system

Abstract

We provide the system. [Solution] A terminal in which the user enters a question, A means of receiving user input and sending it to the server, A server that analyzes user questions and retrieves relevant information from a database, A means of generating an appropriate answer from the search results and sending it back to the device, A means by which the terminal displays the response from the server, 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, the method including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, information regarding in-store support and service contents is diverse, and it is required to obtain such information quickly and accurately. However, in conventional systems, it is a problem that it takes time and effort for a user to manually search for necessary information, and the time efficiency is poor. In addition, there may be a lack of accuracy and speed in search results, which may cause a decrease in customer satisfaction. In order to improve such a situation, there is a demand for a system that allows a user to easily input a question and quickly and accurately obtain necessary information.

Means for Solving the Problems

[0005] This invention provides a system comprising a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server also has means for analyzing the user's question and retrieving relevant information from a database. Furthermore, the invention provides a system that includes means for generating an appropriate answer from the search results and sending it back to the terminal, and means for the terminal to display the answer from the server. By adding means for analyzing the user's question using natural language processing and extracting keywords, and means for formatting the answer generated from the search results in JSON format and sending it back to the terminal, this invention proposes a system that enables users to easily, quickly, and accurately obtain the information they need.

[0006] A "user" is an individual or group that uses this system to input questions and obtain information.

[0007] A "device for entering questions" refers to an electronic device used by a user to enter questions and send those questions to a server, and includes smartphones, tablets, and personal computers.

[0008] A "server" is a computer system that receives questions sent by users, analyzes and retrieves information, generates answers, and sends them back to the terminal.

[0009] "Means of receiving and sending to the server" refers to a combination of software and hardware that performs a series of actions to send questions entered by the user on a terminal to the server.

[0010] "Means of analyzing a question and searching for related information from a database" refers to the process in which a server analyzes a user's question using natural language processing technology and searches the database based on keywords extracted from the results.

[0011] "A means of generating appropriate answers from search results and sending them back to the device" refers to the process of creating appropriate answers to user questions based on information obtained from a database and sending those answers back to the user's device.

[0012] "Means of display" refers to the process by which the terminal visually presents the response received from the server to the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. <0​​​​​​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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server.

[0035] The program for this system works as follows:

[0036] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0037] The server parses the received request and extracts the user's question from the request body. The server then uses a natural language processing engine to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the CrewNavi database to retrieve relevant information.

[0038] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0039] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question. This system significantly improves the efficiency of information retrieval and enhances user convenience.

[0040] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, an answer such as "Our business hours are from 9:00 AM to 6:00 PM on weekdays" is generated and displayed to the user. In this way, the user can easily obtain the information they are looking for.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user launches a dedicated app and enters the question in text format.

[0044] Example: Enter "Please tell me your product return policy."

[0045] Step 2:

[0046] The terminal receives user input and converts its contents into JSON format.

[0047] Example: {"question": "Please tell me your product return policy."}

[0048] Step 3:

[0049] The terminal prepares an HTTP request to the server's API endpoint.

[0050] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0051] Step 4:

[0052] The terminal sends an HTTP request to the server, which includes user input.

[0053] Example: Send it to the server as a POST request.

[0054] Step 5:

[0055] The server receives the HTTP request and extracts the user's question from the request body.

[0056] Example: Extract {"question": "Please tell me your product return policy"}.

[0057] Step 6:

[0058] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0059] Example: Extract using "return policy" as a keyword.

[0060] Step 7:

[0061] The server uses the extracted keywords to send a search query to the CrewNavi database.

[0062] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0063] Step 8:

[0064] The server retrieves the appropriate information from the database.

[0065] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0066] Step 9:

[0067] The server generates appropriate answers to the user's questions from the acquired information and formats them into JSON format.

[0068] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0069] Step 10:

[0070] The server sends the generated response back to the terminal as an HTTP response.

[0071] Example: The generated response is included in the response body and sent back with an HTTP status code of 200 (success).

[0072] Step 11:

[0073] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0074] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}} and display the result.

[0075] Step 12:

[0076] The user checks the answer displayed on the device and obtains the answer to the question.

[0077] Example: Confirm the response, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0078] (Example 1)

[0079] 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."

[0080] Modern users demand information quickly and accurately, but traditional information retrieval systems often take a long time to provide appropriate answers to user questions. Furthermore, there are concerns that search efficiency will decrease as the amount of data handled by the system increases. Additionally, systems capable of accurately analyzing and understanding the meaning of questions entered in natural language are limited, making it difficult to reliably obtain the information users seek.

[0081] 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.

[0082] In this invention, the server includes means for receiving user input, analyzing the user's question, and retrieving relevant information from a storage device; means for generating an appropriate answer from the search results and sending it back to the user terminal; and means for the user terminal to display the answer from the data processing device. This makes it possible to extract keywords using natural language processing based on user input and quickly provide an appropriate answer.

[0083] A "user terminal" is a device used by a user to input questions and display answers from the server.

[0084] A "data processing device" is a device that analyzes a user's question, retrieves relevant information from storage devices, and generates an appropriate answer.

[0085] "Natural language processing" is a technology that analyzes natural language text entered by a user and understands its meaning.

[0086] A "storage device" is a device used to store data that a data processing device will use to retrieve information.

[0087] "Keyword extraction" is the process of extracting important words or phrases from the entered questions.

[0088] A "standard data format" is a format commonly used for exchanging and storing data (e.g., JSON format).

[0089] "Search results" refer to relevant information related to the user's question, retrieved from the storage device.

[0090] "Answer generation" is the process of creating appropriate answers to a user's questions based on the information that has been searched.

[0091] The system according to the present invention comprises a user terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a data processing device. This system includes a data processing device that analyzes the user's question and retrieves related information from a storage device, and means for generating an appropriate answer from the search results and returning it to the user terminal. The user terminal also has means for displaying the answer from the data processing device.

[0092] The user launches a dedicated application and enters a question in text format. For example, the user might enter the question, "What is your product return policy?" The user's device receives this input, converts it to an appropriate format (e.g., JSON) for transmission to the data processing device, and prepares an HTTP request. The prepared request is then sent from the user's device to the data processing device.

[0093] The data processing unit parses the received request and extracts the user's question from the request body. The data processing unit then uses a natural language processing engine (e.g., Google® NLP API or SpaCy) to analyze the question and extract important keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the data processing unit sends a query to the storage device to retrieve the relevant information.

[0094] Based on the acquired information, the data processing unit generates an appropriate answer to the user's question. In this case, the answer generated is, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The data processing unit formats the generated answer in JSON format and sends it back to the user's terminal as an HTTP response.

[0095] The user's device analyzes the received response, extracts the answer text, and displays it to the user. This allows the user to easily obtain the appropriate answer to the question.

[0096] To further illustrate the operation of this system, let's look at an example of how the generating AI model and prompt statements are used. For example, if a user enters the question "What are your business hours?", the prompt statement used will be "Please provide information regarding your business hours." The user's terminal sends this input to the data processing unit, which searches its storage using the keyword "business hours." Based on the retrieved information, the answer "Our business hours are from 9:00 to 18:00 on weekdays" is generated and displayed to the user.

[0097] This invention enables users to easily obtain appropriate answers to their questions, significantly improving the efficiency of information retrieval.

[0098] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0099] Step 1:

[0100] The user launches a dedicated app and enters the question in text format.

