system

Abstract

We provide the system. [Solution] A means of receiving questions from users, A means of analyzing the received questions, A means of obtaining relevant information from a database based on the analyzed question, A means of generating an answer based on the acquired information, A means of sending the generated response to the user's terminal, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, answering questions regarding a wide variety of products requires individual systems or knowledge, and it is difficult to obtain information efficiently. For example, there is a QA system for a specific product, but to obtain information about other products, it is necessary to inquire with the responsible department, which causes an increase in man-hours and inefficiency of operations. In addition, since existing systems cannot respond in real time, there is a problem of user waiting time. To solve these problems, a system that can respond uniformly and promptly to questions across all commercial products is required.

Means for Solving the Problems

[0005] This invention provides a system that centrally receives and analyzes user questions, retrieves information from relevant databases to generate answers, and provides them to users in a timely manner. Specifically, it provides a system that includes means for receiving user questions, means for analyzing questions, means for database linkage to retrieve relevant information, means for generating answers, and means for transmitting the generated answers to the user's terminal. This system centrally manages information on a wide variety of products and enables real-time responses, thereby achieving rapid and accurate answers to user questions. Furthermore, by conducting the exchange of questions and answers in JSON format, communication efficiency and data consistency can be maintained.

[0006] A "user" is a person or entity that inputs questions into a system and performs actions to obtain answers.

[0007] "Means for receiving questions" refers to interfaces or modules for obtaining questions entered by users.

[0008] "Means of analyzing questions" refers to the process or algorithm that understands the content of a received question and performs the analysis necessary to obtain appropriate information.

[0009] "Means of retrieving information from a database" refers to modules or processes that search for and retrieve relevant information from a database based on an analyzed question.

[0010] "Means of generating answers" refers to the process or algorithm that creates appropriate answers to user questions based on acquired information.

[0011] "Means of sending to the user terminal" refers to a communication module for sending the generated response to the user's device (computer, smartphone, etc.).

[0012] The "display area" refers to the region or screen area used to visually display the answers generated on the user's device.

[0013] "Information about multiple products" refers to detailed information about various products that are managed and referenced by the system.

[0014] "Methods for conducting this in real time" refer to processes and technologies that enable the immediate exchange of questions and answers.

[0015] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a lightweight text data format used for representing and communicating data. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system that centrally receives and analyzes questions from users, retrieves information from an appropriate database, generates answers based on that information, and provides them to the user.

[0038] System Overview

[0039] This system includes the following main components: an interface for receiving user questions, a process for analyzing questions, a module for retrieving information by referencing appropriate databases, an algorithm for generating answers based on the retrieved information, and a communication module for sending the generated answers to the user's terminal.

[0040] Program execution steps

[0041] 1. Receiving questions from users

[0042] The user enters the question into the input field on their device and clicks the submit button.

[0043] The terminal retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0044] 2. Analysis of the Question

[0045] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0046] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process understands the user's question and identifies relevant keywords and categories.

[0047] 3. Retrieving information from the database

[0048] The server consults a database containing relevant information based on the analyzed question.

[0049] The database module retrieves the corresponding information and returns the results to the server.

[0050] 4. Generating the answer

[0051] The server generates the best possible answer to the user's question based on the information it has gathered. This process uses pre-defined templates and conditional logic to create answers in natural language.

[0052] 5. Submit your response

[0053] The server converts the generated response into JSON format and sends it to the user's device.

[0054] The device analyzes the received response and displays it in the designated area on the screen.

[0055] Specific example

[0056] 1. Example of user operation

[0057] The user types "What is the warranty period for my new smartphone?" into the text box on the device and presses the send button.

[0058] 2. Example of terminal processing

[0059] The device sends the entered question to the server in JSON format, {'text':'What is the warranty period for the new smartphone?'}.

[0060] 3. Server reception and query analysis

[0061] The server receives this request, analyzes the question, and extracts keywords such as "warranty period" and "smartphone."

[0062] 4. Retrieving information from the database

[0063] The server searches its database for information related to "smartphones" and "warranty period," and retrieves the information, "The warranty period for new smartphones is one year."

[0064] 5. Generate and submit your response.

[0065] The server generates a response stating, "The warranty period for your new smartphone is one year," and sends it back to the device in JSON format.

[0066] The device displays this answer on the screen for the user to see.

[0067] In this way, the system of the present invention provides quick and accurate answers to user questions and enables efficient QA support across all products.

[0068] The following describes the processing flow.

[0069] Step 1:

[0070] The user enters their question into the text box on their device and clicks the submit button.

[0071] Step 2:

[0072] The device retrieves the question entered by the user and converts the question content into JSON format {"question": "What is the warranty period for the new smartphone?"}.

[0073] Step 3:

[0074] The terminal sends the converted JSON-formatted question to the server as a POST request.

[0075] Step 4:

[0076] The server receives a POST request from the terminal and extracts the question content from the request body.

[0077] Example: Retrieve the question "What is the warranty period for the new smartphone?".

[0078] Step 5:

[0079] The server uses natural language processing (NLP) techniques to analyze the extracted questions.

[0080] Example: Analyze the question and identify the keywords "warranty period" and "smartphone".

[0081] Step 6:

[0082] The server references relevant databases based on the analysis.

[0083] The server searches the database for information related to "smartphone" and "warranty period".

[0084] Step 7:

[0085] The database module searches for the relevant records and returns the results to the server.

[0086] Example: Retrieve the search result "The warranty period for new smartphones is one year."

[0087] Step 8:

[0088] The server generates appropriate answers to the user's questions based on the information it has obtained.

[0089] Example: Generated response: "The warranty period for your new smartphone is one year."

[0090] Step 9:

[0091] The server converts the generated response into JSON format {"answer": "The warranty period for the new smartphone is one year."} and sends it to the device.

[0092] Step 10:

[0093] The terminal receives a response from the server and parses the response in JSON format.

[0094] Example: Analyze and extract the answer portion.

[0095] Step 11:

[0096] The device displays the analyzed responses in a designated area on the user interface.

[0097] Example: Display "The warranty period for new smartphones is one year" in the display area.

[0098] Step 12:

[0099] The user reviews the answers displayed in the display area and enters the next question if necessary.

[0100] Thus, the system of the present invention performs appropriate processing at each step in order to provide real-time and accurate answers to user questions.

[0101] (Example 1)

[0102] 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."

[0103] Conventional question answering systems suffer from the problem of each processing step being independent, resulting in significant time spent on information analysis and retrieval. Furthermore, the lack of established methods for providing appropriate answers quickly can lead to decreased user satisfaction. Additionally, maintaining consistency and coherence when retrieving information from multiple databases presents a significant challenge.

[0104] 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.

[0105] In this invention, the server includes means for receiving questions from users, means for converting the received questions into JSON format and sending them to the server, means for analyzing the received questions, means for identifying relevant keywords and categories from the analyzed questions using natural language processing technology, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, and means for converting the generated answers into JSON format and sending them to the user's terminal. This enables the provision of quick and appropriate answers, improving the user experience. Furthermore, consistency can be maintained even when retrieving information from multiple databases.

[0106] A "user" refers to a person who submits questions or inputs data into the system.

[0107] "Device" refers to a computing device used by a user, and includes smartphones, tablets, and personal computers.

[0108] A "server" refers to a central computing system that receives requests from user terminals and processes and analyzes data.

[0109] "Means of receiving questions" refers to the methods and processes by which the system recognizes and retrieves questions entered by the user through a device.

[0110] JSON format is a lightweight data exchange format for representing data in a structured way.

[0111] "Means of sending to the server" refers to the procedures and methods for transferring a user's question from their terminal to the server.

[0112] "Methods for analyzing questions" refers to the process of understanding a received question and identifying the meaning and intent contained within it.

[0113] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0114] "Means of identifying relevant keywords and categories" refers to the process of extracting important words and categories from the analyzed questions.

[0115] A "database" refers to a collection of data that organizes and stores information, making it searchable and retrievable.

[0116] "Means of obtaining information" refers to the methods and processes for searching for and retrieving necessary information from a database.

[0117] "Means of generating answers" refers to the process of creating appropriate answers to user questions based on acquired information.

[0118] "Means of sending to the user terminal" refers to the procedures and methods for transferring the generated response from the server to the user terminal.

[0119] The "display area" refers to the area on the device screen where information or results are displayed.

[0120] This invention relates to a system that centrally receives and analyzes user questions, retrieves information from an appropriate database, and generates and provides answers to the user based on that information. An embodiment of this system will be described in detail.

[0121] System Configuration

[0122] The system consists mainly of the following components:

[0123] 1. User terminal: This refers to the device used by the user to input questions, and includes smartphones, tablets, and personal computers.

[0124] 2. Server: A central system that receives requests from user terminals, analyzes questions, retrieves information, and generates answers.

[0125] 3. Database: A collection of data that stores necessary information. For example, a database containing information about a product.

[0126] Program processing details

[0127] Receiving questions and converting them to JSON format.

[0128] The user enters a question into the input field on the device and clicks the submit button. The device retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0129] Question analysis

[0130] The server receives POST requests from terminals and extracts the content of the question from the requests. The server uses natural language processing (NLP) techniques to analyze the questions. In this process, libraries such as NLTK and spaCy are used to understand the context of the user's question and identify relevant keywords and categories.

[0131] Retrieving information from the database

[0132] The server searches databases that store relevant information based on the parsed query. This typically involves using database management systems such as MySQL® or PostgreSQL. The database module retrieves information based on the specified keywords and returns the results to the server.

[0133] Answer generation

[0134] The server generates the optimal answer to the user's question based on the acquired information. This process can utilize a template engine like Jinja2 and incorporate the algorithm of a generative AI model. For example, it could be generated using OpenAI's GPT-3®.

[0135] Submit and display of responses

[0136] The server converts the generated response into JSON format and sends it to the user's device. The device parses the received response and displays it in the display area on the screen. This allows the user to confirm the answer to their question.

[0137] Specific example

[0138] The user enters "What is the warranty period for my new smartphone?" into the text box on their device and presses the submit button. The device converts this input into JSON format and sends it to the server. The server parses the question and extracts the keywords "warranty period" and "smartphone." Next, the server searches its database and retrieves the information "The warranty period for your new smartphone is one year." The server then generates an answer based on this information and sends it to the device. Finally, the device displays this answer on its screen.

[0139] Example of a prompt

[0140] Examples of prompts for a generative AI model include the following:

[0141] User question: What is the warranty period for a new smartphone?

[0142] Answer: The warranty period for the new smartphone is one year.

[0143] In this way, the system can provide smooth and accurate answers to user questions.

[0144] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0145] Step 1:

[0146] The user enters the question into the input field on the device and clicks the submit button.

[0147] The device retrieves this question, converts it to JSON format ({'text':'What is the warranty period for the new smartphone?'}), and sends it to the server as a POST request.

[0148] Input: A question entered by the user (e.g., "What is the warranty period for the new smartphone?").

[0149] Output: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0150] Step 2:

[0151] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0152] The server uses natural language processing (NLP) techniques to analyze the question. This process involves understanding the context of the question and identifying relevant keywords and categories using libraries such as NLTK and spaCy.

[0153] Input: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0154] Output: Analysis results (e.g., keywords such as "warranty period" and "smartphone").

[0155] Specific operation: The server uses an NLP library to extract the keywords "warranty period" and "smartphone" from the question text.

[0156] Step 3:

[0157] The server searches databases containing relevant information based on the analyzed question.

[0158] The database module searches for information based on specified keywords and returns the results to the server. It uses database management systems such as MySQL or PostgreSQL.

[0159] Input: Analysis results (e.g., "warranty period", "smartphone").

[0160] Output: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0161] Specific operation: The server generates a database query using the keywords "warranty period" and "smartphone" and searches the MySQL database. As a result, it retrieves the information, "The warranty period for new smartphones is one year."

[0162] Step 4:

[0163] The server generates the best possible answer to the user's question based on the information it has obtained.

[0164] This process could also involve using a template engine like Jinja2 and incorporating algorithms for generative AI models.

[0165] Input: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0166] Output: Generated answer (e.g., "The warranty period for the new smartphone is one year.").

[0167] Specific operation: The server applies the retrieved data to a template and generates a natural-sounding response such as, "The warranty period for the new smartphone is one year."

[0168] Step 5:

[0169] The server converts the generated response into JSON format and sends it to the user's device.

[0170] The device analyzes the received response and displays it in the designated area on the screen.

[0171] Input: Generated response (e.g., "The warranty period for the new smartphone is one year.").

[0172] Output: The response displayed on the device ("The warranty period for your new smartphone is one year.").

[0173] Specific operation: The server converts the generated response into JSON format ({'response': 'The warranty period for the new smartphone is one year.'}) and sends it to the device. The device receives this and displays it on the screen.

[0174] (Application Example 1)

[0175] 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."

[0176] Traditional e-commerce sites faced the challenge of not being able to provide quick and accurate answers to user questions about products. This raised concerns about potentially diminishing user purchasing intent and lowering customer satisfaction. Furthermore, some questions were complex, making it time-consuming to find appropriate answers, and there was a risk of receiving incorrect information.

[0177] 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.

[0178] In this invention, the server includes means for receiving questions from users, means for analyzing the received questions, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, means for transmitting the generated answers to the user terminal, and means for using the obtained information to be executed on the e-commerce site. This makes it possible to provide quick and accurate answers when users ask questions about products on the e-commerce site.

[0179] "Means of receiving user questions" refers to the means by which users input questions into the system, and which terminals or servers receive those questions.

[0180] "Means for analyzing received questions" refers to methods for analyzing received user questions using natural language processing techniques and algorithms to identify relevant keywords and categories.

[0181] "Means of obtaining relevant information from a database based on analyzed questions" refers to methods for searching and obtaining relevant information from a database based on analyzed keywords and categories.

[0182] "Means for generating answers based on acquired information" refers to methods for creating the optimal answer to a user's question based on the acquired information.

[0183] "Means for sending generated responses to the user's terminal" refers to means for sending generated responses to the user's terminal via data communication.

[0184] "Methods used on an e-commerce site to utilize acquired information" refers to a series of methods used to generate answers to user questions on an e-commerce site and provide those answers to the user.

[0185] "A database containing information on multiple products" means that the database has accumulated detailed information about various products, allowing for the retrieval of appropriate information in response to a question.

[0186] "Using a generative AI model" means utilizing an artificial intelligence model to generate the optimal answer to a user's question, and this model is pre-trained.

[0187] "A means of generating input prompts for a generative AI model to obtain the optimal response" refers to a method of obtaining the optimal response by creating input statements (prompt statements) in a format suitable for the AI ​​model and passing them to the AI ​​model.

[0188] This invention relates to a system for quickly and accurately answering questions users have about products on an e-commerce site. The system has the function of receiving questions from users, analyzing those questions, retrieving relevant information from a database based on the results, generating the optimal answer, and sending it to the user's terminal.

[0189] System Configuration

[0190] The system includes the following main components:

[0191] 1. User Interface: This interface, provided as a smartphone application, allows users to easily input and submit questions.

[0192] 2. Question Analysis Method: On the server side, questions received from users are analyzed using natural language processing (NLP) technology. This analysis helps understand the intent of the question and extract relevant keywords and categories.

[0193] 3. Information Acquisition Method: Based on the analyzed questions, relevant information is retrieved from the database. The database contains detailed information on multiple products.

[0194] 4. Answer Generation Method: An appropriate answer is generated based on the acquired information. A generative AI model is used in this process. The generative AI model provides the optimal answer based on the input prompt sentence.

[0195] 5. Means of sending responses: The generated responses are converted to JSON format and sent to the user's terminal via a communication module.

[0196] Hardware and software to be used

[0197] Smartphone: A device used by users to input questions and receive answers.

[0198] Server: The central component responsible for parsing questions, retrieving information, generating answers, and sending them. The server is built using Flask (a web framework for Python).

[0199] Natural language processing library (spaCy): A tool for analyzing the content of questions.

[0200] Database (SQLite): A lightweight database for storing product information.

[0201] Generative AI model: A model for generating the optimal answer to a question.

[0202] Specific examples of operations

[0203] User action: The user enters "Please tell me the size guide for this jacket" into the smartphone application and sends it.

[0204] Server processing:

[0205] The server receives the question and performs analysis using spaCy.

[0206] Extract keywords related to the question content: "jacket" and "size guide".

[0207] Search and retrieve information about the "size guide" for "jackets" from the SQLite database.

[0208] Using the generated AI model, natural-sounding responses are produced based on the acquired information.

[0209] The generated response, "This is the size guide for this jacket: S, M, L, XL," is sent to the user's device.

[0210] Presenting the answer to the user: The user confirms the answer displayed on their smartphone screen: "This is the size guide for this jacket: S, M, L, XL."

[0211] Examples of prompts for generative AI models

[0212] "User question: 'What is the size guide for this jacket?' Generate the best answer from the information in your database."

[0213] This system allows users to receive quick and accurate answers to their questions on e-commerce sites, leading to improved customer satisfaction and support for their purchasing intent.

[0214] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0215] Step 1: The user enters and submits the question.

[0216] The user enters a question into the chat interface of the smartphone application and presses the send button. This input is converted to JSON format and sent to the server. The data entered is a text-based question.

[0217] Step 2: The server receives the question.

[0218] The server receives JSON data sent from the user's terminal in POST request format. The received data is parsed to extract the question content. Specifically, the content of the "text" field is extracted from the JSON object and prepared for analysis.

