate solutions based on sentiment analysis. Specific embodiments are described below.

[0796] When a user enters a technical question using a communication device, the data is sent to the server. The server uses a natural language processing unit (NLTK) to analyze the input text and identify the problem being addressed. This analysis uses Python programs and natural language processing libraries such as NLTK and spaCy.

[0797] For identified problems, the server extracts multiple solutions from the database. The database also includes past case data, enabling efficient information extraction.

[0798] Next, the server activates its emotion recognition engine to analyze the user's emotions from the input text. This analysis is performed using text analysis tools such as TextBlob and TensorFlow.

[0799] The server then considers the user's attribute information and the results of the sentiment analysis to select the optimal solution. If the user is showing negative emotions, a support message is generated simultaneously to provide the user with a sense of reassurance.

[0800] For example, if a user asks a question such as, "I want to know why my order was canceled," the system uses an emotion recognition engine to read the underlying anxiety arising from that question and, accordingly, adds a supportive message such as, "We apologize for the inconvenience. You can find more details about your order cancellation here."

[0801] An example of a prompt for a generative AI model might be, "If the user is feeling uncomfortable, please suggest how to respond." This allows the server to present the AI ​​model with prompts that are relevant to the user's emotions, enabling it to create more appropriate responses.

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The user inputs a technical question via a communication device, and the terminal sends that text data to the server. The input is the user's question, and the output is the unparsed text data received by the server.

[0805] Step 2:

[0806] The server uses a natural language processing unit to analyze the received text data. This analysis reveals the intent behind the user's question and specific technical problems. The input is unanalyzed text data, and the output is structured data containing the problematic information.

[0807] Step 3:

[0808] The server extracts relevant solutions from the database based on structured data. The input is structured data related to the problem, and the output is a list containing multiple solutions. Database searches are performed by cross-referencing with past cases.

[0809] Step 4:

[0810] The server uses an emotion recognition engine to analyze the user's emotions from the question text. The input is unanalyzed text data, and the output is the user's emotional state (positive, negative, or neutral). Text analysis tools are used for emotion analysis.

[0811] Step 5:

[0812] The server selects the optimal solution based on user attribute information and emotional state. Inputs are user attribute information, emotional state, and a list of solutions; output is the optimized solution. Selection criteria include the user's environment (device, software, etc.).

[0813] Step 6:

[0814] The server generates and presents to the user a support message tailored to the selected solution and the user's emotional state. The input is the optimized solution and emotional state, while the output is a response message to the user. The support message incorporates content that addresses the user's emotional state.

[0815] Step 7:

[0816] The user receives the proposed solutions and support messages and takes the actions necessary to resolve the problem. The input is the response message, and the output is the user's actions and the progress of problem resolution. This completes the entire system cycle.

[0817] 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.

[0818] 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.

[0819] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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."

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] A means of receiving questions entered by the user via a communication terminal,

[0841] A means of analyzing the received question using a natural language processing engine,

[0842] A means for extracting multiple solutions from a knowledge database based on the analysis results,

[0843] A means of selecting an appropriate solution based on user attribute information,

[0844] A means of presenting the selected solution to the user,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein user attribute information includes information about the type of device used and the operating system.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein solutions are filtered using a database of past cases.

[0850] "Example 1"

[0851] (Claim 1)

[0852] A means of receiving inquiries entered by users via an information terminal,

[0853] A method for analyzing received inquiries using a natural language processing engine, extracting keywords, and identifying the problem,

[0854] A means of searching for multiple solutions from a database based on the analysis results,

[0855] A means of selecting the optimal solution based on user characteristics information,

[0856] A means of displaying the selected solution to the user,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein user characteristic information includes data relating to the type of device used and the operating system.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein solutions are filtered using a database of past examples.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] A means of receiving security-related issues entered by a user via a communication device,

[0865] A means of analyzing the received problem using natural language processing technology,

[0866] A means of extracting multiple solutions from a knowledge base based on the analysis results,

[0867] A means of selecting an appropriate solution based on user attribute information,

[0868] A means of presenting selected solutions to users and providing measures to prevent security risks,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein user attribute information includes information about the type of device used and the operating system.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein solutions are filtered based on risk assessment using a database of past cases.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A means for receiving a question entered by a user via an information processing device,

[0877] A means of analyzing the emotions contained in the received questions,

[0878] A means of analyzing text data using a natural language processing engine,

[0879] A means of extracting multiple solutions from a knowledge base based on the analysis results,

[0880] A means of selecting an appropriate solution based on user attribute information and analyzed sentiment,

[0881] A means of presenting the selected solution to the user along with reassuring supplementary information,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein user attribute information includes information regarding the type of information device being used and the operating platform.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein solutions are filtered using a set of past cases.

[0887] "Application example 2 when combining with an emotional engine"

[0888] (Claim 1)

[0889] A means for receiving technical questions entered by a user via a communication device,

[0890] A means for analyzing a received question using a natural language processing device,

[0891] A means of extracting multiple solutions from a database based on the analysis results,

[0892] A means of analyzing a user's emotions using an emotion recognition engine,

[0893] A means for selecting the optimal solution based on user attribute information and sentiment analysis results,

[0894] A means of presenting the user with selected solutions and emotionally appropriate support messages,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, wherein user attribute information includes information about the type of equipment used and the basic software.

[0898] (Claim 3)

[0899] The system according to claim 1, wherein solutions are filtered using past case data, and support messages are further adjusted according to the user's emotional state. [Explanation of Symbols]

[0900] 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 questions entered by the user via a communication terminal, A means of analyzing the received question using a natural language processing engine, A means for extracting multiple solutions from a knowledge database based on the analysis results, A means of selecting an appropriate solution based on user attribute information, A means of presenting the selected solution to the user, A system that includes this.

2. The system according to claim 1, wherein user attribute information includes information about the type of device used and the operating system.

3. The system according to claim 1, wherein solutions are filtered using a database of past cases.

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