[0101] Input: The user types "What is your product return policy?".

[0102] Output: Question data in text format is generated.

[0103] Specific action: The user enters text into the app's input field and presses the submit button.

[0104] Step 2:

[0105] The terminal receives user input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares the HTTP request.

[0106] Input: Question data in text format.

[0107] Output: Question data and HTTP request converted to JSON format.

[0108] Specific operation: The terminal converts the text data into { "question": "Please tell me the product return policy"} and generates an HTTP request with the header information set.

[0109] Step 3:

[0110] The terminal sends the prepared HTTP request to the server.

[0111] Input: HTTP request containing question data in JSON format.

[0112] Output: HTTP request sent to the server.

[0113] Specific action: The device sends an HTTP POST request to the endpoint (e.g., https: / / api.example.com / questions).

[0114] Step 4:

[0115] The server parses the received HTTP request and extracts the user's question from the request body.

[0116] Input: The HTTP request sent to the server.

[0117] Output: Extracted question text.

[0118] Specific operation: The server uses a JSON parser to analyze the request body and obtain the question text ("What is your product return policy?").

[0119] Step 5:

[0120] The server uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract important keywords.

[0121] Input: Extracted question text.

[0122] Output: Extracted keywords.

[0123] Specific operation: The server sends text to the NLP engine, and the NLP engine extracts the keyword "return policy".

[0124] Step 6:

[0125] The server sends a query to the storage device based on the extracted keywords and retrieves the relevant information.

[0126] Input: Extracted keywords.

[0127] Output: Related information.

[0128] Specific operation: The server executes an SQL query (e.g., SELECT FROM policies WHERE keyword = 'return policy') to retrieve the corresponding entry from storage.

[0129] Step 7:

[0130] The server generates appropriate answers to user questions based on the information it has obtained.

[0131] Input: Related information.

[0132] Output: The generated answer text.

[0133] Specific operation: The server analyzes the acquired information and generates a response stating, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0134] Step 8:

[0135] The server formats the generated response into JSON format and sends it back to the user's device as an HTTP response.

[0136] Input: Generated response text.

[0137] Output: HTTP response containing answer data in JSON format.

[0138] Specific operation: The server converts the response text to { "answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."} and sends it back with HTTP status 200 (OK).

[0139] Step 9:

[0140] The system analyzes the HTTP response received by the user's device, extracts the response text, and displays it to the user.

[0141] Input: HTTP response containing response data in JSON format.

[0142] Output: Extracted response text.

[0143] Specific operation: The user's device parses the JSON data from the response body, retrieves the content of the "answer" field, and displays it in the app's UI.

[0144] (Application Example 1)

[0145] 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."

[0146] The objective of this invention is to efficiently provide staff with inventory status and work instructions at a logistics center, thereby improving operational efficiency and speeding up information retrieval. Conventional systems can cause delays in work due to the time required to search for information and obtain appropriate answers. To solve this problem, it is necessary to construct a highly efficient information provision system using natural language processing.

[0147] 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.

[0148] In this invention, the server includes means for analyzing a user's question and retrieving relevant information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, and means for the terminal to display the answer from the server. This makes it possible to install the system on equipment in a logistics center and quickly provide inventory status and work instructions.

[0149] A "user input device" is an electronic device used by users to input questions in text or voice format.

[0150] "Means for receiving user input and sending it to the server" refers to the system component that has the function of converting user questions entered on a terminal into an appropriate format and sending them to the server via a communication network.

[0151] The "server component" refers to the system part that analyzes the user's question received, searches for relevant information in the database, and generates an answer based on the retrieved information.

[0152] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to the system component that has the function of creating an answer based on the search results on the server and sending that answer back to the terminal.

[0153] "Means by which a terminal displays a response from a server" refers to a terminal function that displays the response sent back from the server to the user visually or audibly.

[0154] "A means of quickly providing inventory status and work instructions by being installed on equipment within the logistics center" refers to the system component that is installed on equipment used in the logistics center and provides appropriate information such as inventory status and work instructions quickly by allowing staff to input questions.

[0155] The system according to the present invention is configured to efficiently provide inventory status and work instructions within a logistics center. This system includes a terminal for users to input questions and means for receiving user input and transmitting it to a server. The server also has means for analyzing the user's questions, searching for relevant information in a database, generating an appropriate answer from the search results, and then returning it to the terminal. In addition, the terminal has means for displaying the answer from the server.

[0156] System Overview

[0157] 1. User terminal

[0158] The user terminal has the ability to accept questions in text format or voice input. This terminal is equipped with the ability to send user input to a server, specifically by transmitting data via the network.

[0159] 2. Server

[0160] The server analyzes the received question using a natural language processing engine. This engine uses a model such as BERT to extract important keywords and sends queries to a database. Based on the information obtained, the server generates an appropriate answer to the user's question, formats it in JSON format, and sends it back to the terminal.

[0161] 3. Database

[0162] The database to which the server sends search queries holds detailed data on inventory information and work orders. This database is frequently updated to ensure that it provides the most up-to-date information.

[0163] Specific example of processing

[0164] Suppose a user enters the question, "What is the stock status?" The device receives this input, converts it to JSON format, and sends it to the server as an HTTP request. The server parses this request and extracts the keyword "stock status." It then sends a query to the database to retrieve the relevant stock information. Based on the retrieved information, the server generates the answer, "There are currently 100 items in stock," and sends it back to the device in JSON format. The device parses this answer and displays it to the user.

[0165] Hardware and software to be used

[0166] User device: Smartphone, tablet, or PC

[0167] Server: A cloud server with high-performance computing resources (e.g., AWS® or Google Cloud)

[0168] Natural language processing engines: BERT, GPT, etc.

[0169] Databases: Relational database management systems such as MySQL (registered trademark) and PostgreSQL.

[0170] Example of a prompt

[0171] As an example of a prompt, consider the text-based question, "What is the stock status?" In response to this question, the system performs the processing described above and provides a quick and appropriate answer.

[0172] As described above, the system of the present invention realizes increased efficiency and speed in providing information at logistics centers.

[0173] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0174] Step 1:

[0175] The user enters a question.

[0176] The user launches a dedicated app on their device and enters their question in text or voice format. For example, they might enter the question, "What is the stock status?" This input is processed as a prompt. The input data can be text or voice data.

[0177] Step 2:

[0178] The terminal sends user input to the server.

[0179] The terminal receives user input and converts it into an appropriate format (e.g., JSON). The converted data is prepared as an HTTP request and sent to the server. Specifically, the data is sent using the terminal's network capabilities. The input data is text data, and the output data is formatted JSON data.

[0180] Step 3:

[0181] The server analyzes the user's question.

[0182] The server parses the received request and extracts the user's question from the request body. Then, it uses a natural language processing engine (e.g., BERT or GPT) to analyze the question and extract important keywords. For example, the keyword "stock status" might be extracted. The input data in this step is JSON data, and the output data is the analyzed keywords.

[0183] Step 4:

[0184] The server searches the database.

[0185] The server uses the extracted keywords to send queries to the database and retrieve relevant information. For example, it searches the database for information related to "inventory status." At this time, the database query is executed, and inventory information is retrieved as a result. The input data is keywords, and the output data is the search results.

[0186] Step 5:

[0187] The server generates the appropriate answer.