[0219] Step 3: Questionnaire Analysis

[0220] The server analyzes the received questions using natural language processing libraries such as spaCy. The data processing performed here involves extracting important keywords and categories from the question content. The input is the received question text, and the output is a list of analyzed keywords.

[0221] Step 4: Database Search

[0222] The server retrieves relevant information from the SQLite database based on the analyzed keywords. The data operation here involves searching for records containing the keywords and retrieving their contents. The input is a list of keywords, and the output is a record of the relevant information.

[0223] Step 5: Generate Response

[0224] The server uses the information obtained through the search to provide prompts to the generative AI model, which then generates the optimal response. This process involves natural language generation using predefined templates and a generative AI model. The input consists of information retrieved from the database and prompts, while the output is the generated response in text format.

[0225] Step 6: Submit your response

[0226] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated text response, and the output is the sent JSON formatted response data.

[0227] Step 7: The user's device displays the answer.

[0228] The user's terminal parses the JSON data received from the server, extracts the answer text, and displays it on the screen. The user can then verify the answer to the question through the screen. The input is the received JSON data of the answer, and the output is the displayed answer text.

[0229] This series of processing steps allows users to obtain answers to their questions quickly and accurately. Furthermore, by utilizing generative AI models and prompt sentences, natural and appropriate answers are provided.

[0230] 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.

[0231] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This enables more personalized responses that are in line with the user's emotions.

[0232] System Overview

[0233] This system includes the following main components: an interface for receiving questions from the user, a process for analyzing the questions, a module for recognizing the user's emotions using an emotion engine, a module for retrieving information by referring to an appropriate database, an algorithm for generating answers based on the retrieved information and recognized emotions, and a communication module for sending the generated answers to the user's terminal.

[0234] Program execution steps

[0235] 1. Receiving questions from users

[0236] The user enters their question into the text box on their device and clicks the submit button.

[0237] The terminal receives a question from the user and sends the question content to the server in JSON format.

[0238] 2. Analysis of questions and emotions

[0239] The server receives the request from the terminal and extracts the question content.

[0240] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process helps the server understand the user's question and identify relevant keywords and categories.

[0241] The emotion engine uses technologies such as speech, text analysis, and facial recognition to recognize the user's emotions as expressed in the questions. For example, it can detect negative tones and emphasis in the text the user sends.

[0242] 3. Retrieving information from the database

[0243] The server then references relevant databases based on the analysis results and recognized emotions.

[0244] The server searches the database for relevant information and retrieves the results. For example, it might retrieve information such as the warranty period, technical support, and usage instructions for a specific product.

[0245] 4. Generating the answer

[0246] Based on the information it receives, the server generates the most appropriate response according to the perceived emotions. For example, if the user is showing anxiety, it will create a response in a reassuring tone.

[0247] The generated response is converted to JSON format and sent to the device.

[0248] 5. Sending and displaying to the user's terminal

[0249] The terminal receives a response from the server and parses the response in JSON format.

[0250] The device displays the analyzed response in the user interface display area. For example, it might display a response such as, "The warranty period for your new smartphone is one year. Please let us know if you have any questions."

[0251] Specific example

[0252] 1. Example of user operation

[0253] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[0254] 2. Example of terminal processing

[0255] The device sends the entered question to the server in JSON format as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[0256] 3. Server reception and question / sentiment analysis

[0257] The server receives this request, analyzes the question, and, along with the keywords "warranty" and "smartphone," uses its emotion engine to recognize the emotion "worry."

[0258] 4. Retrieving information from the database

[0259] The server searches its database for information related to "smartphones" and "warranties," and retrieves the information, "The warranty period for new smartphones is one year."

[0260] 5. Generate and submit your response.

[0261] The server generates a response saying, "The warranty period for your new smartphone is one year. Please rest assured," and sends it back to the device in JSON format.

[0262] 6. Display results on the user's terminal

[0263] The device analyzes the received response and displays the message "The new smartphone has a one-year warranty. Please rest assured." in the display area on the screen.

[0264] Thus, the system of the present invention aims to improve the user experience by taking into account the user's emotions and providing prompt and appropriate emotionally responsive answers to questions.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[0268] Step 2:

[0269] The device retrieves the question entered by the user and converts the question content into JSON format: {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[0270] Step 3:

[0271] The terminal sends the converted JSON-formatted question to the server as a POST request.

[0272] Step 4:

[0273] The server receives a POST request from the terminal and extracts the question content from the request body.

[0274] Example: Retrieve the question "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?"

[0275] Step 5:

[0276] The server analyzes the extracted questions using natural language processing (NLP) techniques.

[0277] Example: Identify keywords such as "warranty period" and "smartphone".

[0278] Step 6:

[0279] The server passes the analyzed question content to the emotion engine.

[0280] The server uses the emotion engine to recognize the emotion of "worried".

[0281] Step 7:

[0282] The server refers to the relevant database based on the keywords and the recognized emotion.

[0283] Example: Obtain information from the database such as "The warranty period for a new smartphone is one year."

[0284] Step 8:

[0285] The server generates an optimal answer based on the obtained information and the recognized emotion.

[0286] Example: Generate an answer such as "The warranty period for a new smartphone is one year. Please rest assured."

[0287] Step 9:

[0288] The server converts the generated answer into JSON format {"answer": "The warranty period for a new smartphone is one year. Please rest assured."} and sends it to the terminal.

[0289] Step 10:

[0290] The terminal receives the response from the server and analyzes the JSON-formatted answer.

[0291] Example: Analyze and extract the answer part.

[0292] Step 11:

[0293] The device displays the analyzed responses in the user interface display area.

[0294] Example: The display area will show the message, "The warranty period for your new smartphone is one year. Please rest assured."

[0295] Step 12:

[0296] Users can see the answers displayed in the display area and feel reassured.

[0297] In this way, the system of the present invention takes into account the user's emotions and quickly provides an appropriate emotionally responsive answer to a question.

[0298] (Example 2)

[0299] 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".

[0300] Traditional systems simply provide factual answers to user questions, lacking the ability to provide personalized responses that take user emotions into account. This resulted in a diminished user experience and decreased satisfaction.

[0301] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question from a user, means for analyzing the received question, means for recognizing the analyzed question and emotions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer based on the obtained information and recognized emotions, and means for transmitting the generated answer to the user terminal. This makes it possible to provide a personalized answer that corresponds to the user's emotions.

[0302] The "means for receiving questions from users" is a function for obtaining questions input by users via a terminal.

[0303] The "means for analyzing received questions" is a function for analyzing the obtained question content using natural language processing technology and identifying keywords and categories.

[0304] The "means for recognizing questions and emotions" is a process for recognizing the emotions of users contained in the question content, and is a function that uses natural language processing and emotion analysis technology.

[0305] The "means for obtaining relevant information from a database" is a function for searching and obtaining relevant information from a database based on the analyzed question content and recognized emotions.

[0306] The "means for generating an answer" is a process for generating an optimal answer based on the obtained information and recognized emotions, and is a function that utilizes a generation AI model.

[0307] The "means for transmitting the generated answer to the user terminal" is a function for converting the generated answer into JSON format and transmitting it to the user terminal through a network.

[0308] The "database containing multiple types of information" is a database system for storing and managing various information, including product information, technical support information, warranty information, etc.

[0309] The "display area of the user terminal" is an interface part for the user to visually confirm information through the terminal.

[0310] The present invention relates to a system that recognizes the emotions contained in questions from users and generates appropriate answers based on those emotions. With this system, it becomes possible to provide a prompt and personalized response according to the emotions to the questions of users.

[0311] This system consists of an interface for receiving questions from the user, a process for analyzing the questions, a module that recognizes the user's emotions using an emotion engine, a module that retrieves information by referring to an appropriate database, an algorithm that generates answers based on the retrieved information and recognized emotions, and a communication module that sends the generated answers to the user's terminal.

[0312] Hardware and software to be used

[0313] Server: A high-performance cloud server will be used. The server will primarily handle question analysis, sentiment recognition, database access, and answer generation.

[0314] User terminal: Provides an interface with the user using devices such as PCs, smartphones, and tablets.

[0315] Natural language processing (NLP) tools: For example, use Google's BERT or OpenAI's GPT-3.

[0316] Emotion recognition engine: Uses Microsoft® Azure® Emotion API or IBM® Watson® Tone Analyzer.

[0317] Database management system (DBMS): MySQL or PostgreSQL are used.

[0318] Communication protocol: HTTP / HTTPS is used to exchange data between the terminal and the server.

[0319] Specific example

[0320] 1. Receiving questions from users

[0321] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and clicks the send button.

[0322] The terminal converts this question into JSON format and sends it to the server.

[0323] 2. Analysis of questions and emotions

[0324] The server receives the JSON data and extracts the question content.

[0325] The server uses natural language processing (NLP) tools (e.g., GPT-3) to analyze the question and identify keywords.

[0326] The server uses an emotion recognition engine (e.g., Emotion API) to recognize the user's emotion of "worry."

[0327] 3. Retrieving information from the database

[0328] The server consults the database and searches for information related to "smartphones" and "warranty."

[0329] The server retrieves information from the database stating that "the warranty period for new smartphones is one year."

[0330] 4. Generating the answer

[0331] The server uses the following prompts to input into the generated AI model (e.g., GPT-3):

[0332] Prompt: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?' The perceived emotion is 'worried.' Please generate an answer."

[0333] The server generates the response, "The warranty period for your new smartphone is one year. Please rest assured," converts it to JSON format, and sends it to the device.

[0334] 5. Sending and displaying to the user's terminal

[0335] The device parses the received JSON data and extracts the answer.

[0336] The device displays the analyzed response, "The warranty period for your new smartphone is one year. Please rest assured," in the user interface display area.

[0337] The system of the present invention can improve the user experience by recognizing the user's emotions and quickly providing personalized responses based on those emotions.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user enters a question into a text box on their device and clicks the submit button. For example, they might enter, "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?" The device converts this entered question into JSON format. Specifically, it converts it to the format {"question": "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?"} and sends it to the server as an HTTP POST request.

[0341] Step 2:

[0342] The server receives JSON data sent from the terminal and extracts the question. The input is JSON data such as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered by warranty?"}. The server analyzes the received question using natural language processing (NLP) tools. Specifically, it performs text tokenization, morphological analysis, and syntactic analysis to identify keywords. For example, it identifies the keywords "warranty" and "smartphone". The output is the identified keywords and the question content.

[0343] Step 3:

[0344] The server runs an emotion recognition engine using the analyzed question content and identified keywords. The input is the analysis results and the question content. Using the emotion recognition engine (e.g., Emotion API), it detects the emotion "worry." The output is the recognized emotion. Specifically, it analyzes negative tones and emphasis in the input text and generates the emotion tag "worry."

[0345] Step 4:

[0346] The server looks up a database based on recognized emotions and identified keywords. The input is the keywords and recognized emotions from the analysis results. For example, it searches the database for information related to "smartphone" and "warranty." Using a database management system (e.g., MySQL), it retrieves information such as "The warranty period for a new smartphone is one year." The output is the retrieved information.

[0347] Step 5:

[0348] The server generates a response based on the acquired information and recognized emotions. The input consists of information retrieved from the database and recognized emotions. Specifically, a generative AI model (e.g., GPT-3) is used, and the following prompt is input to the generative AI model: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still under warranty?' and the recognized emotion is worried. Please generate a response." The response generated based on the prompt is, "Your new smartphone has a one-year warranty. Please rest assured." The output is the response.

[0349] Step 6:

[0350] The server converts the generated response text into JSON format and sends it to the terminal. The input is the generated response text. For example, it is converted to the format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."}. The terminal parses the received JSON data and extracts the response text. The output is the extracted response text.

[0351] Step 7:

[0352] The device displays the extracted response text in the user interface display area. The input is the analyzed response text. For example, it might display the response text, "The warranty period for your new smartphone is one year. Please rest assured." The output is visual feedback to the user.

[0353] ---

[0354] The above outlines the specific processing flow of this system, including the inputs and outputs at each step, as well as the specific actions performed.

[0355] (Application Example 2)

[0356] 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".

[0357] Traditional user question response systems only provide uniform answers without considering user emotions, failing to adequately address user doubts and anxieties. Furthermore, especially in security services, users often experience anxiety and tension, and failure to address these emotions can degrade the user experience and damage trust in the service. Therefore, there is a need for a system that recognizes user emotions and provides appropriate responses tailored to those emotions.

[0358] 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. In this invention, the server includes means for receiving questions from the user, means for analyzing the received questions, means for recognizing the emotions contained in the analyzed questions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer corresponding to the acquired information and the recognized emotions, and means for transmitting the generated answer to the user terminal. This enables more personalized responses that take into account the user's emotions, and in particular in security services, it is possible to provide a sense of security to users who are feeling anxious or stressed, thereby improving the user experience and increasing trust in the service.

[0359] "Method for receiving questions from users" refers to a function that sends questions entered by users on their devices to the server.

[0360] "Method for analyzing received questions" refers to a function where the server analyzes the content of user questions received and identifies relevant keywords and categories.

[0361] "Means of recognizing emotions" refers to engines or algorithms that analyze the emotions contained in the user's questions and recognize those emotions.

[0362] "Means of retrieving information from a database" refers to the function of searching for and retrieving appropriate information from a database based on an analyzed query.

[0363] A "means for generating an answer" is an algorithm that creates the optimal answer based on the information obtained and the emotions recognized.

[0364] "Means of sending to the user's terminal" refers to a communication function for sending the generated response to the user's terminal and displaying it.

[0365] "Information on multiple items" refers to information stored in the database, such as various products, services, and inquiries.

[0366] "Means of displaying on the user terminal's display area" refers to a function on the user terminal that visually displays the generated response.

[0367] This invention improves the user experience in a security service system by recognizing the emotions contained in user questions and generating appropriate answers based on those emotions.

[0368] System Overview

[0369] This system includes means for receiving questions from users, means for analyzing questions, means for recognizing emotions, means for retrieving information from a database, means for generating answers, and means for transmitting the generated answers to the user's terminal. The specific processing details and technologies used for each means are described below.

[0370] Hardware and software usage

[0371] The server functions as a central control unit for executing the various measures described later. The server is equipped with a high-performance CPU and GPU, and has sufficient memory and storage.

[0372] User devices include smartphones and tablets, and they send questions entered by the user to the server and receive and display answers from the server.

[0373] The software used includes the TextBlob library for natural language processing (NLP) and the Flask framework for server-client communication.

[0374] Receiving and analyzing questions

[0375] The server receives questions sent from the user's terminal. After receiving the questions, it analyzes the content using natural language processing techniques to identify relevant keywords and categories. During this process, it uses the TextBlob library to perform sentiment analysis and identify positive, negative, and neutral emotions contained in the questions.

[0376] Retrieving information from the database

[0377] Next, the server searches the database for relevant information based on the analysis results and recognized emotions, and retrieves information to generate an appropriate response. The database contains various security-related information and procedures.

[0378] Generating and submitting responses

[0379] Based on the acquired information, the server generates the most appropriate response corresponding to the recognized emotion. For example, if the user indicates "anxiety" or "tension," the server will create a response in a reassuring tone. The generated response is then sent to the user's terminal using a communication module.

[0380] Display on user terminal

[0381] The user's device analyzes the received responses and displays them on the user's screen. The display method is designed to be intuitively easy for the user to understand.

[0382] Specific example

[0383] Here is a specific example of how a user might enter a question about a security issue.

[0384] Example of a prompt

[0385] I'm worried because it seems like my account has been hacked.

[0386] In this case, the server analyzes the question and recognizes it as "I'm worried because it seems my account has been hacked." Sentiment analysis identifies the emotion of "anxiety" and generates an appropriate response. For example, it might return something like, "Here's what to do if your account has been compromised. First, change your password immediately. Next, report any activity that you suspect is unauthorized access to us. Please rest assured."

[0387] In this way, the system of the present invention can achieve a more personalized response that takes into account the user's emotions.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user enters a question into a device such as a smartphone and clicks the send button. This allows the user's question to be captured by the device and sent to the server in JSON format. The input is the user's question, and the output is the sent JSON data.

[0391] Step 2:

[0392] The server receives JSON data sent from the terminal and extracts the question content. The input is data in JSON format, and the output is the extracted question text. This process identifies the user's question content.

[0393] Step 3:

[0394] The server uses natural language processing (NLP) techniques to analyze the extracted question text. Specifically, it uses the TextBlob library to analyze the question content and identify relevant keywords and categories. The input is the question text, and the output is the keywords and categories from the analysis. In this process, the subject of the question is identified.

[0395] Step 4:

[0396] The server uses an emotion recognition engine to recognize the emotions contained in the analyzed question text. The TextBlob library is used again to classify the emotions in the text as "positive," "negative," or "neutral." The input is the question text, and the output is the recognized emotion. In this step, the user's emotion is identified.

[0397] Step 5:

[0398] The server retrieves relevant information from the database based on identified keywords, categories, and sentiment. It performs database queries to find the appropriate information. The input is the analysis results and sentiment data, and the output is the retrieved information. This process ensures that appropriate information is collected.

[0399] Step 6:

[0400] The server generates the most appropriate response based on the acquired information and the recognized emotion. For example, if the user indicates "anxiety," it will create a response in a reassuring tone. The input is the acquired information and emotion data, and the output is the generated response text.