[0188] The server generates an appropriate answer to the user's question based on the information it has obtained. For example, it might generate the answer, "We currently have 100 items in stock." The generated answer is formatted in JSON format. The input data is the search results, and the output data is the formatted answer in JSON format.

[0189] Step 6:

[0190] The server sends the response back to the terminal.

[0191] The server sends the generated response back to the terminal as an HTTP response. The terminal receives this response and performs analysis. The input data is the response JSON, and the output data is the HTTP response.

[0192] Step 7:

[0193] The device will display the answer.

[0194] The device analyzes the received response, extracts the response text, and displays it to the user. For example, the text "We currently have 100 items in stock" might be displayed on the device's screen. The input data is the HTTP response, and the output data is the displayed text.

[0195] 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.

[0196] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server. Furthermore, the system is equipped with an emotion engine that recognizes emotions from the user's input and means for adjusting the answer based on the user's emotions.

[0197] The program for this system works as follows:

[0198] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0199] The server parses the received request and extracts the user's question from the request body. Then, the server uses a natural language processing engine to analyze the question and extract important keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the database to retrieve relevant information.

[0200] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0201] Furthermore, the server uses an emotion engine to recognize emotions from user input. The emotion engine analyzes the question entered by the user and detects the user's emotional state. For example, if the user enters the question, "What do you mean I can't return it?", the emotion engine will detect that the user is angry. Based on this emotional state, the server adjusts the tone and content of the response. In this case, a response such as, "We apologize for the inconvenience. Please contact customer support for details on returns," would be generated.

[0202] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question, and the emotion engine enables even more appropriate responses. This system not only significantly improves the efficiency of information retrieval but also contributes to increased customer satisfaction by enabling flexible responses that respond to the user's emotions.

[0203] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, the server generates the answer, "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for using our service!" to the answer. This can increase the user's satisfaction with the service.

[0204] The following describes the processing flow.

[0205] Step 1:

[0206] The user launches a dedicated app and enters the question in text format.

[0207] Example: Enter "Please tell me your product return policy."

[0208] Step 2:

[0209] The terminal receives user input and converts its contents into JSON format.

[0210] Example: {"question": "Please tell me your product return policy."}

[0211] Step 3:

[0212] The terminal prepares an HTTP request to the server's API endpoint.

[0213] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0214] Step 4:

[0215] The terminal sends an HTTP request to the server, which includes user input.

[0216] Example: Send it to the server as a POST request.

[0217] Step 5:

[0218] The server receives the HTTP request and extracts the user's question from the request body.

[0219] Example: Extract {"question": "Please tell me your product return policy"}.

[0220] Step 6:

[0221] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0222] Example: Extract using "return policy" as a keyword.

[0223] Step 7:

[0224] The server uses an emotion engine to analyze the user's question and detect the user's emotional state.

[0225] Example: Detect the user's emotion of "confusion" from the question.

[0226] Step 8:

[0227] The server uses the extracted keywords to send a search query to the information database.

[0228] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0229] Step 9:

[0230] The server retrieves the appropriate information from the database.

[0231] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0232] Step 10:

[0233] The server uses an emotion engine to detect the user's emotional state and adjusts the tone and content of its response accordingly.

[0234] Example: To a user who appears confused, add a polite tone such as, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0235] Step 11:

[0236] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response.

[0237] For example, send a response in the format of: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0238] Step 12:

[0239] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0240] Example: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."} is received and displayed to the user.

[0241] Step 13:

[0242] The user checks the answer displayed on the device and obtains the answer to the question.

[0243] Example: Check the response, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0244] (Example 2)

[0245] 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".

[0246] Conventional information retrieval systems have a problem not only in generating appropriate answers to user questions, but also in responding based on user emotions. Therefore, in order to improve user satisfaction, it is necessary to recognize user emotions and provide flexible answers that are tailored to them.

[0247] 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 analyzing the user's question and searching for relevant information from a database, means for generating an appropriate answer from the search results, and means for recognizing the user's emotions from the user's input and adjusting the answer. As a result, the user not only receives an appropriate answer to their question but also receives a flexible response that is in line with their emotions.

[0248] A "user" refers to a person who uses the system to input questions and obtain answers.

[0249] A "terminal" refers to a device used by a user to input questions and communicate with a server. Examples include smartphones and personal computers.

[0250] A "server" refers to a device or system that analyzes user input, searches for information in a database, generates an appropriate response, and sends it back to the terminal.

[0251] A "database" refers to a digital storage location where information is accumulated, and is the target of searches performed by a server using queries.

[0252] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0253] An "emotion engine" refers to software or algorithms that recognize emotions from user input and enable responses that correspond to those emotions.

[0254] An "HTTP request" is a part of the communication protocol used by a terminal to request data from a server, and specifically refers to the method used in web-based communication.

[0255] JSON format is a format for representing data in text format and giving it structure, and is mainly used for sending and receiving data.

[0256] "Keywords" refer to vocabulary extracted from user questions that are considered important for database searches and answer generation.

[0257] "Emotion recognition" refers to technology that identifies emotions from user input and determines the emotional state.

[0258] This system includes a terminal where the user enters a question, and a means for receiving the user input and sending it to a server. The server has means for analyzing the user's question and searching for relevant information in a database. Furthermore, it has means for generating an appropriate answer from the search results and sending that answer back to the terminal. The terminal also has means for displaying the answer from the server, providing the user with appropriate information.

[0259] First, the user launches a dedicated application and enters the question in text format. This question is then captured by the device as text.

[0260] The terminal converts the question received from the user into an HTTP request and sends it to the server. Specifically, the terminal parses the question into JSON format and prepares the HTTP request as follows:

[0261] json

[0262] {

[0263] "Question": "Please tell me your product return policy."

[0264] }

[0265] This JSON data is then sent to the server's endpoint.

[0266] The server receives this request and extracts the question content from the request body. This question content is then analyzed using a natural language processing engine (such as SpaCy or BERT) to extract important keywords. For example, if the user asks "What is the product return policy?", the keyword "return policy" will be extracted.

[0267] The server uses these extracted keywords to send queries to a database (such as PostgreSQL or MySQL) and retrieve the relevant information. For example, the database might contain information such as, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0268] Based on the information obtained, the server generates an appropriate response for the user. This response is then formatted again into JSON and sent back to the terminal as an HTTP response.

[0269] Furthermore, the server uses an emotion engine (e.g., IBM Watson® Tone Analyzer) to analyze the user's emotional state and provide a more appropriate response. For example, if a user asks, "What do you mean I can't return this?", the server uses the emotion engine to detect the user's anger. Based on this emotional state, the server adjusts the content and tone of its response, generating a reply such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[0270] The terminal analyzes the response received from the server and displays the answer text to the user. This entire process allows the user not only to easily obtain answers to their questions but also to receive flexible responses that are tailored to their emotions.

[0271] For example, if a user enters the question "What are your business hours?", this question is sent from the terminal to the server, which then searches its database using the keyword "business hours". Based on the retrieved information, the server generates the answer "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for your continued patronage!" to the answer.

[0272] Example of a prompt:

[0273] "When a user types 'What is your product return policy?', generate an HTTP request that converts this question into JSON format and sends it to the server. Also, describe the process by which the server generates an appropriate answer to this question."

[0274] This system provides detailed processing procedures and specific examples necessary to concretely implement the technical scope of the invention, and serves as a standard for others to accurately understand and practice this invention.

[0275] The flow of the specific process in Example 2 will be described using FIG. 13.

[0276] Processing step:

[0277] Step 1:

[0278] The user inputs a question.

[0279] Specific operation:

[0280] The user launches the dedicated app and inputs the question in the text box.

[0281] Example: Input "Tell me about the product return policy".

[0282] Input: The user's question text.

[0283] Output: The input question text.

[0284] Step 2:

[0285] The terminal sends the input question to the server.

[0286] Specific operation:

[0287] The terminal receives the input question and converts it into JSON format.

[0288] Example of the converted JSON:

[0289] json

[0290] {

[0291] "question": "Tell me about the product return policy"

[0292] }

[0293] The terminal sets this JSON in the body of the HTTP request and sends it to the server endpoint.

[0294] Input: The input question text.

[0295] Output: The HTTP request to be sent to the server.

[0296] Step 3: [[ID=十三]]

[0297] The server analyzes the question.

[0298] Specific operations:

[0299] The server analyzes the received HTTP request and extracts the question from the request body.

[0300] The server uses a natural language processing engine (e.g., SpaCy or BERT) to analyze the question and extract important keywords.

[0301] Example: The keyword "return policy" is extracted.

[0302] Input: HTTP request (including the question in JSON format).

[0303] Output: The extracted keyword (e.g., "return policy").

[0304] Step 4:

[0305] The server searches the database.

[0306] Specific operations:

[0307] The server sends a query to the database based on the extracted keywords.

[0308] Example:

[0309] sql

[0310] SELECT policy FROM return_policies WHERE keyword = 'return_policies';

[0311] Retrieve relevant information from the database.

[0312] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0313] Input: Extracted keywords (e.g., "return policy").

[0314] Output: Information retrieved from the database.

[0315] Step 5:

[0316] The server generates the answer.

[0317] Specific actions:

[0318] The server generates appropriate answers to the user's questions based on the information it has obtained.

[0319] The generated response is formatted into JSON and sent back to the terminal as an HTTP response.

[0320] example:

[0321] json

[0322] {

[0323] "Response": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0324] }

[0325] Input: Information retrieved from the database.

[0326] Output: Generated response (in JSON format).

[0327] Step 6:

[0328] The server uses an emotion engine to recognize emotions.

[0329] Specific actions:

[0330] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's question and recognize its sentiment.

[0331] Example: The system detects "anger" in response to the question, "What do you mean I can't return it?"

[0332] Input: User's question text.

[0333] Output: Detected emotional state (e.g., "anger").

[0334] Step 7:

[0335] The server adjusts its response based on emotions.

[0336] Specific actions:

[0337] The server adjusts the tone and content of the response based on the results of the emotion engine.

[0338] Example: Response when "anger" is detected:

[0339] "We apologize for any inconvenience this may have caused. Please contact customer support for details regarding returns."

[0340] Input: Detected emotional state.

[0341] Output: Adjusted response text.

[0342] Step 8:

[0343] The device will display the answer.

[0344] Specific actions:

[0345] The terminal analyzes the response received from the server and extracts the answer text.

[0346] The extracted response text is displayed to the user.

[0347] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0348] Input: Response received from the server (in JSON format).

[0349] Output: The response text displayed to the user.

[0350] The above outlines the specific processing steps of this system. This allows users not only to receive appropriate answers to their questions but also to receive flexible responses that take their emotions into account.

[0351] (Application Example 2)

[0352] 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".

[0353] Traditional information retrieval systems have the drawback of only being able to provide a uniform answer to user-inputted questions, making it difficult to respond appropriately to users' emotions and circumstances. Furthermore, depending on the content of the question, it may not be possible to clearly define what the user wants to know, often resulting in a long wait to obtain an accurate answer.

[0354] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input and transmitting it to the server, means for analyzing the user's question and searching for relevant information from a database, means for recognizing emotions from the user's input and adjusting the tone and content of the response based on the recognized emotions, and means for generating and providing appropriate prompt sentences using a generative AI model. This makes it possible to quickly provide appropriate answers that are in line with the user's emotions and situation.

[0355] "The device on which the user enters the question" refers to a device used by the user to enter and submit a question in text format, and includes smartphones, tablets, or computers.

[0356] "Means for receiving user input and sending it to the server" refers to a software algorithm or protocol for transferring user-entered questions to a server.

[0357] A "server that analyzes user questions and retrieves relevant information from a database" refers to a server that uses natural language processing technology to analyze user-inputted questions and retrieves relevant information from a database.

[0358] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to a server-based processing function that generates an appropriate answer based on information obtained from a database and sends it back to the terminal from which the user entered the question.

[0359] "A means for a terminal to display a response from a server" refers to software that has the function of displaying the response received from the server on the screen of the terminal on which the user entered the question.

[0360] "A means of recognizing emotions from user input and adjusting responses" refers to an emotion analysis algorithm that analyzes the emotions from the questions entered by the user and adjusts the tone and content of the response based on the detected emotions.

[0361] "A means of analyzing user questions and extracting keywords using natural language processing" refers to an algorithm that uses natural language processing techniques to analyze user-entered questions and identify important words or phrases.

[0362] "A means of formatting the answers generated from search results into JSON format and sending them back to the terminal" refers to a function that formats the answers generated based on information obtained from the database into JSON format and sends them back to the terminal from which the user entered the question.

[0363] "Means for generating and providing appropriate prompt sentences using a generative AI model" refers to a function that uses a generative AI model to clarify the content of a question and derive an appropriate prompt sentence when a user inputs a question.

[0364]

[0365] This invention is a system that includes a terminal in which a user inputs a question, means for receiving user input and transmitting it to a server, server means for analyzing the user's question and searching for related information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, means for the terminal to display the answer from the server, means for recognizing emotions from the user's input and adjusting the answer, and means for generating and providing an appropriate prompt sentence using a generative AI model.

[0366] Description of the system's programs and processes:

[0367] 1. User question input and sentiment analysis

[0368] A smartphone, tablet, or computer is used as the terminal. Users input questions in text format using these devices. For example, a question such as "What is your product return policy?" This input is analyzed by an emotion analysis algorithm to determine the user's emotional state. Emotion analysis is performed based on specific keywords and symbols, such as "!" or "why," to detect emotions from the entered text.

[0369] 2. Question Analysis and Information Retrieval

[0370] After receiving user input, the terminal converts this input to JSON format and sends it to the server. The server parses the received input using natural language processing techniques (e.g., Python's natural language processing library) and extracts keywords. For example, the keyword "return policy" might be extracted. Based on this keyword, the server queries the database to retrieve relevant information.

[0371] 3. Generating and adjusting responses

[0372] The server generates an appropriate response based on information retrieved from the database. This response is then adjusted based on the results of sentiment analysis. For example, if a user enters "What do you mean I can't return it?", the server's sentiment engine detects that the user is angry and generates a response in a tone such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[0373] 4. Return and display of responses

[0374] The generated response is formatted in JSON and sent back to the terminal as an HTTP response. The terminal receives this response and displays it to the user in an appropriate format. This allows the user to get answers quickly and receive flexible responses that are sensitive to their emotions.