[0401] Step 7:

[0402] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated response text, and the output is the JSON data sent to the terminal. This process provides the user with an answer.

[0403] Step 8:

[0404] The terminal receives a response from the server, parses the JSON data, and displays the answer in the user interface display area. The input is the JSON data received from the server, and the output is the answer text displayed on the user screen. Through this process, the user can obtain the answer to the question.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Second Embodiment]

[0409] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention relates to a system that centrally receives and analyzes questions from users, retrieves information from an appropriate database, generates answers based on that information, and provides them to the user.

[0422] System Overview

[0423] This system includes the following main components: an interface for receiving user questions, a process for analyzing questions, a module for retrieving information by referencing appropriate databases, an algorithm for generating answers based on the retrieved information, and a communication module for sending the generated answers to the user's terminal.

[0424] Program execution steps

[0425] 1. Receiving questions from users

[0426] The user enters the question into the input field on their device and clicks the submit button.

[0427] The terminal retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0428] 2. Analysis of the Question

[0429] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0430] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process understands the user's question and identifies relevant keywords and categories.

[0431] 3. Retrieving information from the database

[0432] The server consults a database containing relevant information based on the analyzed question.

[0433] The database module retrieves the corresponding information and returns the results to the server.

[0434] 4. Generating the answer

[0435] The server generates the best possible answer to the user's question based on the information it has gathered. This process uses pre-defined templates and conditional logic to create answers in natural language.

[0436] 5. Submit your response

[0437] The server converts the generated response into JSON format and sends it to the user's device.

[0438] The device analyzes the received response and displays it in the designated area on the screen.

[0439] Specific example

[0440] 1. Example of user operation

[0441] The user types "What is the warranty period for my new smartphone?" into the text box on the device and presses the send button.

[0442] 2. Example of terminal processing

[0443] The device sends the entered question to the server in JSON format, {'text':'What is the warranty period for the new smartphone?'}.

[0444] 3. Server reception and query analysis

[0445] The server receives this request, analyzes the question, and extracts keywords such as "warranty period" and "smartphone."

[0446] 4. Retrieving information from the database

[0447] The server searches its database for information related to "smartphones" and "warranty period," and retrieves the information, "The warranty period for new smartphones is one year."

[0448] 5. Generate and submit your response.

[0449] The server generates a response stating, "The warranty period for your new smartphone is one year," and sends it back to the device in JSON format.

[0450] The device displays this answer on the screen for the user to see.

[0451] In this way, the system of the present invention provides quick and accurate answers to user questions and enables efficient QA support across all products.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The user enters their question into the text box on their device and clicks the submit button.

[0455] Step 2:

[0456] The device retrieves the question entered by the user and converts the question content into JSON format {"question": "What is the warranty period for the new smartphone?"}.

[0457] Step 3:

[0458] The terminal sends the converted JSON-formatted question to the server as a POST request.

[0459] Step 4:

[0460] The server receives a POST request from the terminal and extracts the question content from the request body.

[0461] Example: Retrieve the question "What is the warranty period for the new smartphone?".

[0462] Step 5:

[0463] The server uses natural language processing (NLP) techniques to analyze the extracted questions.

[0464] Example: Analyze the question and identify the keywords "warranty period" and "smartphone".

[0465] Step 6:

[0466] The server references relevant databases based on the analysis.

[0467] The server searches the database for information related to "smartphone" and "warranty period".

[0468] Step 7:

[0469] The database module searches for the relevant records and returns the results to the server.

[0470] Example: Retrieve the search result "The warranty period for new smartphones is one year."

[0471] Step 8:

[0472] The server generates appropriate answers to the user's questions based on the information it has obtained.

[0473] Example: Generated response: "The warranty period for your new smartphone is one year."

[0474] Step 9:

[0475] The server converts the generated response into JSON format {"answer": "The warranty period for the new smartphone is one year."} and sends it to the device.

[0476] Step 10:

[0477] The terminal receives a response from the server and parses the response in JSON format.

[0478] Example: Analyze and extract the answer portion.

[0479] Step 11:

[0480] The device displays the analyzed responses in a designated area on the user interface.

[0481] Example: Display "The warranty period for new smartphones is one year" in the display area.

[0482] Step 12:

[0483] The user reviews the answers displayed in the display area and enters the next question if necessary.

[0484] Thus, the system of the present invention performs appropriate processing at each step in order to provide real-time and accurate answers to user questions.

[0485] (Example 1)

[0486] 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."

[0487] Conventional question answering systems suffer from the problem of each processing step being independent, resulting in significant time spent on information analysis and retrieval. Furthermore, the lack of established methods for providing appropriate answers quickly can lead to decreased user satisfaction. Additionally, maintaining consistency and coherence when retrieving information from multiple databases presents a significant challenge.

[0488] 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.

[0489] In this invention, the server includes means for receiving questions from users, means for converting the received questions into JSON format and sending them to the server, means for analyzing the received questions, means for identifying relevant keywords and categories from the analyzed questions using natural language processing technology, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, and means for converting the generated answers into JSON format and sending them to the user's terminal. This enables the provision of quick and appropriate answers, improving the user experience. Furthermore, consistency can be maintained even when retrieving information from multiple databases.

[0490] A "user" refers to a person who submits questions or inputs data into the system.

[0491] "Device" refers to a computing device used by a user, and includes smartphones, tablets, and personal computers.

[0492] A "server" refers to a central computing system that receives requests from user terminals and processes and analyzes data.

[0493] "Means of receiving questions" refers to the methods and processes by which the system recognizes and retrieves questions entered by the user through a device.

[0494] JSON format is a lightweight data exchange format for representing data in a structured way.

[0495] "Means of sending to the server" refers to the procedures and methods for transferring a user's question from their terminal to the server.

[0496] "Methods for analyzing questions" refers to the process of understanding a received question and identifying the meaning and intent contained within it.

[0497] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0498] "Means of identifying relevant keywords and categories" refers to the process of extracting important words and categories from the analyzed questions.

[0499] A "database" refers to a collection of data that organizes and stores information, making it searchable and retrievable.

[0500] "Means of obtaining information" refers to the methods and processes for searching for and retrieving necessary information from a database.

[0501] "Means of generating answers" refers to the process of creating appropriate answers to user questions based on acquired information.

[0502] "Means of sending to the user terminal" refers to the procedures and methods for transferring the generated response from the server to the user terminal.

[0503] The "display area" refers to the area on the device screen where information or results are displayed.

[0504] This invention relates to a system that centrally receives and analyzes user questions, retrieves information from an appropriate database, and generates and provides answers to the user based on that information. An embodiment of this system will be described in detail.

[0505] System Configuration

[0506] The system consists mainly of the following components:

[0507] 1. User terminal: This refers to the device used by the user to input questions, and includes smartphones, tablets, and personal computers.

[0508] 2. Server: A central system that receives requests from user terminals, analyzes questions, retrieves information, and generates answers.

[0509] 3. Database: A collection of data that stores necessary information. For example, a database containing information about a product.

[0510] Program processing details

[0511] Receiving questions and converting them to JSON format.

[0512] The user enters a question into the input field on the device and clicks the submit button. The device retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0513] Question analysis

[0514] The server receives POST requests from terminals and extracts the content of the question from the requests. The server uses natural language processing (NLP) techniques to analyze the questions. In this process, libraries such as NLTK and spaCy are used to understand the context of the user's question and identify relevant keywords and categories.

[0515] Retrieving information from the database

[0516] The server searches a database containing relevant information based on the parsed query. This typically involves using a database management system such as MySQL or PostgreSQL. The database module retrieves information based on the specified keywords and returns the results to the server.

[0517] Answer generation

[0518] The server generates the optimal answer to the user's question based on the acquired information. This process can utilize a template engine like Jinja2 and incorporate the algorithms of a generative AI model. For example, it could be generated using OpenAI's GPT-3.

[0519] Submit and display of responses

[0520] The server converts the generated response into JSON format and sends it to the user's device. The device parses the received response and displays it in the display area on the screen. This allows the user to confirm the answer to their question.

[0521] Specific example

[0522] The user enters "What is the warranty period for my new smartphone?" into the text box on their device and presses the submit button. The device converts this input into JSON format and sends it to the server. The server parses the question and extracts the keywords "warranty period" and "smartphone." Next, the server searches its database and retrieves the information "The warranty period for your new smartphone is one year." The server then generates an answer based on this information and sends it to the device. Finally, the device displays this answer on its screen.

[0523] Example of a prompt

[0524] Examples of prompts for a generative AI model include the following:

[0525] User question: What is the warranty period for a new smartphone?

[0526] Answer: The warranty period for the new smartphone is one year.

[0527] In this way, the system can provide smooth and accurate answers to user questions.

[0528] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0529] Step 1:

[0530] The user enters the question into the input field on the device and clicks the submit button.

[0531] The device retrieves this question, converts it to JSON format ({'text':'What is the warranty period for the new smartphone?'}), and sends it to the server as a POST request.

[0532] Input: A question entered by the user (e.g., "What is the warranty period for the new smartphone?").

[0533] Output: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0534] Step 2:

[0535] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0536] The server uses natural language processing (NLP) techniques to analyze the question. This process involves understanding the context of the question and identifying relevant keywords and categories using libraries such as NLTK and spaCy.

[0537] Input: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0538] Output: Analysis results (e.g., keywords such as "warranty period" and "smartphone").

[0539] Specific operation: The server uses an NLP library to extract the keywords "warranty period" and "smartphone" from the question text.

[0540] Step 3:

[0541] The server searches databases containing relevant information based on the analyzed question.

[0542] The database module searches for information based on specified keywords and returns the results to the server. It uses database management systems such as MySQL or PostgreSQL.

[0543] Input: Analysis results (e.g., "warranty period", "smartphone").

[0544] Output: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0545] Specific operation: The server generates a database query using the keywords "warranty period" and "smartphone" and searches the MySQL database. As a result, it retrieves the information, "The warranty period for new smartphones is one year."

[0546] Step 4:

[0547] The server generates the best possible answer to the user's question based on the information it has obtained.

[0548] This process could also involve using a template engine like Jinja2 and incorporating algorithms for generative AI models.

[0549] Input: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0550] Output: Generated answer (e.g., "The warranty period for the new smartphone is one year.").

[0551] Specific operation: The server applies the retrieved data to a template and generates a natural-sounding response such as, "The warranty period for the new smartphone is one year."

[0552] Step 5:

[0553] The server converts the generated response into JSON format and sends it to the user's device.

[0554] The device analyzes the received response and displays it in the designated area on the screen.

[0555] Input: Generated response (e.g., "The warranty period for the new smartphone is one year.").

[0556] Output: The response displayed on the device ("The warranty period for your new smartphone is one year.").

[0557] Specific operation: The server converts the generated response into JSON format ({'response': 'The warranty period for the new smartphone is one year.'}) and sends it to the device. The device receives this and displays it on the screen.

[0558] (Application Example 1)

[0559] 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."

[0560] Traditional e-commerce sites faced the challenge of not being able to provide quick and accurate answers to user questions about products. This raised concerns about potentially diminishing user purchasing intent and lowering customer satisfaction. Furthermore, some questions were complex, making it time-consuming to find appropriate answers, and there was a risk of receiving incorrect information.

[0561] 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.

[0562] In this invention, the server includes means for receiving questions from users, means for analyzing the received questions, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, means for transmitting the generated answers to the user terminal, and means for using the obtained information to be executed on the e-commerce site. This makes it possible to provide quick and accurate answers when users ask questions about products on the e-commerce site.

[0563] "Means of receiving user questions" refers to the means by which users input questions into the system, and which terminals or servers receive those questions.

[0564] "Means for analyzing received questions" refers to methods for analyzing received user questions using natural language processing techniques and algorithms to identify relevant keywords and categories.

[0565] "Means of obtaining relevant information from a database based on analyzed questions" refers to methods for searching and obtaining relevant information from a database based on analyzed keywords and categories.

[0566] "Means for generating answers based on acquired information" refers to methods for creating the optimal answer to a user's question based on the acquired information.

[0567] "Means for sending generated responses to the user's terminal" refers to means for sending generated responses to the user's terminal via data communication.

[0568] "Methods used on an e-commerce site to utilize acquired information" refers to a series of methods used to generate answers to user questions on an e-commerce site and provide those answers to the user.

[0569] "A database containing information on multiple products" means that the database has accumulated detailed information about various products, allowing for the retrieval of appropriate information in response to a question.

[0570] "Using a generative AI model" means utilizing an artificial intelligence model to generate the optimal answer to a user's question, and this model is pre-trained.

[0571] "A means of generating input prompts for a generative AI model to obtain the optimal response" refers to a method of obtaining the optimal response by creating input statements (prompt statements) in a format suitable for the AI ​​model and passing them to the AI ​​model.

[0572] This invention relates to a system for quickly and accurately answering questions users have about products on an e-commerce site. The system has the function of receiving questions from users, analyzing those questions, retrieving relevant information from a database based on the results, generating the optimal answer, and sending it to the user's terminal.

[0573] System Configuration

[0574] The system includes the following main components:

[0575] 1. User Interface: This interface, provided as a smartphone application, allows users to easily input and submit questions.

[0576] 2. Question Analysis Method: On the server side, questions received from users are analyzed using natural language processing (NLP) technology. This analysis helps understand the intent of the question and extract relevant keywords and categories.

[0577] 3. Information Acquisition Method: Based on the analyzed questions, relevant information is retrieved from the database. The database contains detailed information on multiple products.

[0578] 4. Answer Generation Method: An appropriate answer is generated based on the acquired information. A generative AI model is used in this process. The generative AI model provides the optimal answer based on the input prompt sentence.

[0579] 5. Means of sending responses: The generated responses are converted to JSON format and sent to the user's terminal via a communication module.

[0580] Hardware and software to be used

[0581] Smartphone: A device used by users to input questions and receive answers.

[0582] Server: The central component responsible for parsing questions, retrieving information, generating answers, and sending them. The server is built using Flask (a web framework for Python).

[0583] Natural language processing library (spaCy): A tool for analyzing the content of questions.

[0584] Database (SQLite): A lightweight database for storing product information.

[0585] Generative AI model: A model for generating the optimal answer to a question.

[0586] Specific examples of operations

[0587] User action: The user enters "Please tell me the size guide for this jacket" into the smartphone application and sends it.

[0588] Server processing:

[0589] The server receives the question and performs analysis using spaCy.

[0590] Extract keywords related to the question content: "jacket" and "size guide".

[0591] Search and retrieve information about the "size guide" for "jackets" from the SQLite database.

[0592] Using the generated AI model, natural-sounding responses are produced based on the acquired information.

[0593] The generated response, "This is the size guide for this jacket: S, M, L, XL," is sent to the user's device.

[0594] Presenting the answer to the user: The user confirms the answer displayed on their smartphone screen: "This is the size guide for this jacket: S, M, L, XL."

[0595] Examples of prompts for generative AI models

[0596] "User question: 'What is the size guide for this jacket?' Generate the best answer from the information in your database."

[0597] This system allows users to receive quick and accurate answers to their questions on e-commerce sites, leading to improved customer satisfaction and support for their purchasing intent.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1: The user enters and submits the question.

[0600] The user enters a question into the chat interface of the smartphone application and presses the send button. This input is converted to JSON format and sent to the server. The data entered is a text-based question.

[0601] Step 2: The server receives the question.

[0602] The server receives JSON data sent from the user's terminal in POST request format. The received data is parsed to extract the question content. Specifically, the content of the "text" field is extracted from the JSON object and prepared for analysis.

[0603] Step 3: Questionnaire Analysis

[0604] The server analyzes the received questions using natural language processing libraries such as spaCy. The data processing performed here involves extracting important keywords and categories from the question content. The input is the received question text, and the output is a list of analyzed keywords.

[0605] Step 4: Database Search

[0606] The server retrieves relevant information from the SQLite database based on the analyzed keywords. The data operation here involves searching for records containing the keywords and retrieving their contents. The input is a list of keywords, and the output is a record of the relevant information.

[0607] Step 5: Generate Response

[0608] The server uses the information obtained through the search to provide prompts to the generative AI model, which then generates the optimal response. This process involves natural language generation using predefined templates and a generative AI model. The input consists of information retrieved from the database and prompts, while the output is the generated response in text format.

[0609] Step 6: Submit your response

[0610] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated text response, and the output is the sent JSON formatted response data.

[0611] Step 7: The user's device displays the answer.

[0612] The user's terminal parses the JSON data received from the server, extracts the answer text, and displays it on the screen. The user can then verify the answer to the question through the screen. The input is the received JSON data of the answer, and the output is the displayed answer text.

[0613] This series of processing steps allows users to obtain answers to their questions quickly and accurately. Furthermore, by utilizing generative AI models and prompt sentences, natural and appropriate answers are provided.

[0614] 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.

[0615] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This enables more personalized responses that are in line with the user's emotions.

[0616] System Overview

[0617] This system includes the following main components: an interface for receiving questions from the user, a process for analyzing the questions, a module for recognizing the user's emotions using an emotion engine, a module for retrieving information by referring to an appropriate database, an algorithm for generating answers based on the retrieved information and recognized emotions, and a communication module for sending the generated answers to the user's terminal.

[0618] Program execution steps

[0619] 1. Receiving questions from users

[0620] The user enters their question into the text box on their device and clicks the submit button.

[0621] The terminal receives a question from the user and sends the question content to the server in JSON format.