[0375] 5. Providing prompt sentences using a generative AI model

[0376] The system also includes a mechanism to generate and present appropriate prompts to the user using a generative AI model when a question is entered. For example, when a general question is entered, the model provides appropriate completion and suggestions to make the information the user is looking for more specific. An example of a prompt is shown below.

[0377] "Please tell me your return policy."

[0378] "When does the sale start?"

[0379] This allows users to quickly obtain clear and accurate information.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The user enters a question into the device. During input, the device uses a generative AI model to generate appropriate prompts and present them to the user. For example, if the user enters a question like "What is your return policy?", the generative AI model analyzes it and provides completions or suggestions. Input: User's text question. Output: Generated prompts and the user's final question.

[0383] Step 2:

[0384] The terminal receives the user's input question and performs sentiment analysis. The sentiment analysis algorithm analyzes the user's emotional state from the input text. For example, it determines emotions based on exclamation marks and question words. Input: User's final question. Output: Analyzed emotional state (e.g., anger, joy, neutral).

[0385] Step 3:

[0386] The terminal converts the question into JSON format and prepares an HTTP request to send to the server. For example, it will be formatted as {"question": "What is your return policy?"}. Input: User's final question and sentiment state. Output: HTTP request (in JSON format).

[0387] Step 4:

[0388] The server parses the received HTTP request and extracts the content of the question. The server then uses a natural language processing engine (e.g., Python's NLP library) to analyze the question and extract keywords. Input: HTTP request (JSON format). Output: Extracted keywords (e.g., "return policy").

[0389] Step 5:

[0390] The server sends queries to the database based on the extracted keywords and retrieves relevant information. For example, it retrieves information about the "return policy" from the database. Input: Extracted keywords. Output: Information retrieved from the database.

[0391] Step 6:

[0392] The server generates an answer to the user's question based on the information it has gathered. Based on the sentiment analysis results, it adjusts the tone and content of the answer. For example, it might be adjusted to something like, "We apologize for any inconvenience this may have caused. Please contact customer support for details on returns." Input: Information gathered from the database and analyzed sentiment. Output: Adjusted answer.

[0393] Step 7:

[0394] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response. For example, it might be formatted as {"answer": "Unused items can be returned within 30 days of purchase."}. Input: Generated response. Output: HTTP response (JSON format).

[0395] Step 8:

[0396] The terminal analyzes the received response and displays the reply text. The user can see the content, and the system also provides flexible responses tailored to their emotions. Input: HTTP response (JSON format). Output: Reply text displayed to the user.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Second Embodiment]

[0401] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server.

[0414] The program for this system works as follows:

[0415] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0416] The server parses the received request and extracts the user's question from the request body. The server then uses a natural language processing engine to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the CrewNavi database to retrieve relevant information.

[0417] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0418] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question. This system significantly improves the efficiency of information retrieval and enhances user convenience.

[0419] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, an answer such as "Our business hours are from 9:00 AM to 6:00 PM on weekdays" is generated and displayed to the user. In this way, the user can easily obtain the information they are looking for.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The user launches a dedicated app and enters the question in text format.

[0423] Example: Enter "Please tell me your product return policy."

[0424] Step 2:

[0425] The terminal receives user input and converts its contents into JSON format.

[0426] Example: {"question": "Please tell me your product return policy."}

[0427] Step 3:

[0428] The terminal prepares an HTTP request to the server's API endpoint.

[0429] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0430] Step 4:

[0431] The terminal sends an HTTP request to the server, which includes user input.

[0432] Example: Send it to the server as a POST request.

[0433] Step 5:

[0434] The server receives the HTTP request and extracts the user's question from the request body.

[0435] Example: Extract {"question": "Please tell me your product return policy"}.

[0436] Step 6:

[0437] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0438] Example: Extract using "return policy" as a keyword.

[0439] Step 7:

[0440] The server uses the extracted keywords to send a search query to the CrewNavi database.

[0441] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0442] Step 8:

[0443] The server retrieves the appropriate information from the database.

[0444] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0445] Step 9:

[0446] The server generates appropriate answers to the user's questions from the acquired information and formats them into JSON format.

[0447] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0448] Step 10:

[0449] The server sends the generated response back to the terminal as an HTTP response.

[0450] Example: The generated response is included in the response body and sent back with an HTTP status code of 200 (success).

[0451] Step 11:

[0452] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0453] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}} and display the result.

[0454] Step 12:

[0455] The user checks the answer displayed on the device and obtains the answer to the question.

[0456] Example: Confirm the response, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0459] Modern users demand information quickly and accurately, but traditional information retrieval systems often take a long time to provide appropriate answers to user questions. Furthermore, there are concerns that search efficiency will decrease as the amount of data handled by the system increases. Additionally, systems capable of accurately analyzing and understanding the meaning of questions entered in natural language are limited, making it difficult to reliably obtain the information users seek.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for receiving user input, analyzing the user's question, and retrieving relevant information from a storage device; means for generating an appropriate answer from the search results and sending it back to the user terminal; and means for the user terminal to display the answer from the data processing device. This makes it possible to extract keywords using natural language processing based on user input and quickly provide an appropriate answer.

[0462] A "user terminal" is a device used by a user to input questions and display answers from the server.

[0463] A "data processing device" is a device that analyzes a user's question, retrieves relevant information from storage devices, and generates an appropriate answer.

[0464] "Natural language processing" is a technology that analyzes natural language text entered by a user and understands its meaning.

[0465] A "storage device" is a device used to store data that a data processing device will use to retrieve information.

[0466] "Keyword extraction" is the process of extracting important words or phrases from the entered questions.

[0467] A "standard data format" is a format commonly used for exchanging and storing data (e.g., JSON format).

[0468] "Search results" refer to relevant information related to the user's question, retrieved from the storage device.

[0469] "Answer generation" is the process of creating appropriate answers to a user's questions based on the information that has been searched.

[0470] The system according to the present invention comprises a user terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a data processing device. This system includes a data processing device that analyzes the user's question and retrieves related information from a storage device, and means for generating an appropriate answer from the search results and returning it to the user terminal. The user terminal also has means for displaying the answer from the data processing device.

[0471] The user launches a dedicated application and enters a question in text format. For example, the user might enter the question, "What is your product return policy?" The user's device receives this input, converts it to an appropriate format (e.g., JSON) for transmission to the data processing device, and prepares an HTTP request. The prepared request is then sent from the user's device to the data processing device.

[0472] The data processing unit parses the received request and extracts the user's question from the request body. The data processing unit then uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the data processing unit sends a query to the storage device to retrieve the relevant information.

[0473] Based on the acquired information, the data processing unit generates an appropriate answer to the user's question. In this case, the answer generated is, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The data processing unit formats the generated answer in JSON format and sends it back to the user's terminal as an HTTP response.

[0474] The user's device analyzes the received response, extracts the answer text, and displays it to the user. This allows the user to easily obtain the appropriate answer to the question.

[0475] To further illustrate the operation of this system, let's look at an example of how the generating AI model and prompt statements are used. For example, if a user enters the question "What are your business hours?", the prompt statement used will be "Please provide information regarding your business hours." The user's terminal sends this input to the data processing unit, which searches its storage using the keyword "business hours." Based on the retrieved information, the answer "Our business hours are from 9:00 to 18:00 on weekdays" is generated and displayed to the user.

[0476] This invention enables users to easily obtain appropriate answers to their questions, significantly improving the efficiency of information retrieval.

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

[0478] Step 1:

[0479] The user launches a dedicated app and enters the question in text format.

[0480] Input: The user types "What is your product return policy?".

[0481] Output: Question data in text format is generated.

[0482] Specific action: The user enters text into the app's input field and presses the submit button.

[0483] Step 2:

[0484] The terminal receives user input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares the HTTP request.

[0485] Input: Question data in text format.

[0486] Output: Question data and HTTP request converted to JSON format.

[0487] Specific operation: The terminal converts the text data into { "question": "Please tell me the product return policy"} and generates an HTTP request with the header information set.

[0488] Step 3:

[0489] The terminal sends the prepared HTTP request to the server.

[0490] Input: HTTP request containing question data in JSON format.

[0491] Output: HTTP request sent to the server.

[0492] Specific action: The device sends an HTTP POST request to the endpoint (e.g., https: / / api.example.com / questions).

[0493] Step 4:

[0494] The server parses the received HTTP request and extracts the user's question from the request body.

[0495] Input: The HTTP request sent to the server.

[0496] Output: Extracted question text.

[0497] Specific operation: The server uses a JSON parser to analyze the request body and obtain the question text ("What is your product return policy?").

[0498] Step 5:

[0499] The server uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract important keywords.

[0500] Input: Extracted question text.

[0501] Output: Extracted keywords.

[0502] Specific operation: The server sends text to the NLP engine, and the NLP engine extracts the keyword "return policy".

[0503] Step 6:

[0504] The server sends a query to the storage device based on the extracted keywords and retrieves the relevant information.

[0505] Input: Extracted keywords.

[0506] Output: Related information.

[0507] Specific operation: The server executes an SQL query (e.g., SELECT FROM policies WHERE keyword = 'return policy') to retrieve the corresponding entry from storage.

[0508] Step 7:

[0509] The server generates appropriate answers to user questions based on the information it has obtained.

[0510] Input: Related information.

[0511] Output: The generated answer text.

[0512] Specific operation: The server analyzes the acquired information and generates a response stating, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0513] Step 8:

[0514] The server formats the generated response into JSON format and sends it back to the user's device as an HTTP response.

[0515] Input: Generated response text.

[0516] Output: HTTP response containing answer data in JSON format.

[0517] Specific operation: The server converts the response text to { "answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."} and sends it back with HTTP status 200 (OK).

[0518] Step 9:

[0519] The system analyzes the HTTP response received by the user's device, extracts the response text, and displays it to the user.

[0520] Input: HTTP response containing response data in JSON format.

[0521] Output: Extracted response text.

[0522] Specific operation: The user's device parses the JSON data from the response body, retrieves the content of the "answer" field, and displays it in the app's UI.

[0523] (Application Example 1)

[0524] 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."

[0525] The objective of this invention is to efficiently provide staff with inventory status and work instructions at a logistics center, thereby improving operational efficiency and speeding up information retrieval. Conventional systems can cause delays in work due to the time required to search for information and obtain appropriate answers. To solve this problem, it is necessary to construct a highly efficient information provision system using natural language processing.

[0526] 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.

[0527] In this invention, the server includes means for analyzing a user's question and retrieving relevant information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, and means for the terminal to display the answer from the server. This makes it possible to install the system on equipment in a logistics center and quickly provide inventory status and work instructions.

[0528] A "user input device" is an electronic device used by users to input questions in text or voice format.

[0529] "Means for receiving user input and sending it to the server" refers to the system component that has the function of converting user questions entered on a terminal into an appropriate format and sending them to the server via a communication network.

[0530] The "server component" refers to the system part that analyzes the user's question received, searches for relevant information in the database, and generates an answer based on the retrieved information.

[0531] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to the system component that has the function of creating an answer based on the search results on the server and sending that answer back to the terminal.

[0532] "Means by which a terminal displays a response from a server" refers to a terminal function that displays the response sent back from the server to the user visually or audibly.

[0533] "A means of quickly providing inventory status and work instructions by being installed on equipment within the logistics center" refers to the system component that is installed on equipment used in the logistics center and provides appropriate information such as inventory status and work instructions quickly by allowing staff to input questions.

[0534] The system according to the present invention is configured to efficiently provide inventory status and work instructions within a logistics center. This system includes a terminal for users to input questions and means for receiving user input and transmitting it to a server. The server also has means for analyzing the user's questions, searching for relevant information in a database, generating an appropriate answer from the search results, and then returning it to the terminal. In addition, the terminal has means for displaying the answer from the server.

[0535] System Overview

[0536] 1. User terminal

[0537] The user terminal has the ability to accept questions in text format or voice input. This terminal is equipped with the ability to send user input to a server, specifically by transmitting data via the network.

[0538] 2. Server

[0539] The server analyzes the received question using a natural language processing engine. This engine uses a model such as BERT to extract important keywords and sends queries to a database. Based on the information obtained, the server generates an appropriate answer to the user's question, formats it in JSON format, and sends it back to the terminal.

[0540] 3. Database

[0541] The database to which the server sends search queries holds detailed data on inventory information and work orders. This database is frequently updated to ensure that it provides the most up-to-date information.

[0542] Specific example of processing

[0543] Suppose a user enters the question, "What is the stock status?" The device receives this input, converts it to JSON format, and sends it to the server as an HTTP request. The server parses this request and extracts the keyword "stock status." It then sends a query to the database to retrieve the relevant stock information. Based on the retrieved information, the server generates the answer, "There are currently 100 items in stock," and sends it back to the device in JSON format. The device parses this answer and displays it to the user.

[0544] Hardware and software to be used

[0545] User device: Smartphone, tablet, or PC

[0546] Server: A cloud server with high-performance computing resources (e.g., AWS or Google Cloud)

[0547] Natural language processing engines: BERT, GPT, etc.

[0548] Databases: Relational database management systems such as MySQL and PostgreSQL

[0549] Example of a prompt

[0550] As an example of a prompt, consider the text-based question, "What is the stock status?" In response to this question, the system performs the processing described above and provides a quick and appropriate answer.

[0551] As described above, the system of the present invention realizes increased efficiency and speed in providing information at logistics centers.

[0552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0553] Step 1:

[0554] The user enters a question.

[0555] The user launches a dedicated app on their device and enters their question in text or voice format. For example, they might enter the question, "What is the stock status?" This input is processed as a prompt. The input data can be text or voice data.

[0556] Step 2:

[0557] The terminal sends user input to the server.

[0558] The terminal receives user input and converts it into an appropriate format (e.g., JSON). The converted data is prepared as an HTTP request and sent to the server. Specifically, the data is sent using the terminal's network capabilities. The input data is text data, and the output data is formatted JSON data.

[0559] Step 3:

[0560] The server analyzes the user's question.

[0561] The server parses the received request and extracts the user's question from the request body. Then, it uses a natural language processing engine (e.g., BERT or GPT) to analyze the question and extract important keywords. For example, the keyword "stock status" might be extracted. The input data in this step is JSON data, and the output data is the analyzed keywords.

[0562] Step 4:

[0563] The server searches the database.

[0564] The server uses the extracted keywords to send queries to the database and retrieve relevant information. For example, it searches the database for information related to "inventory status." At this time, the database query is executed, and inventory information is retrieved as a result. The input data is keywords, and the output data is the search results.