[0622] 2. Analysis of questions and emotions

[0623] The server receives the request from the terminal and extracts the question content.

[0624] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process helps the server understand the user's question and identify relevant keywords and categories.

[0625] The emotion engine uses technologies such as speech, text analysis, and facial recognition to recognize the user's emotions as expressed in the questions. For example, it can detect negative tones and emphasis in the text the user sends.

[0626] 3. Retrieving information from the database

[0627] The server then references relevant databases based on the analysis results and recognized emotions.

[0628] The server searches the database for relevant information and retrieves the results. For example, it might retrieve information such as the warranty period, technical support, and usage instructions for a specific product.

[0629] 4. Generating the answer

[0630] Based on the information it receives, the server generates the most appropriate response according to the perceived emotions. For example, if the user is showing anxiety, it will create a response in a reassuring tone.

[0631] The generated response is converted to JSON format and sent to the device.

[0632] 5. Sending and displaying to the user's terminal

[0633] The terminal receives a response from the server and parses the response in JSON format.

[0634] The device displays the analyzed response in the user interface display area. For example, it might display a response such as, "The warranty period for your new smartphone is one year. Please let us know if you have any questions."

[0635] Specific example

[0636] 1. Example of user operation

[0637] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[0638] 2. Example of terminal processing

[0639] The device sends the entered question to the server in JSON format as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[0640] 3. Server reception and question / sentiment analysis

[0641] The server receives this request, analyzes the question, and, along with the keywords "warranty" and "smartphone," uses its emotion engine to recognize the emotion "worry."

[0642] 4. Retrieving information from the database

[0643] The server searches its database for information related to "smartphones" and "warranties," and retrieves the information, "The warranty period for new smartphones is one year."

[0644] 5. Generate and submit your response.

[0645] The server generates a response saying, "The warranty period for your new smartphone is one year. Please rest assured," and sends it back to the device in JSON format.

[0646] 6. Display results on the user's terminal

[0647] The device analyzes the received response and displays the message "The new smartphone has a one-year warranty. Please rest assured." in the display area on the screen.

[0648] Thus, the system of the present invention aims to improve the user experience by taking into account the user's emotions and providing prompt and appropriate emotionally responsive answers to questions.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[0652] Step 2:

[0653] The device retrieves the question entered by the user and converts the question content into JSON format: {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[0654] Step 3:

[0655] The terminal sends the converted JSON-formatted question to the server as a POST request.

[0656] Step 4:

[0657] The server receives a POST request from the terminal and extracts the question content from the request body.

[0658] Example: Retrieve the question "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?"

[0659] Step 5:

[0660] The server analyzes the extracted questions using natural language processing (NLP) techniques.

[0661] Example: Identify keywords such as "warranty period" or "smartphone".

[0662] Step 6:

[0663] The server passes the analyzed question content to the emotion engine.

[0664] The server uses an emotion engine to recognize the emotion of "worry."

[0665] Step 7:

[0666] The server looks up relevant databases based on keywords and recognized emotions.

[0667] Example: Retrieve the information "The warranty period for new smartphones is one year" from the database.

[0668] Step 8:

[0669] The server generates the optimal response based on the information it has gathered and the emotions it has perceived.

[0670] Example: Generate the response, "The warranty period for your new smartphone is one year. Please rest assured."

[0671] Step 9:

[0672] The server converts the generated response into JSON format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."} and sends it to the device.

[0673] Step 10:

[0674] The terminal receives a response from the server and parses the response in JSON format.

[0675] Example: Analyze and extract the answer portion.

[0676] Step 11:

[0677] The device displays the analyzed responses in the user interface display area.

[0678] Example: The display area will show the message, "The warranty period for your new smartphone is one year. Please rest assured."

[0679] Step 12:

[0680] Users can see the answers displayed in the display area and feel reassured.

[0681] In this way, the system of the present invention takes into account the user's emotions and quickly provides an appropriate emotionally responsive answer to a question.

[0682] (Example 2)

[0683] 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".

[0684] Traditional systems simply provide factual answers to user questions, lacking the ability to provide personalized responses that take user emotions into account. This resulted in a diminished user experience and decreased satisfaction.

[0685] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question from a user, means for analyzing the received question, means for recognizing the analyzed question and emotions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer based on the obtained information and recognized emotions, and means for transmitting the generated answer to the user terminal. This makes it possible to provide a personalized answer that corresponds to the user's emotions.

[0686] "A means of receiving questions from users" refers to a function that retrieves questions entered by users via their devices.

[0687] "Means for analyzing received questions" refers to a function that uses natural language processing technology to analyze the content of the acquired questions and identify keywords and categories.

[0688] "Means for recognizing questions and emotions" refers to the process of recognizing the user's emotions contained in the question, and is a function that uses natural language processing and sentiment analysis technologies.

[0689] "Means of retrieving relevant information from a database" refers to a function that searches and retrieves relevant information from a database based on the analyzed question content and recognized sentiment.

[0690] "Means of generating responses" refers to the process of generating the optimal response based on acquired information and recognized emotions, and is a function that utilizes a generative AI model.

[0691] "Means for sending generated responses to the user's terminal" refers to a function that converts the generated responses into JSON format and sends them to the user's terminal via the network.

[0692] A "database containing multiple types of information" is a database system for storing and managing various types of information, including product information, technical support information, and warranty information.

[0693] The "user terminal display area" refers to the interface portion that allows users to visually view information through their terminal.

[0694] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This system enables a rapid and emotionally personalized response to the user's question.

[0695] This system consists of an interface for receiving questions from the user, a process for analyzing the questions, a module that recognizes the user's emotions using an emotion engine, a module that retrieves information by referring to an appropriate database, an algorithm that generates answers based on the retrieved information and recognized emotions, and a communication module that sends the generated answers to the user's terminal.

[0696] Hardware and software to be used

[0697] Server: A high-performance cloud server will be used. The server will primarily handle question analysis, sentiment recognition, database access, and answer generation.

[0698] User terminal: Provides an interface with the user using devices such as PCs, smartphones, and tablets.

[0699] Natural language processing (NLP) tools: For example, use Google's BERT or OpenAI's GPT-3.

[0700] Emotion recognition engine: Uses Microsoft Azure's Emotion API or IBM's Watson Tone Analyzer.

[0701] Database management system (DBMS): MySQL or PostgreSQL are used.

[0702] Communication protocol: HTTP / HTTPS is used to exchange data between the terminal and the server.

[0703] Specific example

[0704] 1. Receiving questions from users

[0705] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and clicks the send button.

[0706] The terminal converts this question into JSON format and sends it to the server.

[0707] 2. Analysis of questions and emotions

[0708] The server receives the JSON data and extracts the question content.

[0709] The server uses natural language processing (NLP) tools (e.g., GPT-3) to analyze the question and identify keywords.

[0710] The server uses an emotion recognition engine (e.g., Emotion API) to recognize the user's emotion of "worry."

[0711] 3. Retrieving information from the database

[0712] The server consults the database and searches for information related to "smartphones" and "warranty."

[0713] The server retrieves information from the database stating that "the warranty period for new smartphones is one year."

[0714] 4. Generating the answer

[0715] The server uses the following prompts to input into the generated AI model (e.g., GPT-3):

[0716] Prompt: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?' The perceived emotion is 'worried.' Please generate an answer."

[0717] The server generates the response, "The warranty period for your new smartphone is one year. Please rest assured," converts it to JSON format, and sends it to the device.

[0718] 5. Sending and displaying to the user's terminal

[0719] The device parses the received JSON data and extracts the answer.

[0720] The device displays the analyzed response, "The warranty period for your new smartphone is one year. Please rest assured," in the user interface display area.

[0721] The system of the present invention can improve the user experience by recognizing the user's emotions and quickly providing personalized responses based on those emotions.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The user enters a question into a text box on their device and clicks the submit button. For example, they might enter, "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?" The device converts this entered question into JSON format. Specifically, it converts it to the format {"question": "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?"} and sends it to the server as an HTTP POST request.

[0725] Step 2:

[0726] The server receives JSON data sent from the terminal and extracts the question. The input is JSON data such as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered by warranty?"}. The server analyzes the received question using natural language processing (NLP) tools. Specifically, it performs text tokenization, morphological analysis, and syntactic analysis to identify keywords. For example, it identifies the keywords "warranty" and "smartphone". The output is the identified keywords and the question content.

[0727] Step 3:

[0728] The server runs an emotion recognition engine using the analyzed question content and identified keywords. The input is the analysis results and the question content. Using the emotion recognition engine (e.g., Emotion API), it detects the emotion "worry." The output is the recognized emotion. Specifically, it analyzes negative tones and emphasis in the input text and generates the emotion tag "worry."

[0729] Step 4:

[0730] The server looks up a database based on recognized emotions and identified keywords. The input is the keywords and recognized emotions from the analysis results. For example, it searches the database for information related to "smartphone" and "warranty." Using a database management system (e.g., MySQL), it retrieves information such as "The warranty period for a new smartphone is one year." The output is the retrieved information.

[0731] Step 5:

[0732] The server generates a response based on the acquired information and recognized emotions. The input consists of information retrieved from the database and recognized emotions. Specifically, a generative AI model (e.g., GPT-3) is used, and the following prompt is input to the generative AI model: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still under warranty?' and the recognized emotion is worried. Please generate a response." The response generated based on the prompt is, "Your new smartphone has a one-year warranty. Please rest assured." The output is the response.

[0733] Step 6:

[0734] The server converts the generated response text into JSON format and sends it to the terminal. The input is the generated response text. For example, it is converted to the format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."}. The terminal parses the received JSON data and extracts the response text. The output is the extracted response text.

[0735] Step 7:

[0736] The device displays the extracted response text in the user interface display area. The input is the analyzed response text. For example, it might display the response text, "The warranty period for your new smartphone is one year. Please rest assured." The output is visual feedback to the user.

[0737] ---

[0738] The above outlines the specific processing flow of this system, including the inputs and outputs at each step, as well as the specific actions performed.

[0739] (Application Example 2)

[0740] 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."

[0741] Traditional user question response systems only provide uniform answers without considering user emotions, failing to adequately address user doubts and anxieties. Furthermore, especially in security services, users often experience anxiety and tension, and failure to address these emotions can degrade the user experience and damage trust in the service. Therefore, there is a need for a system that recognizes user emotions and provides appropriate responses tailored to those emotions.

[0742] 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. In this invention, the server includes means for receiving questions from the user, means for analyzing the received questions, means for recognizing the emotions contained in the analyzed questions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer corresponding to the acquired information and the recognized emotions, and means for transmitting the generated answer to the user terminal. This enables more personalized responses that take into account the user's emotions, and in particular in security services, it is possible to provide a sense of security to users who are feeling anxious or stressed, thereby improving the user experience and increasing trust in the service.

[0743] "Method for receiving questions from users" refers to a function that sends questions entered by users on their devices to the server.

[0744] "Method for analyzing received questions" refers to a function where the server analyzes the content of user questions received and identifies relevant keywords and categories.

[0745] "Means of recognizing emotions" refers to engines or algorithms that analyze the emotions contained in the user's questions and recognize those emotions.

[0746] "Means of retrieving information from a database" refers to the function of searching for and retrieving appropriate information from a database based on an analyzed query.

[0747] A "means for generating an answer" is an algorithm that creates the optimal answer based on the information obtained and the emotions recognized.

[0748] "Means of sending to the user's terminal" refers to a communication function for sending the generated response to the user's terminal and displaying it.

[0749] "Information on multiple items" refers to information stored in the database, such as various products, services, and inquiries.

[0750] "Means of displaying on the user terminal's display area" refers to a function on the user terminal that visually displays the generated response.

[0751] This invention improves the user experience in a security service system by recognizing the emotions contained in user questions and generating appropriate answers based on those emotions.

[0752] System Overview

[0753] This system includes means for receiving questions from users, means for analyzing questions, means for recognizing emotions, means for retrieving information from a database, means for generating answers, and means for transmitting the generated answers to the user's terminal. The specific processing details and technologies used for each means are described below.

[0754] Hardware and software usage

[0755] The server functions as a central control unit for executing the various measures described later. The server is equipped with a high-performance CPU and GPU, and has sufficient memory and storage.

[0756] User devices include smartphones and tablets, and they send questions entered by the user to the server and receive and display answers from the server.

[0757] The software used includes the TextBlob library for natural language processing (NLP) and the Flask framework for server-client communication.

[0758] Receiving and analyzing questions

[0759] The server receives questions sent from the user's terminal. After receiving the questions, it analyzes the content using natural language processing techniques to identify relevant keywords and categories. During this process, it uses the TextBlob library to perform sentiment analysis and identify positive, negative, and neutral emotions contained in the questions.

[0760] Retrieving information from the database

[0761] Next, the server searches the database for relevant information based on the analysis results and recognized emotions, and retrieves information to generate an appropriate response. The database contains various security-related information and procedures.

[0762] Generating and submitting responses

[0763] Based on the acquired information, the server generates the most appropriate response corresponding to the recognized emotion. For example, if the user indicates "anxiety" or "tension," the server will create a response in a reassuring tone. The generated response is then sent to the user's terminal using a communication module.

[0764] Display on user terminal

[0765] The user's device analyzes the received responses and displays them on the user's screen. The display method is designed to be intuitively easy for the user to understand.

[0766] Specific example

[0767] Here is a specific example of how a user might enter a question about a security issue.

[0768] Example of a prompt

[0769] I'm worried because it seems like my account has been hacked.

[0770] In this case, the server analyzes the question and recognizes it as "I'm worried because it seems my account has been hacked." Sentiment analysis identifies the emotion of "anxiety" and generates an appropriate response. For example, it might return something like, "Here's what to do if your account has been compromised. First, change your password immediately. Next, report any activity that you suspect is unauthorized access to us. Please rest assured."

[0771] In this way, the system of the present invention can achieve a more personalized response that takes into account the user's emotions.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The user enters a question into a device such as a smartphone and clicks the send button. This allows the user's question to be captured by the device and sent to the server in JSON format. The input is the user's question, and the output is the sent JSON data.

[0775] Step 2:

[0776] The server receives JSON data sent from the terminal and extracts the question content. The input is data in JSON format, and the output is the extracted question text. This process identifies the user's question content.

[0777] Step 3:

[0778] The server uses natural language processing (NLP) techniques to analyze the extracted question text. Specifically, it uses the TextBlob library to analyze the question content and identify relevant keywords and categories. The input is the question text, and the output is the keywords and categories from the analysis. In this process, the subject of the question is identified.

[0779] Step 4:

[0780] The server uses an emotion recognition engine to recognize the emotions contained in the analyzed question text. The TextBlob library is used again to classify the emotions in the text as "positive," "negative," or "neutral." The input is the question text, and the output is the recognized emotion. In this step, the user's emotion is identified.

[0781] Step 5:

[0782] The server retrieves relevant information from the database based on identified keywords, categories, and sentiment. It performs database queries to find the appropriate information. The input is the analysis results and sentiment data, and the output is the retrieved information. This process ensures that appropriate information is collected.

[0783] Step 6:

[0784] The server generates the most appropriate response based on the acquired information and the recognized emotion. For example, if the user indicates "anxiety," it will create a response in a reassuring tone. The input is the acquired information and emotion data, and the output is the generated response text.

[0785] Step 7:

[0786] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated response text, and the output is the JSON data sent to the terminal. This process provides the user with an answer.

[0787] Step 8:

[0788] The terminal receives a response from the server, parses the JSON data, and displays the answer in the user interface display area. The input is the JSON data received from the server, and the output is the answer text displayed on the user screen. Through this process, the user can obtain the answer to the question.

[0789] 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.

[0790] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the 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.

[0791] 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.

[0792] [Third Embodiment]

[0793] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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".

[0805] This invention relates to a system that centrally receives and analyzes questions from users, retrieves information from an appropriate database, generates answers based on that information, and provides them to the user.

[0806] System Overview

[0807] This system includes the following main components: an interface for receiving user questions, a process for analyzing questions, a module for retrieving information by referencing appropriate databases, an algorithm for generating answers based on the retrieved information, and a communication module for sending the generated answers to the user's terminal.

[0808] Program execution steps

[0809] 1. Receiving questions from users

[0810] The user enters the question into the input field on their device and clicks the submit button.

[0811] The terminal retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0812] 2. Analysis of the Question

[0813] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0814] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process understands the user's question and identifies relevant keywords and categories.

[0815] 3. Retrieving information from the database

[0816] The server consults a database containing relevant information based on the analyzed question.

[0817] The database module retrieves the corresponding information and returns the results to the server.

[0818] 4. Generating the answer

[0819] The server generates the best possible answer to the user's question based on the information it has gathered. This process uses pre-defined templates and conditional logic to create answers in natural language.

[0820] 5. Submit your response

[0821] The server converts the generated response into JSON format and sends it to the user's device.

[0822] The device analyzes the received response and displays it in the designated area on the screen.

[0823] Specific example

[0824] 1. Example of user operation

[0825] The user types "What is the warranty period for my new smartphone?" into the text box on the device and presses the send button.

[0826] 2. Example of terminal processing

[0827] The device sends the entered question to the server in JSON format, {'text':'What is the warranty period for the new smartphone?'}.

[0828] 3. Server reception and query analysis

[0829] The server receives this request, analyzes the question, and extracts keywords such as "warranty period" and "smartphone."

[0830] 4. Retrieving information from the database

[0831] The server searches its database for information related to "smartphones" and "warranty period," and retrieves the information, "The warranty period for new smartphones is one year."

[0832] 5. Generate and submit your response.

[0833] The server generates a response stating, "The warranty period for your new smartphone is one year," and sends it back to the device in JSON format.

[0834] The device displays this answer on the screen for the user to see.

[0835] In this way, the system of the present invention provides quick and accurate answers to user questions and enables efficient QA support across all products.

[0836] The following describes the processing flow.

[0837] Step 1:

[0838] The user enters their question into the text box on their device and clicks the submit button.

[0839] Step 2:

[0840] The device retrieves the question entered by the user and converts the question content into JSON format {"question": "What is the warranty period for the new smartphone?"}.

[0841] Step 3:

[0842] The terminal sends the converted JSON-formatted question to the server as a POST request.

[0843] Step 4:

[0844] The server receives a POST request from the terminal and extracts the question content from the request body.

[0845] Example: Retrieve the question "What is the warranty period for the new smartphone?".

[0846] Step 5:

[0847] The server uses natural language processing (NLP) techniques to analyze the extracted questions.

[0848] Example: Analyze the question and identify the keywords "warranty period" and "smartphone".

[0849] Step 6:

[0850] The server references relevant databases based on the analysis.

[0851] The server searches the database for information related to "smartphone" and "warranty period".

[0852] Step 7:

[0853] The database module searches for the relevant records and returns the results to the server.

[0854] Example: Retrieve the search result "The warranty period for new smartphones is one year."

[0855] Step 8:

[0856] The server generates appropriate answers to the user's questions based on the information it has obtained.

[0857] Example: Generated response: "The warranty period for your new smartphone is one year."

[0858] Step 9:

[0859] The server converts the generated response into JSON format {"answer": "The warranty period for the new smartphone is one year."} and sends it to the device.

[0860] Step 10:

[0861] The terminal receives a response from the server and parses the response in JSON format.

[0862] Example: Analyze and extract the answer portion.

[0863] Step 11:

[0864] The device displays the analyzed responses in a designated area on the user interface.

[0865] Example: Display "The warranty period for new smartphones is one year" in the display area.

[0866] Step 12:

[0867] The user reviews the answers displayed in the display area and enters the next question if necessary.

[0868] Thus, the system of the present invention performs appropriate processing at each step in order to provide real-time and accurate answers to user questions.

[0869] (Example 1)

[0870] 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."

[0871] Conventional question answering systems suffer from the problem of each processing step being independent, resulting in significant time spent on information analysis and retrieval. Furthermore, the lack of established methods for providing appropriate answers quickly can lead to decreased user satisfaction. Additionally, maintaining consistency and coherence when retrieving information from multiple databases presents a significant challenge.

[0872] 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.

[0873] In this invention, the server includes means for receiving questions from users, means for converting the received questions into JSON format and sending them to the server, means for analyzing the received questions, means for identifying relevant keywords and categories from the analyzed questions using natural language processing technology, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, and means for converting the generated answers into JSON format and sending them to the user's terminal. This enables the provision of quick and appropriate answers, improving the user experience. Furthermore, consistency can be maintained even when retrieving information from multiple databases.

[0874] A "user" refers to a person who submits questions or inputs data into the system.

[0875] "Device" refers to a computing device used by a user, and includes smartphones, tablets, and personal computers.

[0876] A "server" refers to a central computing system that receives requests from user terminals and processes and analyzes data.

[0877] "Means of receiving questions" refers to the methods and processes by which the system recognizes and retrieves questions entered by the user through a device.

[0878] JSON format is a lightweight data exchange format for representing data in a structured way.

[0879] "Means of sending to the server" refers to the procedures and methods for transferring a user's question from their terminal to the server.

[0880] "Methods for analyzing questions" refers to the process of understanding a received question and identifying the meaning and intent contained within it.

[0881] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0882] "Means of identifying relevant keywords and categories" refers to the process of extracting important words and categories from the analyzed questions.

[0883] A "database" refers to a collection of data that organizes and stores information, making it searchable and retrievable.

[0884] "Means of obtaining information" refers to the methods and processes for searching for and retrieving necessary information from a database.

[0885] "Means of generating answers" refers to the process of creating appropriate answers to user questions based on acquired information.

[0886] "Means of sending to the user terminal" refers to the procedures and methods for transferring the generated response from the server to the user terminal.

[0887] The "display area" refers to the area on the device screen where information or results are displayed.

[0888] This invention relates to a system that centrally receives and analyzes user questions, retrieves information from an appropriate database, and generates and provides answers to the user based on that information. An embodiment of this system will be described in detail.

[0889] System Configuration

[0890] The system consists mainly of the following components:

[0891] 1. User terminal: This refers to the device used by the user to input questions, and includes smartphones, tablets, and personal computers.

[0892] 2. Server: A central system that receives requests from user terminals, analyzes questions, retrieves information, and generates answers.

[0893] 3. Database: A collection of data that stores necessary information. For example, a database containing information about a product.

[0894] Program processing details

[0895] Receiving questions and converting them to JSON format.

[0896] The user enters a question into the input field on the device and clicks the submit button. The device retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[0897] Question analysis

[0898] The server receives POST requests from terminals and extracts the content of the question from the requests. The server uses natural language processing (NLP) techniques to analyze the questions. In this process, libraries such as NLTK and spaCy are used to understand the context of the user's question and identify relevant keywords and categories.

[0899] Retrieving information from the database

[0900] The server searches a database containing relevant information based on the parsed query. This typically involves using a database management system such as MySQL or PostgreSQL. The database module retrieves information based on the specified keywords and returns the results to the server.

[0901] Answer generation

[0902] The server generates the optimal answer to the user's question based on the acquired information. This process can utilize a template engine like Jinja2 and incorporate the algorithms of a generative AI model. For example, it could be generated using OpenAI's GPT-3.

[0903] Submit and display of responses

[0904] The server converts the generated response into JSON format and sends it to the user's device. The device parses the received response and displays it in the display area on the screen. This allows the user to confirm the answer to their question.

[0905] Specific example

[0906] The user enters "What is the warranty period for my new smartphone?" into the text box on their device and presses the submit button. The device converts this input into JSON format and sends it to the server. The server parses the question and extracts the keywords "warranty period" and "smartphone." Next, the server searches its database and retrieves the information "The warranty period for your new smartphone is one year." The server then generates an answer based on this information and sends it to the device. Finally, the device displays this answer on its screen.

[0907] Example of a prompt

[0908] Examples of prompts for a generative AI model include the following:

[0909] User question: What is the warranty period for a new smartphone?

[0910] Answer: The warranty period for the new smartphone is one year.

[0911] In this way, the system can provide smooth and accurate answers to user questions.

[0912] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0913] Step 1:

[0914] The user enters the question into the input field on the device and clicks the submit button.

[0915] The device retrieves this question, converts it to JSON format ({'text':'What is the warranty period for the new smartphone?'}), and sends it to the server as a POST request.

[0916] Input: A question entered by the user (e.g., "What is the warranty period for the new smartphone?").

[0917] Output: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0918] Step 2:

[0919] The server receives a POST request from the terminal and extracts the content of the question from the request.

[0920] The server uses natural language processing (NLP) techniques to analyze the question. This process involves understanding the context of the question and identifying relevant keywords and categories using libraries such as NLTK and spaCy.

[0921] Input: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[0922] Output: Analysis results (e.g., keywords such as "warranty period" and "smartphone").

[0923] Specific operation: The server uses an NLP library to extract the keywords "warranty period" and "smartphone" from the question text.

[0924] Step 3:

[0925] The server searches databases containing relevant information based on the analyzed question.

[0926] The database module searches for information based on specified keywords and returns the results to the server. It uses database management systems such as MySQL or PostgreSQL.

[0927] Input: Analysis results (e.g., "warranty period", "smartphone").

[0928] Output: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0929] Specific operation: The server generates a database query using the keywords "warranty period" and "smartphone" and searches the MySQL database. As a result, it retrieves the information, "The warranty period for new smartphones is one year."

[0930] Step 4:

[0931] The server generates the best possible answer to the user's question based on the information it has obtained.

[0932] This process could also involve using a template engine like Jinja2 and incorporating algorithms for generative AI models.

[0933] Input: Database search results (e.g., "The warranty period for new smartphones is one year.").

[0934] Output: Generated answer (e.g., "The warranty period for the new smartphone is one year.").

[0935] Specific operation: The server applies the retrieved data to a template and generates a natural-sounding response such as, "The warranty period for the new smartphone is one year."

[0936] Step 5:

[0937] The server converts the generated response into JSON format and sends it to the user's device.

[0938] The device analyzes the received response and displays it in the designated area on the screen.

[0939] Input: Generated response (e.g., "The warranty period for the new smartphone is one year.").

[0940] Output: The response displayed on the device ("The warranty period for your new smartphone is one year.").

[0941] Specific operation: The server converts the generated response into JSON format ({'response': 'The warranty period for the new smartphone is one year.'}) and sends it to the device. The device receives this and displays it on the screen.

[0942] (Application Example 1)

[0943] 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."

[0944] Traditional e-commerce sites faced the challenge of not being able to provide quick and accurate answers to user questions about products. This raised concerns about potentially diminishing user purchasing intent and lowering customer satisfaction. Furthermore, some questions were complex, making it time-consuming to find appropriate answers, and there was a risk of receiving incorrect information.

[0945] 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.

[0946] In this invention, the server includes means for receiving questions from users, means for analyzing the received questions, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, means for transmitting the generated answers to the user terminal, and means for using the obtained information to be executed on the e-commerce site. This makes it possible to provide quick and accurate answers when users ask questions about products on the e-commerce site.

[0947] "Means of receiving user questions" refers to the means by which users input questions into the system, and which terminals or servers receive those questions.

[0948] "Means for analyzing received questions" refers to methods for analyzing received user questions using natural language processing techniques and algorithms to identify relevant keywords and categories.

[0949] "Means of obtaining relevant information from a database based on analyzed questions" refers to methods for searching and obtaining relevant information from a database based on analyzed keywords and categories.

[0950] "Means for generating answers based on acquired information" refers to methods for creating the optimal answer to a user's question based on the acquired information.

[0951] "Means for sending generated responses to the user's terminal" refers to means for sending generated responses to the user's terminal via data communication.

[0952] "Methods used on an e-commerce site to utilize acquired information" refers to a series of methods used to generate answers to user questions on an e-commerce site and provide those answers to the user.

[0953] "A database containing information on multiple products" means that the database has accumulated detailed information about various products, allowing for the retrieval of appropriate information in response to a question.

[0954] "Using a generative AI model" means utilizing an artificial intelligence model to generate the optimal answer to a user's question, and this model is pre-trained.

[0955] "A means of generating input prompts for a generative AI model to obtain the optimal response" refers to a method of obtaining the optimal response by creating input statements (prompt statements) in a format suitable for the AI ​​model and passing them to the AI ​​model.

[0956] This invention relates to a system for quickly and accurately answering questions users have about products on an e-commerce site. The system has the function of receiving questions from users, analyzing those questions, retrieving relevant information from a database based on the results, generating the optimal answer, and sending it to the user's terminal.

[0957] System Configuration

[0958] The system includes the following main components:

[0959] 1. User Interface: This interface, provided as a smartphone application, allows users to easily input and submit questions.

[0960] 2. Question Analysis Method: On the server side, questions received from users are analyzed using natural language processing (NLP) technology. This analysis helps understand the intent of the question and extract relevant keywords and categories.

[0961] 3. Information Acquisition Method: Based on the analyzed questions, relevant information is retrieved from the database. The database contains detailed information on multiple products.

[0962] 4. Answer Generation Method: An appropriate answer is generated based on the acquired information. A generative AI model is used in this process. The generative AI model provides the optimal answer based on the input prompt sentence.

[0963] 5. Means of sending responses: The generated responses are converted to JSON format and sent to the user's terminal via a communication module.

[0964] Hardware and software to be used

[0965] Smartphone: A device used by users to input questions and receive answers.

[0966] Server: The central component responsible for parsing questions, retrieving information, generating answers, and sending them. The server is built using Flask (a web framework for Python).

[0967] Natural language processing library (spaCy): A tool for analyzing the content of questions.

[0968] Database (SQLite): A lightweight database for storing product information.

[0969] Generative AI model: A model for generating the optimal answer to a question.

[0970] Specific examples of operations

[0971] User action: The user enters "Please tell me the size guide for this jacket" into the smartphone application and sends it.

[0972] Server processing:

[0973] The server receives the question and performs analysis using spaCy.

[0974] Extract keywords related to the question content: "jacket" and "size guide".

[0975] Search and retrieve information about the "size guide" for "jackets" from the SQLite database.

[0976] Using the generated AI model, natural-sounding responses are produced based on the acquired information.

[0977] The generated response, "This is the size guide for this jacket: S, M, L, XL," is sent to the user's device.

[0978] Presenting the answer to the user: The user confirms the answer displayed on their smartphone screen: "This is the size guide for this jacket: S, M, L, XL."

[0979] Examples of prompts for generative AI models

[0980] "User question: 'What is the size guide for this jacket?' Generate the best answer from the information in your database."

[0981] This system allows users to receive quick and accurate answers to their questions on e-commerce sites, leading to improved customer satisfaction and support for their purchasing intent.

[0982] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0983] Step 1: The user enters and submits the question.

[0984] The user enters a question into the chat interface of the smartphone application and presses the send button. This input is converted to JSON format and sent to the server. The data entered is a text-based question.

[0985] Step 2: The server receives the question.

[0986] The server receives JSON data sent from the user's terminal in POST request format. The received data is parsed to extract the question content. Specifically, the content of the "text" field is extracted from the JSON object and prepared for analysis.

[0987] Step 3: Questionnaire Analysis

[0988] The server analyzes the received questions using natural language processing libraries such as spaCy. The data processing performed here involves extracting important keywords and categories from the question content. The input is the received question text, and the output is a list of analyzed keywords.

[0989] Step 4: Database Search

[0990] The server retrieves relevant information from the SQLite database based on the analyzed keywords. The data operation here involves searching for records containing the keywords and retrieving their contents. The input is a list of keywords, and the output is a record of the relevant information.

[0991] Step 5: Generate Response

[0992] The server uses the information obtained through the search to provide prompts to the generative AI model, which then generates the optimal response. This process involves natural language generation using predefined templates and a generative AI model. The input consists of information retrieved from the database and prompts, while the output is the generated response in text format.

[0993] Step 6: Submit your response

[0994] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated text response, and the output is the sent JSON formatted response data.

[0995] Step 7: The user's device displays the answer.

[0996] The user's terminal parses the JSON data received from the server, extracts the answer text, and displays it on the screen. The user can then verify the answer to the question through the screen. The input is the received JSON data of the answer, and the output is the displayed answer text.

[0997] This series of processing steps allows users to obtain answers to their questions quickly and accurately. Furthermore, by utilizing generative AI models and prompt sentences, natural and appropriate answers are provided.

[0998] 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.

[0999] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This enables more personalized responses that are in line with the user's emotions.

[1000] System Overview

[1001] This system includes the following main components: an interface for receiving questions from the user, a process for analyzing the questions, a module for recognizing the user's emotions using an emotion engine, a module for retrieving information by referring to an appropriate database, an algorithm for generating answers based on the retrieved information and recognized emotions, and a communication module for sending the generated answers to the user's terminal.

[1002] Program execution steps

[1003] 1. Receiving questions from users

[1004] The user enters their question into the text box on their device and clicks the submit button.

[1005] The terminal receives a question from the user and sends the question content to the server in JSON format.

[1006] 2. Analysis of questions and emotions

[1007] The server receives the request from the terminal and extracts the question content.

[1008] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process helps the server understand the user's question and identify relevant keywords and categories.

[1009] The emotion engine uses technologies such as speech, text analysis, and facial recognition to recognize the user's emotions as expressed in the questions. For example, it can detect negative tones and emphasis in the text the user sends.

[1010] 3. Retrieving information from the database

[1011] The server then references relevant databases based on the analysis results and recognized emotions.

[1012] The server searches the database for relevant information and retrieves the results. For example, it might retrieve information such as the warranty period, technical support, and usage instructions for a specific product.

[1013] 4. Generating the answer

[1014] Based on the information it receives, the server generates the most appropriate response according to the perceived emotions. For example, if the user is showing anxiety, it will create a response in a reassuring tone.

[1015] The generated response is converted to JSON format and sent to the device.

[1016] 5. Sending and displaying to the user's terminal

[1017] The terminal receives a response from the server and parses the response in JSON format.

[1018] The device displays the analyzed response in the user interface display area. For example, it might display a response such as, "The warranty period for your new smartphone is one year. Please let us know if you have any questions."

[1019] Specific example

[1020] 1. Example of user operation

[1021] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[1022] 2. Example of terminal processing

[1023] The device sends the entered question to the server in JSON format as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[1024] 3. Server reception and question / sentiment analysis

[1025] The server receives this request, analyzes the question, and, along with the keywords "warranty" and "smartphone," uses its emotion engine to recognize the emotion "worry."

[1026] 4. Retrieving information from the database

[1027] The server searches its database for information related to "smartphones" and "warranties," and retrieves the information, "The warranty period for new smartphones is one year."

[1028] 5. Generate and submit your response.