[0565] Step 5:

[0566] The server generates the appropriate answer.

[0567] The server generates an appropriate answer to the user's question based on the information it has obtained. For example, it might generate the answer, "We currently have 100 items in stock." The generated answer is formatted in JSON format. The input data is the search results, and the output data is the formatted answer in JSON format.

[0568] Step 6:

[0569] The server sends the response back to the terminal.

[0570] The server sends the generated response back to the terminal as an HTTP response. The terminal receives this response and performs analysis. The input data is the response JSON, and the output data is the HTTP response.

[0571] Step 7:

[0572] The device will display the answer.

[0573] The device analyzes the received response, extracts the response text, and displays it to the user. For example, the text "We currently have 100 items in stock" might be displayed on the device's screen. The input data is the HTTP response, and the output data is the displayed text.

[0574] 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.

[0575] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server. Furthermore, the system is equipped with an emotion engine that recognizes emotions from the user's input and means for adjusting the answer based on the user's emotions.

[0576] The program for this system works as follows:

[0577] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0578] The server parses the received request and extracts the user's question from the request body. Then, the server uses a natural language processing engine to analyze the question and extract important keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the database to retrieve relevant information.

[0579] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0580] Furthermore, the server uses an emotion engine to recognize emotions from user input. The emotion engine analyzes the question entered by the user and detects the user's emotional state. For example, if the user enters the question, "What do you mean I can't return it?", the emotion engine will detect that the user is angry. Based on this emotional state, the server adjusts the tone and content of the response. In this case, a response such as, "We apologize for the inconvenience. Please contact customer support for details on returns," would be generated.

[0581] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question, and the emotion engine enables even more appropriate responses. This system not only significantly improves the efficiency of information retrieval but also contributes to increased customer satisfaction by enabling flexible responses that respond to the user's emotions.

[0582] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, the server generates the answer, "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for using our service!" to the answer. This can increase the user's satisfaction with the service.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The user launches a dedicated app and enters the question in text format.

[0586] Example: Enter "Please tell me your product return policy."

[0587] Step 2:

[0588] The terminal receives user input and converts its contents into JSON format.

[0589] Example: {"question": "Please tell me your product return policy."}

[0590] Step 3:

[0591] The terminal prepares an HTTP request to the server's API endpoint.

[0592] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0593] Step 4:

[0594] The terminal sends an HTTP request to the server, which includes user input.

[0595] Example: Send it to the server as a POST request.

[0596] Step 5:

[0597] The server receives the HTTP request and extracts the user's question from the request body.

[0598] Example: Extract {"question": "Please tell me your product return policy"}.

[0599] Step 6:

[0600] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0601] Example: Extract using "return policy" as a keyword.

[0602] Step 7:

[0603] The server uses an emotion engine to analyze the user's question and detect the user's emotional state.

[0604] Example: Detect the user's emotion of "confusion" from the question.

[0605] Step 8:

[0606] The server uses the extracted keywords to send a search query to the information database.

[0607] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0608] Step 9:

[0609] The server retrieves the appropriate information from the database.

[0610] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0611] Step 10:

[0612] The server uses an emotion engine to detect the user's emotional state and adjusts the tone and content of its response accordingly.

[0613] Example: To a user who appears confused, add a polite tone such as, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0614] Step 11:

[0615] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response.

[0616] For example, send a response in the format of: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0617] Step 12:

[0618] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0619] Example: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."} is received and displayed to the user.

[0620] Step 13:

[0621] The user checks the answer displayed on the device and obtains the answer to the question.

[0622] Example: Check the response, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0623] (Example 2)

[0624] 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".

[0625] Conventional information retrieval systems have a problem not only in generating appropriate answers to user questions, but also in responding based on user emotions. Therefore, in order to improve user satisfaction, it is necessary to recognize user emotions and provide flexible answers that are tailored to them.

[0626] 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 analyzing the user's question and searching for relevant information from a database, means for generating an appropriate answer from the search results, and means for recognizing the user's emotions from the user's input and adjusting the answer. As a result, the user not only receives an appropriate answer to their question but also receives a flexible response that is in line with their emotions.

[0627] A "user" refers to a person who uses the system to input questions and obtain answers.

[0628] A "terminal" refers to a device used by a user to input questions and communicate with a server. Examples include smartphones and personal computers.

[0629] A "server" refers to a device or system that analyzes user input, searches for information in a database, generates an appropriate response, and sends it back to the terminal.

[0630] A "database" refers to a digital storage location where information is accumulated, and is the target of searches performed by a server using queries.

[0631] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0632] An "emotion engine" refers to software or algorithms that recognize emotions from user input and enable responses that correspond to those emotions.

[0633] An "HTTP request" is a part of the communication protocol used by a terminal to request data from a server, and specifically refers to the method used in web-based communication.

[0634] JSON format is a format for representing data in text format and giving it structure, and is mainly used for sending and receiving data.

[0635] "Keywords" refer to vocabulary extracted from user questions that are considered important for database searches and answer generation.

[0636] "Emotion recognition" refers to technology that identifies emotions from user input and determines the emotional state.

[0637] This system includes a terminal where the user enters a question, and a means for receiving the user input and sending it to a server. The server has means for analyzing the user's question and searching for relevant information in a database. Furthermore, it has means for generating an appropriate answer from the search results and sending that answer back to the terminal. The terminal also has means for displaying the answer from the server, providing the user with appropriate information.

[0638] First, the user launches a dedicated application and enters the question in text format. This question is then captured by the device as text.

[0639] The terminal converts the question received from the user into an HTTP request and sends it to the server. Specifically, the terminal parses the question into JSON format and prepares the HTTP request as follows:

[0640] json

[0641] {

[0642] "Question": "Please tell me your product return policy."

[0643] }

[0644] This JSON data is then sent to the server's endpoint.

[0645] The server receives this request and extracts the question content from the request body. This question content is then analyzed using a natural language processing engine (such as SpaCy or BERT) to extract important keywords. For example, if the user asks "What is the product return policy?", the keyword "return policy" will be extracted.

[0646] The server uses these extracted keywords to send queries to a database (such as PostgreSQL or MySQL) and retrieve the relevant information. For example, the database might contain information such as, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0647] Based on the information obtained, the server generates an appropriate response for the user. This response is then formatted again into JSON and sent back to the terminal as an HTTP response.

[0648] Furthermore, the server uses an emotion engine (such as IBM Watson Tone Analyzer) to analyze the user's emotional state and provide a more appropriate response. For example, if a user asks, "What do you mean I can't return this?", the server uses the emotion engine to detect the user's anger. Based on this emotional state, the server adjusts the content and tone of its response, generating a reply such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[0649] The terminal analyzes the response received from the server and displays the answer text to the user. This entire process allows the user not only to easily obtain answers to their questions but also to receive flexible responses that are tailored to their emotions.

[0650] For example, if a user enters the question "What are your business hours?", this question is sent from the terminal to the server, which then searches its database using the keyword "business hours". Based on the retrieved information, the server generates the answer "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for your continued patronage!" to the answer.

[0651] Example of a prompt:

[0652] "When a user types 'What is your product return policy?', generate an HTTP request that converts this question into JSON format and sends it to the server. Also, describe the process by which the server generates an appropriate answer to this question."