[1029] The server generates a response saying, "The warranty period for your new smartphone is one year. Please rest assured," and sends it back to the device in JSON format.

[1030] 6. Display results on the user's terminal

[1031] The device analyzes the received response and displays the message "The new smartphone has a one-year warranty. Please rest assured." in the display area on the screen.

[1032] Thus, the system of the present invention aims to improve the user experience by taking into account the user's emotions and providing prompt and appropriate emotionally responsive answers to questions.

[1033] The following describes the processing flow.

[1034] Step 1:

[1035] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[1036] Step 2:

[1037] The device retrieves the question entered by the user and converts the question content into JSON format: {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[1038] Step 3:

[1039] The terminal sends the converted JSON-formatted question to the server as a POST request.

[1040] Step 4:

[1041] The server receives a POST request from the terminal and extracts the question content from the request body.

[1042] Example: Retrieve the question "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?"

[1043] Step 5:

[1044] The server analyzes the extracted questions using natural language processing (NLP) techniques.

[1045] Example: Identify keywords such as "warranty period" or "smartphone".

[1046] Step 6:

[1047] The server passes the analyzed question content to the emotion engine.

[1048] The server uses an emotion engine to recognize the emotion of "worry."

[1049] Step 7:

[1050] The server looks up relevant databases based on keywords and recognized emotions.

[1051] Example: Retrieve the information "The warranty period for new smartphones is one year" from the database.

[1052] Step 8:

[1053] The server generates the optimal response based on the information it has gathered and the emotions it has perceived.

[1054] Example: Generate the response, "The warranty period for your new smartphone is one year. Please rest assured."

[1055] Step 9:

[1056] The server converts the generated response into JSON format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."} and sends it to the device.

[1057] Step 10:

[1058] The terminal receives a response from the server and parses the response in JSON format.

[1059] Example: Analyze and extract the answer portion.

[1060] Step 11:

[1061] The device displays the analyzed responses in the user interface display area.

[1062] Example: The display area will show the message, "The warranty period for your new smartphone is one year. Please rest assured."

[1063] Step 12:

[1064] Users can see the answers displayed in the display area and feel reassured.

[1065] In this way, the system of the present invention takes into account the user's emotions and quickly provides an appropriate emotionally responsive answer to a question.

[1066] (Example 2)

[1067] 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."

[1068] Traditional systems simply provide factual answers to user questions, lacking the ability to provide personalized responses that take user emotions into account. This resulted in a diminished user experience and decreased satisfaction.

[1069] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question from a user, means for analyzing the received question, means for recognizing the analyzed question and emotions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer based on the obtained information and recognized emotions, and means for transmitting the generated answer to the user terminal. This makes it possible to provide a personalized answer that corresponds to the user's emotions.

[1070] "A means of receiving questions from users" refers to a function that retrieves questions entered by users via their devices.

[1071] "Means for analyzing received questions" refers to a function that uses natural language processing technology to analyze the content of the acquired questions and identify keywords and categories.

[1072] "Means for recognizing questions and emotions" refers to the process of recognizing the user's emotions contained in the question, and is a function that uses natural language processing and sentiment analysis technologies.

[1073] "Means of retrieving relevant information from a database" refers to a function that searches and retrieves relevant information from a database based on the analyzed question content and recognized sentiment.

[1074] "Means of generating responses" refers to the process of generating the optimal response based on acquired information and recognized emotions, and is a function that utilizes a generative AI model.

[1075] "Means for sending generated responses to the user's terminal" refers to a function that converts the generated responses into JSON format and sends them to the user's terminal via the network.

[1076] A "database containing multiple types of information" is a database system for storing and managing various types of information, including product information, technical support information, and warranty information.

[1077] The "user terminal display area" refers to the interface portion that allows users to visually view information through their terminal.

[1078] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This system enables a rapid and emotionally personalized response to the user's question.

[1079] This system consists of an interface for receiving questions from the user, a process for analyzing the questions, a module that recognizes the user's emotions using an emotion engine, a module that retrieves information by referring to an appropriate database, an algorithm that generates answers based on the retrieved information and recognized emotions, and a communication module that sends the generated answers to the user's terminal.

[1080] Hardware and software to be used

[1081] Server: A high-performance cloud server will be used. The server will primarily handle question analysis, sentiment recognition, database access, and answer generation.

[1082] User terminal: Provides an interface with the user using devices such as PCs, smartphones, and tablets.

[1083] Natural language processing (NLP) tools: For example, use Google's BERT or OpenAI's GPT-3.

[1084] Emotion recognition engine: Uses Microsoft Azure's Emotion API or IBM's Watson Tone Analyzer.

[1085] Database management system (DBMS): MySQL or PostgreSQL are used.

[1086] Communication protocol: HTTP / HTTPS is used to exchange data between the terminal and the server.

[1087] Specific example

[1088] 1. Receiving questions from users

[1089] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and clicks the send button.

[1090] The terminal converts this question into JSON format and sends it to the server.

[1091] 2. Analysis of questions and emotions

[1092] The server receives the JSON data and extracts the question content.

[1093] The server uses natural language processing (NLP) tools (e.g., GPT-3) to analyze the question and identify keywords.

[1094] The server uses an emotion recognition engine (e.g., Emotion API) to recognize the user's emotion of "worry."

[1095] 3. Retrieving information from the database

[1096] The server consults the database and searches for information related to "smartphones" and "warranty."

[1097] The server retrieves information from the database stating that "the warranty period for new smartphones is one year."

[1098] 4. Generating the answer

[1099] The server uses the following prompts to input into the generated AI model (e.g., GPT-3):

[1100] Prompt: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?' The perceived emotion is 'worried.' Please generate an answer."

[1101] The server generates the response, "The warranty period for your new smartphone is one year. Please rest assured," converts it to JSON format, and sends it to the device.

[1102] 5. Sending and displaying to the user's terminal

[1103] The device parses the received JSON data and extracts the answer.

[1104] The device displays the analyzed response, "The warranty period for your new smartphone is one year. Please rest assured," in the user interface display area.

[1105] The system of the present invention can improve the user experience by recognizing the user's emotions and quickly providing personalized responses based on those emotions.

[1106] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1107] Step 1:

[1108] The user enters a question into a text box on their device and clicks the submit button. For example, they might enter, "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?" The device converts this entered question into JSON format. Specifically, it converts it to the format {"question": "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?"} and sends it to the server as an HTTP POST request.

[1109] Step 2:

[1110] The server receives JSON data sent from the terminal and extracts the question. The input is JSON data such as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered by warranty?"}. The server analyzes the received question using natural language processing (NLP) tools. Specifically, it performs text tokenization, morphological analysis, and syntactic analysis to identify keywords. For example, it identifies the keywords "warranty" and "smartphone". The output is the identified keywords and the question content.

[1111] Step 3:

[1112] The server runs an emotion recognition engine using the analyzed question content and identified keywords. The input is the analysis results and the question content. Using the emotion recognition engine (e.g., Emotion API), it detects the emotion "worry." The output is the recognized emotion. Specifically, it analyzes negative tones and emphasis in the input text and generates the emotion tag "worry."

[1113] Step 4:

[1114] The server looks up a database based on recognized emotions and identified keywords. The input is the keywords and recognized emotions from the analysis results. For example, it searches the database for information related to "smartphone" and "warranty." Using a database management system (e.g., MySQL), it retrieves information such as "The warranty period for a new smartphone is one year." The output is the retrieved information.

[1115] Step 5:

[1116] The server generates a response based on the acquired information and recognized emotions. The input consists of information retrieved from the database and recognized emotions. Specifically, a generative AI model (e.g., GPT-3) is used, and the following prompt is input to the generative AI model: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still under warranty?' and the recognized emotion is worried. Please generate a response." The response generated based on the prompt is, "Your new smartphone has a one-year warranty. Please rest assured." The output is the response.

[1117] Step 6:

[1118] The server converts the generated response text into JSON format and sends it to the terminal. The input is the generated response text. For example, it is converted to the format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."}. The terminal parses the received JSON data and extracts the response text. The output is the extracted response text.

[1119] Step 7:

[1120] The device displays the extracted response text in the user interface display area. The input is the analyzed response text. For example, it might display the response text, "The warranty period for your new smartphone is one year. Please rest assured." The output is visual feedback to the user.

[1121] ---

[1122] The above outlines the specific processing flow of this system, including the inputs and outputs at each step, as well as the specific actions performed.

[1123] (Application Example 2)

[1124] 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."

[1125] Traditional user question response systems only provide uniform answers without considering user emotions, failing to adequately address user doubts and anxieties. Furthermore, especially in security services, users often experience anxiety and tension, and failure to address these emotions can degrade the user experience and damage trust in the service. Therefore, there is a need for a system that recognizes user emotions and provides appropriate responses tailored to those emotions.

[1126] 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. In this invention, the server includes means for receiving questions from the user, means for analyzing the received questions, means for recognizing the emotions contained in the analyzed questions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer corresponding to the acquired information and the recognized emotions, and means for transmitting the generated answer to the user terminal. This enables more personalized responses that take into account the user's emotions, and in particular in security services, it is possible to provide a sense of security to users who are feeling anxious or stressed, thereby improving the user experience and increasing trust in the service.

[1127] "Method for receiving questions from users" refers to a function that sends questions entered by users on their devices to the server.

[1128] "Method for analyzing received questions" refers to a function where the server analyzes the content of user questions received and identifies relevant keywords and categories.

[1129] "Means of recognizing emotions" refers to engines or algorithms that analyze the emotions contained in the user's questions and recognize those emotions.

[1130] "Means of retrieving information from a database" refers to the function of searching for and retrieving appropriate information from a database based on an analyzed query.

[1131] A "means for generating an answer" is an algorithm that creates the optimal answer based on the information obtained and the emotions recognized.

[1132] "Means of sending to the user's terminal" refers to a communication function for sending the generated response to the user's terminal and displaying it.

[1133] "Information on multiple items" refers to information stored in the database, such as various products, services, and inquiries.

[1134] "Means of displaying on the user terminal's display area" refers to a function on the user terminal that visually displays the generated response.

[1135] This invention improves the user experience in a security service system by recognizing the emotions contained in user questions and generating appropriate answers based on those emotions.

[1136] System Overview

[1137] This system includes means for receiving questions from users, means for analyzing questions, means for recognizing emotions, means for retrieving information from a database, means for generating answers, and means for transmitting the generated answers to the user's terminal. The specific processing details and technologies used for each means are described below.

[1138] Hardware and software usage

[1139] The server functions as a central control unit for executing the various measures described later. The server is equipped with a high-performance CPU and GPU, and has sufficient memory and storage.

[1140] User devices include smartphones and tablets, and they send questions entered by the user to the server and receive and display answers from the server.

[1141] The software used includes the TextBlob library for natural language processing (NLP) and the Flask framework for server-client communication.

[1142] Receiving and analyzing questions

[1143] The server receives questions sent from the user's terminal. After receiving the questions, it analyzes the content using natural language processing techniques to identify relevant keywords and categories. During this process, it uses the TextBlob library to perform sentiment analysis and identify positive, negative, and neutral emotions contained in the questions.

[1144] Retrieving information from the database

[1145] Next, the server searches the database for relevant information based on the analysis results and recognized emotions, and retrieves information to generate an appropriate response. The database contains various security-related information and procedures.

[1146] Generating and submitting responses

[1147] Based on the acquired information, the server generates the most appropriate response corresponding to the recognized emotion. For example, if the user indicates "anxiety" or "tension," the server will create a response in a reassuring tone. The generated response is then sent to the user's terminal using a communication module.

[1148] Display on user terminal

[1149] The user's device analyzes the received responses and displays them on the user's screen. The display method is designed to be intuitively easy for the user to understand.

[1150] Specific example

[1151] Here is a specific example of how a user might enter a question about a security issue.

[1152] Example of a prompt

[1153] I'm worried because it seems like my account has been hacked.

[1154] In this case, the server analyzes the question and recognizes it as "I'm worried because it seems my account has been hacked." Sentiment analysis identifies the emotion of "anxiety" and generates an appropriate response. For example, it might return something like, "Here's what to do if your account has been compromised. First, change your password immediately. Next, report any activity that you suspect is unauthorized access to us. Please rest assured."

[1155] In this way, the system of the present invention can achieve a more personalized response that takes into account the user's emotions.

[1156] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1157] Step 1:

[1158] The user enters a question into a device such as a smartphone and clicks the send button. This allows the user's question to be captured by the device and sent to the server in JSON format. The input is the user's question, and the output is the sent JSON data.

[1159] Step 2:

[1160] The server receives JSON data sent from the terminal and extracts the question content. The input is data in JSON format, and the output is the extracted question text. This process identifies the user's question content.

[1161] Step 3:

[1162] The server uses natural language processing (NLP) techniques to analyze the extracted question text. Specifically, it uses the TextBlob library to analyze the question content and identify relevant keywords and categories. The input is the question text, and the output is the keywords and categories from the analysis. In this process, the subject of the question is identified.

[1163] Step 4:

[1164] The server uses an emotion recognition engine to recognize the emotions contained in the analyzed question text. The TextBlob library is used again to classify the emotions in the text as "positive," "negative," or "neutral." The input is the question text, and the output is the recognized emotion. In this step, the user's emotion is identified.

[1165] Step 5:

[1166] The server retrieves relevant information from the database based on identified keywords, categories, and sentiment. It performs database queries to find the appropriate information. The input is the analysis results and sentiment data, and the output is the retrieved information. This process ensures that appropriate information is collected.

[1167] Step 6:

[1168] The server generates the most appropriate response based on the acquired information and the recognized emotion. For example, if the user indicates "anxiety," it will create a response in a reassuring tone. The input is the acquired information and emotion data, and the output is the generated response text.

[1169] Step 7:

[1170] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated response text, and the output is the JSON data sent to the terminal. This process provides the user with an answer.

[1171] Step 8:

[1172] The terminal receives a response from the server, parses the JSON data, and displays the answer in the user interface display area. The input is the JSON data received from the server, and the output is the answer text displayed on the user screen. Through this process, the user can obtain the answer to the question.

[1173] 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.

[1174] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the 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.

[1175] 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.

[1176] [Fourth Embodiment]

[1177] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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".

[1190] This invention relates to a system that centrally receives and analyzes questions from users, retrieves information from an appropriate database, generates answers based on that information, and provides them to the user.

[1191] System Overview

[1192] This system includes the following main components: an interface for receiving user questions, a process for analyzing questions, a module for retrieving information by referencing appropriate databases, an algorithm for generating answers based on the retrieved information, and a communication module for sending the generated answers to the user's terminal.

[1193] Program execution steps

[1194] 1. Receiving questions from users

[1195] The user enters the question into the input field on their device and clicks the submit button.

[1196] The terminal retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[1197] 2. Analysis of the Question

[1198] The server receives a POST request from the terminal and extracts the content of the question from the request.

[1199] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process understands the user's question and identifies relevant keywords and categories.

[1200] 3. Retrieving information from the database

[1201] The server consults a database containing relevant information based on the analyzed question.

[1202] The database module retrieves the corresponding information and returns the results to the server.

[1203] 4. Generating the answer

[1204] The server generates the best possible answer to the user's question based on the information it has gathered. This process uses pre-defined templates and conditional logic to create answers in natural language.

[1205] 5. Submit your response

[1206] The server converts the generated response into JSON format and sends it to the user's device.

[1207] The device analyzes the received response and displays it in the designated area on the screen.

[1208] Specific example

[1209] 1. Example of user operation

[1210] The user types "What is the warranty period for my new smartphone?" into the text box on the device and presses the send button.

[1211] 2. Example of terminal processing

[1212] The device sends the entered question to the server in JSON format, {'text':'What is the warranty period for the new smartphone?'}.

[1213] 3. Server reception and query analysis

[1214] The server receives this request, analyzes the question, and extracts keywords such as "warranty period" and "smartphone."

[1215] 4. Retrieving information from the database

[1216] The server searches its database for information related to "smartphones" and "warranty period," and retrieves the information, "The warranty period for new smartphones is one year."

[1217] 5. Generate and submit your response.

[1218] The server generates a response stating, "The warranty period for your new smartphone is one year," and sends it back to the device in JSON format.

[1219] The device displays this answer on the screen for the user to see.

[1220] In this way, the system of the present invention provides quick and accurate answers to user questions and enables efficient QA support across all products.

[1221] The following describes the processing flow.

[1222] Step 1:

[1223] The user enters their question into the text box on their device and clicks the submit button.

[1224] Step 2:

[1225] The device retrieves the question entered by the user and converts the question content into JSON format {"question": "What is the warranty period for the new smartphone?"}.

[1226] Step 3:

[1227] The terminal sends the converted JSON-formatted question to the server as a POST request.

[1228] Step 4:

[1229] The server receives a POST request from the terminal and extracts the question content from the request body.

[1230] Example: Retrieve the question "What is the warranty period for the new smartphone?".

[1231] Step 5:

[1232] The server uses natural language processing (NLP) techniques to analyze the extracted questions.

[1233] Example: Analyze the question and identify the keywords "warranty period" and "smartphone".

[1234] Step 6:

[1235] The server references relevant databases based on the analysis.

[1236] The server searches the database for information related to "smartphone" and "warranty period".

[1237] Step 7:

[1238] The database module searches for the relevant records and returns the results to the server.

[1239] Example: Retrieve the search result "The warranty period for new smartphones is one year."

[1240] Step 8:

[1241] The server generates appropriate answers to the user's questions based on the information it has obtained.

[1242] Example: Generated response: "The warranty period for your new smartphone is one year."

[1243] Step 9:

[1244] The server converts the generated response into JSON format {"answer": "The warranty period for the new smartphone is one year."} and sends it to the device.

[1245] Step 10:

[1246] The terminal receives a response from the server and parses the response in JSON format.

[1247] Example: Analyze and extract the answer portion.

[1248] Step 11:

[1249] The device displays the analyzed responses in a designated area on the user interface.

[1250] Example: Display "The warranty period for new smartphones is one year" in the display area.

[1251] Step 12:

[1252] The user reviews the answers displayed in the display area and enters the next question if necessary.

[1253] Thus, the system of the present invention performs appropriate processing at each step in order to provide real-time and accurate answers to user questions.

[1254] (Example 1)

[1255] 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".

[1256] Conventional question answering systems suffer from the problem of each processing step being independent, resulting in significant time spent on information analysis and retrieval. Furthermore, the lack of established methods for providing appropriate answers quickly can lead to decreased user satisfaction. Additionally, maintaining consistency and coherence when retrieving information from multiple databases presents a significant challenge.

[1257] 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.

[1258] In this invention, the server includes means for receiving questions from users, means for converting the received questions into JSON format and sending them to the server, means for analyzing the received questions, means for identifying relevant keywords and categories from the analyzed questions using natural language processing technology, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, and means for converting the generated answers into JSON format and sending them to the user's terminal. This enables the provision of quick and appropriate answers, improving the user experience. Furthermore, consistency can be maintained even when retrieving information from multiple databases.

[1259] A "user" refers to a person who submits questions or inputs data into the system.

[1260] "Device" refers to a computing device used by a user, and includes smartphones, tablets, and personal computers.

[1261] A "server" refers to a central computing system that receives requests from user terminals and processes and analyzes data.

[1262] "Means of receiving questions" refers to the methods and processes by which the system recognizes and retrieves questions entered by the user through a device.

[1263] JSON format is a lightweight data exchange format for representing data in a structured way.

[1264] "Means of sending to the server" refers to the procedures and methods for transferring a user's question from their terminal to the server.

[1265] "Methods for analyzing questions" refers to the process of understanding a received question and identifying the meaning and intent contained within it.

[1266] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[1267] "Means of identifying relevant keywords and categories" refers to the process of extracting important words and categories from the analyzed questions.

[1268] A "database" refers to a collection of data that organizes and stores information, making it searchable and retrievable.

[1269] "Means of obtaining information" refers to the methods and processes for searching for and retrieving necessary information from a database.

[1270] "Means of generating answers" refers to the process of creating appropriate answers to user questions based on acquired information.

[1271] "Means of sending to the user terminal" refers to the procedures and methods for transferring the generated response from the server to the user terminal.

[1272] The "display area" refers to the area on the device screen where information or results are displayed.

[1273] This invention relates to a system that centrally receives and analyzes user questions, retrieves information from an appropriate database, and generates and provides answers to the user based on that information. An embodiment of this system will be described in detail.

[1274] System Configuration

[1275] The system consists mainly of the following components:

[1276] 1. User terminal: This refers to the device used by the user to input questions, and includes smartphones, tablets, and personal computers.

[1277] 2. Server: A central system that receives requests from user terminals, analyzes questions, retrieves information, and generates answers.

[1278] 3. Database: A collection of data that stores necessary information. For example, a database containing information about a product.

[1279] Program processing details

[1280] Receiving questions and converting them to JSON format.

[1281] The user enters a question into the input field on the device and clicks the submit button. The device retrieves the user's question and sends the question content to the server as a POST request in JSON format.

[1282] Question analysis

[1283] The server receives POST requests from terminals and extracts the content of the question from the requests. The server uses natural language processing (NLP) techniques to analyze the questions. In this process, libraries such as NLTK and spaCy are used to understand the context of the user's question and identify relevant keywords and categories.

[1284] Retrieving information from the database

[1285] The server searches a database containing relevant information based on the parsed query. This typically involves using a database management system such as MySQL or PostgreSQL. The database module retrieves information based on the specified keywords and returns the results to the server.

[1286] Answer generation

[1287] The server generates the optimal answer to the user's question based on the acquired information. This process can utilize a template engine like Jinja2 and incorporate the algorithms of a generative AI model. For example, it could be generated using OpenAI's GPT-3.

[1288] Submit and display of responses

[1289] The server converts the generated response into JSON format and sends it to the user's device. The device parses the received response and displays it in the display area on the screen. This allows the user to confirm the answer to their question.

[1290] Specific example

[1291] The user enters "What is the warranty period for my new smartphone?" into the text box on their device and presses the submit button. The device converts this input into JSON format and sends it to the server. The server parses the question and extracts the keywords "warranty period" and "smartphone." Next, the server searches its database and retrieves the information "The warranty period for your new smartphone is one year." The server then generates an answer based on this information and sends it to the device. Finally, the device displays this answer on its screen.

[1292] Example of a prompt

[1293] Examples of prompts for a generative AI model include the following:

[1294] User question: What is the warranty period for a new smartphone?

[1295] Answer: The warranty period for the new smartphone is one year.

[1296] In this way, the system can provide smooth and accurate answers to user questions.

[1297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1298] Step 1:

[1299] The user enters the question into the input field on the device and clicks the submit button.

[1300] The device retrieves this question, converts it to JSON format ({'text':'What is the warranty period for the new smartphone?'}), and sends it to the server as a POST request.

[1301] Input: A question entered by the user (e.g., "What is the warranty period for the new smartphone?").

[1302] Output: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[1303] Step 2:

[1304] The server receives a POST request from the terminal and extracts the content of the question from the request.

[1305] The server uses natural language processing (NLP) techniques to analyze the question. This process involves understanding the context of the question and identifying relevant keywords and categories using libraries such as NLTK and spaCy.

[1306] Input: Question data in JSON format ({'text':'What is the warranty period for the new smartphone?'}).

[1307] Output: Analysis results (e.g., keywords such as "warranty period" and "smartphone").

[1308] Specific operation: The server uses an NLP library to extract the keywords "warranty period" and "smartphone" from the question text.

[1309] Step 3:

[1310] The server searches databases containing relevant information based on the analyzed question.

[1311] The database module searches for information based on specified keywords and returns the results to the server. It uses database management systems such as MySQL or PostgreSQL.

[1312] Input: Analysis results (e.g., "warranty period", "smartphone").

[1313] Output: Database search results (e.g., "The warranty period for new smartphones is one year.").

[1314] Specific operation: The server generates a database query using the keywords "warranty period" and "smartphone" and searches the MySQL database. As a result, it retrieves the information, "The warranty period for new smartphones is one year."

[1315] Step 4:

[1316] The server generates the best possible answer to the user's question based on the information it has obtained.

[1317] This process could also involve using a template engine like Jinja2 and incorporating algorithms for generative AI models.

[1318] Input: Database search results (e.g., "The warranty period for new smartphones is one year.").

[1319] Output: Generated answer (e.g., "The warranty period for the new smartphone is one year.").

[1320] Specific operation: The server applies the retrieved data to a template and generates a natural-sounding response such as, "The warranty period for the new smartphone is one year."

[1321] Step 5:

[1322] The server converts the generated response into JSON format and sends it to the user's device.

[1323] The device analyzes the received response and displays it in the designated area on the screen.

[1324] Input: Generated response (e.g., "The warranty period for the new smartphone is one year.").

[1325] Output: The response displayed on the device ("The warranty period for your new smartphone is one year.").

[1326] Specific operation: The server converts the generated response into JSON format ({'response': 'The warranty period for the new smartphone is one year.'}) and sends it to the device. The device receives this and displays it on the screen.

[1327] (Application Example 1)

[1328] 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".

[1329] Traditional e-commerce sites faced the challenge of not being able to provide quick and accurate answers to user questions about products. This raised concerns about potentially diminishing user purchasing intent and lowering customer satisfaction. Furthermore, some questions were complex, making it time-consuming to find appropriate answers, and there was a risk of receiving incorrect information.

[1330] 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.

[1331] In this invention, the server includes means for receiving questions from users, means for analyzing the received questions, means for obtaining relevant information from a database based on the analyzed questions, means for generating answers based on the obtained information, means for transmitting the generated answers to the user terminal, and means for using the obtained information to be executed on the e-commerce site. This makes it possible to provide quick and accurate answers when users ask questions about products on the e-commerce site.

[1332] "Means of receiving user questions" refers to the means by which users input questions into the system, and which terminals or servers receive those questions.

[1333] "Means for analyzing received questions" refers to methods for analyzing received user questions using natural language processing techniques and algorithms to identify relevant keywords and categories.

[1334] "Means of obtaining relevant information from a database based on analyzed questions" refers to methods for searching and obtaining relevant information from a database based on analyzed keywords and categories.

[1335] "Means for generating answers based on acquired information" refers to methods for creating the optimal answer to a user's question based on the acquired information.

[1336] "Means for sending generated responses to the user's terminal" refers to means for sending generated responses to the user's terminal via data communication.

[1337] "Methods used on an e-commerce site to utilize acquired information" refers to a series of methods used to generate answers to user questions on an e-commerce site and provide those answers to the user.

[1338] "A database containing information on multiple products" means that the database has accumulated detailed information about various products, allowing for the retrieval of appropriate information in response to a question.

[1339] "Using a generative AI model" means utilizing an artificial intelligence model to generate the optimal answer to a user's question, and this model is pre-trained.

[1340] "A means of generating input prompts for a generative AI model to obtain the optimal response" refers to a method of obtaining the optimal response by creating input statements (prompt statements) in a format suitable for the AI ​​model and passing them to the AI ​​model.

[1341] This invention relates to a system for quickly and accurately answering questions users have about products on an e-commerce site. The system has the function of receiving questions from users, analyzing those questions, retrieving relevant information from a database based on the results, generating the optimal answer, and sending it to the user's terminal.

[1342] System Configuration

[1343] The system includes the following main components:

[1344] 1. User Interface: This interface, provided as a smartphone application, allows users to easily input and submit questions.

[1345] 2. Question Analysis Method: On the server side, questions received from users are analyzed using natural language processing (NLP) technology. This analysis helps understand the intent of the question and extract relevant keywords and categories.

[1346] 3. Information Acquisition Method: Based on the analyzed questions, relevant information is retrieved from the database. The database contains detailed information on multiple products.

[1347] 4. Answer Generation Method: An appropriate answer is generated based on the acquired information. A generative AI model is used in this process. The generative AI model provides the optimal answer based on the input prompt sentence.

[1348] 5. Means of sending responses: The generated responses are converted to JSON format and sent to the user's terminal via a communication module.

[1349] Hardware and software to be used

[1350] Smartphone: A device used by users to input questions and receive answers.

[1351] Server: The central component responsible for parsing questions, retrieving information, generating answers, and sending them. The server is built using Flask (a web framework for Python).

[1352] Natural language processing library (spaCy): A tool for analyzing the content of questions.

[1353] Database (SQLite): A lightweight database for storing product information.

[1354] Generative AI model: A model for generating the optimal answer to a question.

[1355] Specific examples of operations

[1356] User action: The user enters "Please tell me the size guide for this jacket" into the smartphone application and sends it.

[1357] Server processing:

[1358] The server receives the question and performs analysis using spaCy.

[1359] Extract keywords related to the question content: "jacket" and "size guide".

[1360] Search and retrieve information about the "size guide" for "jackets" from the SQLite database.

[1361] Using the generated AI model, natural-sounding responses are produced based on the acquired information.

[1362] The generated response, "This is the size guide for this jacket: S, M, L, XL," is sent to the user's device.

[1363] Presenting the answer to the user: The user confirms the answer displayed on their smartphone screen: "This is the size guide for this jacket: S, M, L, XL."

[1364] Examples of prompts for generative AI models

[1365] "User question: 'What is the size guide for this jacket?' Generate the best answer from the information in your database."

[1366] This system allows users to receive quick and accurate answers to their questions on e-commerce sites, leading to improved customer satisfaction and support for their purchasing intent.

[1367] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1368] Step 1: The user enters and submits the question.

[1369] The user enters a question into the chat interface of the smartphone application and presses the send button. This input is converted to JSON format and sent to the server. The data entered is a text-based question.

[1370] Step 2: The server receives the question.

[1371] The server receives JSON data sent from the user's terminal in POST request format. The received data is parsed to extract the question content. Specifically, the content of the "text" field is extracted from the JSON object and prepared for analysis.

[1372] Step 3: Questionnaire Analysis

[1373] The server analyzes the received questions using natural language processing libraries such as spaCy. The data processing performed here involves extracting important keywords and categories from the question content. The input is the received question text, and the output is a list of analyzed keywords.

[1374] Step 4: Database Search

[1375] The server retrieves relevant information from the SQLite database based on the analyzed keywords. The data operation here involves searching for records containing the keywords and retrieving their contents. The input is a list of keywords, and the output is a record of the relevant information.

[1376] Step 5: Generate Response

[1377] The server uses the information obtained through the search to provide prompts to the generative AI model, which then generates the optimal response. This process involves natural language generation using predefined templates and a generative AI model. The input consists of information retrieved from the database and prompts, while the output is the generated response in text format.

[1378] Step 6: Submit your response

[1379] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated text response, and the output is the sent JSON formatted response data.

[1380] Step 7: The user's device displays the answer.

[1381] The user's terminal parses the JSON data received from the server, extracts the answer text, and displays it on the screen. The user can then verify the answer to the question through the screen. The input is the received JSON data of the answer, and the output is the displayed answer text.

[1382] This series of processing steps allows users to obtain answers to their questions quickly and accurately. Furthermore, by utilizing generative AI models and prompt sentences, natural and appropriate answers are provided.

[1383] 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.

[1384] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This enables more personalized responses that are in line with the user's emotions.

[1385] System Overview

[1386] This system includes the following main components: an interface for receiving questions from the user, a process for analyzing the questions, a module for recognizing the user's emotions using an emotion engine, a module for retrieving information by referring to an appropriate database, an algorithm for generating answers based on the retrieved information and recognized emotions, and a communication module for sending the generated answers to the user's terminal.

[1387] Program execution steps

[1388] 1. Receiving questions from users

[1389] The user enters their question into the text box on their device and clicks the submit button.

[1390] The terminal receives a question from the user and sends the question content to the server in JSON format.

[1391] 2. Analysis of questions and emotions

[1392] The server receives the request from the terminal and extracts the question content.

[1393] The server uses natural language processing (NLP) techniques to analyze the question. This analysis process helps the server understand the user's question and identify relevant keywords and categories.

[1394] The emotion engine uses technologies such as speech, text analysis, and facial recognition to recognize the user's emotions as expressed in the questions. For example, it can detect negative tones and emphasis in the text the user sends.

[1395] 3. Retrieving information from the database

[1396] The server then references relevant databases based on the analysis results and recognized emotions.

[1397] The server searches the database for relevant information and retrieves the results. For example, it might retrieve information such as the warranty period, technical support, and usage instructions for a specific product.

[1398] 4. Generating the answer

[1399] Based on the information it receives, the server generates the most appropriate response according to the perceived emotions. For example, if the user is showing anxiety, it will create a response in a reassuring tone.

[1400] The generated response is converted to JSON format and sent to the device.

[1401] 5. Sending and displaying to the user's terminal

[1402] The terminal receives a response from the server and parses the response in JSON format.

[1403] The device displays the analyzed response in the user interface display area. For example, it might display a response such as, "The warranty period for your new smartphone is one year. Please let us know if you have any questions."

[1404] Specific example

[1405] 1. Example of user operation

[1406] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[1407] 2. Example of terminal processing

[1408] The device sends the entered question to the server in JSON format as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[1409] 3. Server reception and question / sentiment analysis

[1410] The server receives this request, analyzes the question, and, along with the keywords "warranty" and "smartphone," uses its emotion engine to recognize the emotion "worry."

[1411] 4. Retrieving information from the database

[1412] The server searches its database for information related to "smartphones" and "warranties," and retrieves the information, "The warranty period for new smartphones is one year."

[1413] 5. Generate and submit your response.

[1414] The server generates a response saying, "The warranty period for your new smartphone is one year. Please rest assured," and sends it back to the device in JSON format.

[1415] 6. Display results on the user's terminal

[1416] The device analyzes the received response and displays the message "The new smartphone has a one-year warranty. Please rest assured." in the display area on the screen.

[1417] Thus, the system of the present invention aims to improve the user experience by taking into account the user's emotions and providing prompt and appropriate emotionally responsive answers to questions.

[1418] The following describes the processing flow.

[1419] Step 1:

[1420] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and presses the send button.

[1421] Step 2:

[1422] The device retrieves the question entered by the user and converts the question content into JSON format: {"question": "I'm worried because my new smartphone isn't working properly. Is it covered under warranty?"}.

[1423] Step 3:

[1424] The terminal sends the converted JSON-formatted question to the server as a POST request.

[1425] Step 4:

[1426] The server receives a POST request from the terminal and extracts the question content from the request body.

[1427] Example: Retrieve the question "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?"

[1428] Step 5:

[1429] The server analyzes the extracted questions using natural language processing (NLP) techniques.

[1430] Example: Identify keywords such as "warranty period" or "smartphone".

[1431] Step 6:

[1432] The server passes the analyzed question content to the emotion engine.

[1433] The server uses an emotion engine to recognize the emotion of "worry."

[1434] Step 7:

[1435] The server looks up relevant databases based on keywords and recognized emotions.

[1436] Example: Retrieve the information "The warranty period for new smartphones is one year" from the database.

[1437] Step 8:

[1438] The server generates the optimal response based on the information it has gathered and the emotions it has perceived.

[1439] Example: Generate the response, "The warranty period for your new smartphone is one year. Please rest assured."

[1440] Step 9:

[1441] The server converts the generated response into JSON format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."} and sends it to the device.

[1442] Step 10:

[1443] The terminal receives a response from the server and parses the response in JSON format.

[1444] Example: Analyze and extract the answer portion.

[1445] Step 11:

[1446] The device displays the analyzed responses in the user interface display area.

[1447] Example: The display area will show the message, "The warranty period for your new smartphone is one year. Please rest assured."

[1448] Step 12:

[1449] Users can see the answers displayed in the display area and feel reassured.

[1450] In this way, the system of the present invention takes into account the user's emotions and quickly provides an appropriate emotionally responsive answer to a question.

[1451] (Example 2)

[1452] 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".

[1453] Traditional systems simply provide factual answers to user questions, lacking the ability to provide personalized responses that take user emotions into account. This resulted in a diminished user experience and decreased satisfaction.

[1454] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question from a user, means for analyzing the received question, means for recognizing the analyzed question and emotions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer based on the obtained information and recognized emotions, and means for transmitting the generated answer to the user terminal. This makes it possible to provide a personalized answer that corresponds to the user's emotions.

[1455] "A means of receiving questions from users" refers to a function that retrieves questions entered by users via their devices.

[1456] "Means for analyzing received questions" refers to a function that uses natural language processing technology to analyze the content of the acquired questions and identify keywords and categories.

[1457] "Means for recognizing questions and emotions" refers to the process of recognizing the user's emotions contained in the question, and is a function that uses natural language processing and sentiment analysis technologies.

[1458] "Means of retrieving relevant information from a database" refers to a function that searches and retrieves relevant information from a database based on the analyzed question content and recognized sentiment.

[1459] "Means of generating responses" refers to the process of generating the optimal response based on acquired information and recognized emotions, and is a function that utilizes a generative AI model.

[1460] "Means for sending generated responses to the user's terminal" refers to a function that converts the generated responses into JSON format and sends them to the user's terminal via the network.

[1461] A "database containing multiple types of information" is a database system for storing and managing various types of information, including product information, technical support information, and warranty information.

[1462] The "user terminal display area" refers to the interface portion that allows users to visually view information through their terminal.

[1463] This invention relates to a system that recognizes the emotions contained in a user's question and generates an appropriate response based on those emotions. This system enables a rapid and emotionally personalized response to the user's question.

[1464] This system consists of an interface for receiving questions from the user, a process for analyzing the questions, a module that recognizes the user's emotions using an emotion engine, a module that retrieves information by referring to an appropriate database, an algorithm that generates answers based on the retrieved information and recognized emotions, and a communication module that sends the generated answers to the user's terminal.

[1465] Hardware and software to be used

[1466] Server: A high-performance cloud server will be used. The server will primarily handle question analysis, sentiment recognition, database access, and answer generation.

[1467] User terminal: Provides an interface with the user using devices such as PCs, smartphones, and tablets.

[1468] Natural language processing (NLP) tools: For example, use Google's BERT or OpenAI's GPT-3.

[1469] Emotion recognition engine: Uses Microsoft Azure's Emotion API or IBM's Watson Tone Analyzer.

[1470] Database management system (DBMS): MySQL or PostgreSQL are used.

[1471] Communication protocol: HTTP / HTTPS is used to exchange data between the terminal and the server.

[1472] Specific example

[1473] 1. Receiving questions from users

[1474] The user types "My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?" into the text box on their device and clicks the send button.

[1475] The terminal converts this question into JSON format and sends it to the server.

[1476] 2. Analysis of questions and emotions

[1477] The server receives the JSON data and extracts the question content.

[1478] The server uses natural language processing (NLP) tools (e.g., GPT-3) to analyze the question and identify keywords.

[1479] The server uses an emotion recognition engine (e.g., Emotion API) to recognize the user's emotion of "worry."

[1480] 3. Retrieving information from the database

[1481] The server consults the database and searches for information related to "smartphones" and "warranty."

[1482] The server retrieves information from the database stating that "the warranty period for new smartphones is one year."

[1483] 4. Generating the answer

[1484] The server uses the following prompts to input into the generated AI model (e.g., GPT-3):

[1485] Prompt: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still covered under warranty?' The perceived emotion is 'worried.' Please generate an answer."

[1486] The server generates the response, "The warranty period for your new smartphone is one year. Please rest assured," converts it to JSON format, and sends it to the device.

[1487] 5. Sending and displaying to the user's terminal

[1488] The device parses the received JSON data and extracts the answer.

[1489] The device displays the analyzed response, "The warranty period for your new smartphone is one year. Please rest assured," in the user interface display area.

[1490] The system of the present invention can improve the user experience by recognizing the user's emotions and quickly providing personalized responses based on those emotions.

[1491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1492] Step 1:

[1493] The user enters a question into a text box on their device and clicks the submit button. For example, they might enter, "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?" The device converts this entered question into JSON format. Specifically, it converts it to the format {"question": "I'm worried because my new smartphone isn't working properly. Is it still covered by warranty?"} and sends it to the server as an HTTP POST request.

[1494] Step 2:

[1495] The server receives JSON data sent from the terminal and extracts the question. The input is JSON data such as {"question": "I'm worried because my new smartphone isn't working properly. Is it covered by warranty?"}. The server analyzes the received question using natural language processing (NLP) tools. Specifically, it performs text tokenization, morphological analysis, and syntactic analysis to identify keywords. For example, it identifies the keywords "warranty" and "smartphone". The output is the identified keywords and the question content.

[1496] Step 3:

[1497] The server runs an emotion recognition engine using the analyzed question content and identified keywords. The input is the analysis results and the question content. Using the emotion recognition engine (e.g., Emotion API), it detects the emotion "worry." The output is the recognized emotion. Specifically, it analyzes negative tones and emphasis in the input text and generates the emotion tag "worry."

[1498] Step 4:

[1499] The server looks up a database based on recognized emotions and identified keywords. The input is the keywords and recognized emotions from the analysis results. For example, it searches the database for information related to "smartphone" and "warranty." Using a database management system (e.g., MySQL), it retrieves information such as "The warranty period for a new smartphone is one year." The output is the retrieved information.

[1500] Step 5:

[1501] The server generates a response based on the acquired information and recognized emotions. The input consists of information retrieved from the database and recognized emotions. Specifically, a generative AI model (e.g., GPT-3) is used, and the following prompt is input to the generative AI model: "The user's question is 'My new smartphone isn't working properly and I'm worried. Is it still under warranty?' and the recognized emotion is worried. Please generate a response." The response generated based on the prompt is, "Your new smartphone has a one-year warranty. Please rest assured." The output is the response.

[1502] Step 6:

[1503] The server converts the generated response text into JSON format and sends it to the terminal. The input is the generated response text. For example, it is converted to the format {"answer": "The warranty period for your new smartphone is one year. Please rest assured."}. The terminal parses the received JSON data and extracts the response text. The output is the extracted response text.

[1504] Step 7:

[1505] The device displays the extracted response text in the user interface display area. The input is the analyzed response text. For example, it might display the response text, "The warranty period for your new smartphone is one year. Please rest assured." The output is visual feedback to the user.

[1506] ---

[1507] The above outlines the specific processing flow of this system, including the inputs and outputs at each step, as well as the specific actions performed.

[1508] (Application Example 2)

[1509] 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".

[1510] Traditional user question response systems only provide uniform answers without considering user emotions, failing to adequately address user doubts and anxieties. Furthermore, especially in security services, users often experience anxiety and tension, and failure to address these emotions can degrade the user experience and damage trust in the service. Therefore, there is a need for a system that recognizes user emotions and provides appropriate responses tailored to those emotions.

[1511] 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. In this invention, the server includes means for receiving questions from the user, means for analyzing the received questions, means for recognizing the emotions contained in the analyzed questions, means for obtaining relevant information from a database based on the recognized emotions, means for generating an answer corresponding to the acquired information and the recognized emotions, and means for transmitting the generated answer to the user terminal. This enables more personalized responses that take into account the user's emotions, and in particular in security services, it is possible to provide a sense of security to users who are feeling anxious or stressed, thereby improving the user experience and increasing trust in the service.

[1512] "Method for receiving questions from users" refers to a function that sends questions entered by users on their devices to the server.

[1513] "Method for analyzing received questions" refers to a function where the server analyzes the content of user questions received and identifies relevant keywords and categories.

[1514] "Means of recognizing emotions" refers to engines or algorithms that analyze the emotions contained in the user's questions and recognize those emotions.

[1515] "Means of retrieving information from a database" refers to the function of searching for and retrieving appropriate information from a database based on an analyzed query.

[1516] A "means for generating an answer" is an algorithm that creates the optimal answer based on the information obtained and the emotions recognized.

[1517] "Means of sending to the user's terminal" refers to a communication function for sending the generated response to the user's terminal and displaying it.

[1518] "Information on multiple items" refers to information stored in the database, such as various products, services, and inquiries.

[1519] "Means of displaying on the user terminal's display area" refers to a function on the user terminal that visually displays the generated response.

[1520] This invention improves the user experience in a security service system by recognizing the emotions contained in user questions and generating appropriate answers based on those emotions.

[1521] System Overview

[1522] This system includes means for receiving questions from users, means for analyzing questions, means for recognizing emotions, means for retrieving information from a database, means for generating answers, and means for transmitting the generated answers to the user's terminal. The specific processing details and technologies used for each means are described below.

[1523] Hardware and software usage

[1524] The server functions as a central control unit for executing the various measures described later. The server is equipped with a high-performance CPU and GPU, and has sufficient memory and storage.

[1525] User devices include smartphones and tablets, and they send questions entered by the user to the server and receive and display answers from the server.

[1526] The software used includes the TextBlob library for natural language processing (NLP) and the Flask framework for server-client communication.

[1527] Receiving and analyzing questions

[1528] The server receives questions sent from the user's terminal. After receiving the questions, it analyzes the content using natural language processing techniques to identify relevant keywords and categories. During this process, it uses the TextBlob library to perform sentiment analysis and identify positive, negative, and neutral emotions contained in the questions.

[1529] Retrieving information from the database

[1530] Next, the server searches the database for relevant information based on the analysis results and recognized emotions, and retrieves information to generate an appropriate response. The database contains various security-related information and procedures.

[1531] Generating and submitting responses

[1532] Based on the acquired information, the server generates the most appropriate response corresponding to the recognized emotion. For example, if the user indicates "anxiety" or "tension," the server will create a response in a reassuring tone. The generated response is then sent to the user's terminal using a communication module.

[1533] Display on user terminal

[1534] The user's device analyzes the received responses and displays them on the user's screen. The display method is designed to be intuitively easy for the user to understand.

[1535] Specific example

[1536] Here is a specific example of how a user might enter a question about a security issue.

[1537] Example of a prompt

[1538] I'm worried because it seems like my account has been hacked.

[1539] In this case, the server analyzes the question and recognizes it as "I'm worried because it seems my account has been hacked." Sentiment analysis identifies the emotion of "anxiety" and generates an appropriate response. For example, it might return something like, "Here's what to do if your account has been compromised. First, change your password immediately. Next, report any activity that you suspect is unauthorized access to us. Please rest assured."

[1540] In this way, the system of the present invention can achieve a more personalized response that takes into account the user's emotions.

[1541] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1542] Step 1:

[1543] The user enters a question into a device such as a smartphone and clicks the send button. This allows the user's question to be captured by the device and sent to the server in JSON format. The input is the user's question, and the output is the sent JSON data.

[1544] Step 2:

[1545] The server receives JSON data sent from the terminal and extracts the question content. The input is data in JSON format, and the output is the extracted question text. This process identifies the user's question content.

[1546] Step 3:

[1547] The server uses natural language processing (NLP) techniques to analyze the extracted question text. Specifically, it uses the TextBlob library to analyze the question content and identify relevant keywords and categories. The input is the question text, and the output is the keywords and categories from the analysis. In this process, the subject of the question is identified.

[1548] Step 4:

[1549] The server uses an emotion recognition engine to recognize the emotions contained in the analyzed question text. The TextBlob library is used again to classify the emotions in the text as "positive," "negative," or "neutral." The input is the question text, and the output is the recognized emotion. In this step, the user's emotion is identified.

[1550] Step 5:

[1551] The server retrieves relevant information from the database based on identified keywords, categories, and sentiment. It performs database queries to find the appropriate information. The input is the analysis results and sentiment data, and the output is the retrieved information. This process ensures that appropriate information is collected.

[1552] Step 6:

[1553] The server generates the most appropriate response based on the acquired information and the recognized emotion. For example, if the user indicates "anxiety," it will create a response in a reassuring tone. The input is the acquired information and emotion data, and the output is the generated response text.

[1554] Step 7:

[1555] The server converts the generated response into JSON format and sends it to the user's terminal. The input is the generated response text, and the output is the JSON data sent to the terminal. This process provides the user with an answer.

[1556] Step 8:

[1557] The terminal receives a response from the server, parses the JSON data, and displays the answer in the user interface display area. The input is the JSON data received from the server, and the output is the answer text displayed on the user screen. Through this process, the user can obtain the answer to the question.

[1558] 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.

[1559] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the 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.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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."

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] The following is further disclosed regarding the embodiments described above.

[1580] (Claim 1)

[1581] A means of receiving questions from users,

[1582] A means of analyzing the received questions,

[1583] A means of obtaining relevant information from a database based on the analyzed question,

[1584] A means of generating an answer based on the acquired information,

[1585] A means of sending the generated response to the user's terminal,

[1586] A system that includes this.

[1587] (Claim 2)

[1588] The system according to claim 1, wherein the database contains information about multiple products.

[1589] (Claim 3)

[1590] The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal.

[1591] (Claim 4)

[1592] The system according to claim 1, further comprising means for conducting question-and-answer exchanges in real time.

[1593] (Claim 5)

[1594] The system according to claim 1, further comprising means for sending a question in JSON format and receiving an answer in JSON format.

[1595] "Example 1"

[1596] (Claim 1)

[1597] A means of receiving questions from users,

[1598] A method for converting the received question into JSON format and sending it to the server,

[1599] A means of analyzing the received questions,

[1600] A method for identifying relevant keywords and categories from questions analyzed using natural language processing technology,

[1601] A means of obtaining relevant information from a database based on the analyzed question,

[1602] A means of generating an answer based on the acquired information,

[1603] A means of converting the generated response into JSON format and sending it to the user's terminal,

[1604] A system that includes this.

[1605] (Claim 2)

[1606] The system according to claim 1, wherein the database contains information about multiple objects.

[1607] (Claim 3)

[1608] The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal.

[1609] "Application Example 1"

[1610] (Claim 1)

[1611] A means of receiving questions from users,

[1612] A means of analyzing the received questions,

[1613] A means of obtaining relevant information from a database based on the analyzed question,

[1614] A means of generating an answer based on the acquired information,

[1615] A means of sending the generated response to the user's terminal,

[1616] The means used on the e-commerce site to utilize the acquired information,

[1617] A system that includes this.

[1618] (Claim 2)

[1619] The system according to claim 1, which uses a generative AI model that generates the best answer to a user's question, and whose database contains information about multiple products.

[1620] (Claim 3)

[1621] The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal, and means for generating input prompt sentences for a generating AI model to obtain the optimal response.

[1622] "Example 2 of combining an emotion engine"

[1623] (Claim 1)

[1624] A means of receiving questions from users,

[1625] A means of analyzing the received questions,

[1626] A means of recognizing the analyzed questions and emotions,

[1627] A means of retrieving relevant information from a database based on recognized emotions,

[1628] A means for generating a response based on acquired information and recognized emotions,

[1629] A means of sending the generated response to the user's terminal,

[1630] A system that includes this.

[1631] (Claim 2)

[1632] The system according to claim 1, which references a database containing multiple types of information.

[1633] (Claim 3)

[1634] The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal.

[1635] "Application example 2 when combining with an emotional engine"

[1636] (Claim 1)

[1637] A means of receiving questions from users,

[1638] A means of analyzing the received questions,

[1639] A means of recognizing the emotions contained in the analyzed questions,

[1640] A means of retrieving relevant information from a database based on recognized emotions,

[1641] A means for generating a response in accordance with acquired information and recognized emotions,

[1642] A means of sending the generated response to the user's terminal,

[1643] A system that includes this.

[1644] (Claim 2)

[1645] The system according to claim 1, wherein the database contains information relating to multiple items.

[1646] (Claim 3)

[1647] The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal. [Explanation of symbols]

[1648] 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 from users, A means of analyzing the received questions, A means of obtaining relevant information from a database based on the analyzed question, A means of generating an answer based on the acquired information, A means of sending the generated response to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the database includes information about multiple products.

3. The system according to claim 1, further comprising means for displaying the generated response in the display area of ​​the user terminal.

4. The system according to claim 1, further comprising means for conducting question-and-answer exchanges in real time.

5. The system according to claim 1, further comprising means for sending a question in JSON format and receiving an answer in JSON format.

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