[0653] This system provides detailed processing procedures and specific examples necessary to concretely implement the technical scope of the invention, and serves as a standard for others to accurately understand and practice this invention.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Processing steps:

[0656] Step 1:

[0657] The user enters a question.

[0658] Specific actions:

[0659] The user launches the dedicated app and enters the question into the text box.

[0660] Example: Enter "Please tell me your product return policy."

[0661] Input: User's question text.

[0662] Output: The entered question text.

[0663] Step 2:

[0664] The terminal sends the entered question to the server.

[0665] Specific actions:

[0666] The terminal receives the entered question and converts it to JSON format.

[0667] Example of converted JSON:

[0668] json

[0669] {

[0670] "Question": "Please tell me your product return policy."

[0671] }

[0672] The device sets this JSON in the body of the HTTP request and sends it to the server's endpoint.

[0673] Input: The entered question text.

[0674] Output: The HTTP request sent to the server.

[0675] Step 3:

[0676] The server analyzes the question.

[0677] Specific actions:

[0678] The server parses the received HTTP request and extracts the question from the request body.

[0679] The server uses a natural language processing engine (e.g., SpaCy or BERT) to analyze the question and extract important keywords.

[0680] Example: The keyword "return policy" is extracted.

[0681] Input: HTTP request (including the question in JSON format).

[0682] Output: Extracted keywords (e.g., "return policy").

[0683] Step 4:

[0684] The server searches the database.

[0685] Specific actions:

[0686] The server sends a query to the database based on the extracted keywords.

[0687] example:

[0688] sql

[0689] SELECT policy FROM return_policies WHERE keyword = 'return_policies';

[0690] Retrieve relevant information from the database.

[0691] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0692] Input: Extracted keywords (e.g., "return policy").

[0693] Output: Information retrieved from the database.

[0694] Step 5:

[0695] The server generates the answer.

[0696] Specific actions:

[0697] The server generates appropriate answers to the user's questions based on the information it has obtained.

[0698] The generated response is formatted into JSON and sent back to the terminal as an HTTP response.

[0699] example:

[0700] json

[0701] {

[0702] "Response": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0703] }

[0704] Input: Information retrieved from the database.

[0705] Output: Generated response (in JSON format).

[0706] Step 6:

[0707] The server uses an emotion engine to recognize emotions.

[0708] Specific actions:

[0709] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's question and recognize its sentiment.

[0710] Example: The system detects "anger" in response to the question, "What do you mean I can't return it?"

[0711] Input: User's question text.

[0712] Output: Detected emotional state (e.g., "anger").

[0713] Step 7:

[0714] The server adjusts its response based on emotions.

[0715] Specific actions:

[0716] The server adjusts the tone and content of the response based on the results of the emotion engine.

[0717] Example: Response when "anger" is detected:

[0718] "We apologize for any inconvenience this may have caused. Please contact customer support for details regarding returns."

[0719] Input: Detected emotional state.

[0720] Output: Adjusted response text.

[0721] Step 8:

[0722] The device will display the answer.

[0723] Specific actions:

[0724] The terminal analyzes the response received from the server and extracts the answer text.

[0725] The extracted response text is displayed to the user.

[0726] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0727] Input: Response received from the server (in JSON format).

[0728] Output: The response text displayed to the user.

[0729] The above outlines the specific processing steps of this system. This allows users not only to receive appropriate answers to their questions but also to receive flexible responses that take their emotions into account.

[0730] (Application Example 2)

[0731] 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."

[0732] Traditional information retrieval systems have the drawback of only being able to provide a uniform answer to user-inputted questions, making it difficult to respond appropriately to users' emotions and circumstances. Furthermore, depending on the content of the question, it may not be possible to clearly define what the user wants to know, often resulting in a long wait to obtain an accurate answer.

[0733] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input and transmitting it to the server, means for analyzing the user's question and searching for relevant information from a database, means for recognizing emotions from the user's input and adjusting the tone and content of the response based on the recognized emotions, and means for generating and providing appropriate prompt sentences using a generative AI model. This makes it possible to quickly provide appropriate answers that are in line with the user's emotions and situation.

[0734] "The device on which the user enters the question" refers to a device used by the user to enter and submit a question in text format, and includes smartphones, tablets, or computers.

[0735] "Means for receiving user input and sending it to the server" refers to a software algorithm or protocol for transferring user-entered questions to a server.

[0736] A "server that analyzes user questions and retrieves relevant information from a database" refers to a server that uses natural language processing technology to analyze user-inputted questions and retrieves relevant information from a database.

[0737] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to a server-based processing function that generates an appropriate answer based on information obtained from a database and sends it back to the terminal from which the user entered the question.

[0738] "A means for a terminal to display a response from a server" refers to software that has the function of displaying the response received from the server on the screen of the terminal on which the user entered the question.

[0739] "A means of recognizing emotions from user input and adjusting responses" refers to an emotion analysis algorithm that analyzes the emotions from the questions entered by the user and adjusts the tone and content of the response based on the detected emotions.

[0740] "A means of analyzing user questions and extracting keywords using natural language processing" refers to an algorithm that uses natural language processing techniques to analyze user-entered questions and identify important words or phrases.

[0741] "A means of formatting the answers generated from search results into JSON format and sending them back to the device" refers to a function that formats the answers generated based on information obtained from the database into JSON format and sends them back to the device on which the user entered the question.

[0742] "Means for generating and providing appropriate prompt sentences using a generative AI model" refers to a function that uses a generative AI model to clarify the content of a question and derive an appropriate prompt sentence when a user inputs a question.

[0743]

[0744] This invention is a system that includes a terminal in which a user inputs a question, means for receiving user input and transmitting it to a server, server means for analyzing the user's question and searching for related information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, means for the terminal to display the answer from the server, means for recognizing emotions from the user's input and adjusting the answer, and means for generating and providing an appropriate prompt sentence using a generative AI model.

[0745] Description of the system's programs and processes:

[0746] 1. User question input and sentiment analysis

[0747] A smartphone, tablet, or computer is used as the terminal. Users input questions in text format using these devices. For example, a question such as "What is your product return policy?" This input is analyzed by an emotion analysis algorithm to determine the user's emotional state. Emotion analysis is performed based on specific keywords and symbols, such as "!" or "why," to detect emotions from the entered text.

[0748] 2. Question Analysis and Information Retrieval

[0749] After receiving user input, the terminal converts this input to JSON format and sends it to the server. The server parses the received input using natural language processing techniques (e.g., Python's natural language processing library) and extracts keywords. For example, the keyword "return policy" might be extracted. Based on this keyword, the server queries the database to retrieve relevant information.

[0750] 3. Generating and adjusting responses

[0751] The server generates an appropriate response based on information retrieved from the database. This response is then adjusted based on the results of sentiment analysis. For example, if a user enters "What do you mean I can't return it?", the server's sentiment engine detects that the user is angry and generates a response in a tone such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[0752] 4. Return and display of responses

[0753] The generated response is formatted in JSON and sent back to the terminal as an HTTP response. The terminal receives this response and displays it to the user in an appropriate format. This allows the user to get answers quickly and receive flexible responses that are sensitive to their emotions.

[0754] 5. Providing prompt sentences using a generative AI model

[0755] The system also includes a mechanism to generate and present appropriate prompts to the user using a generative AI model when a question is entered. For example, when a general question is entered, the model provides appropriate completion and suggestions to make the information the user is looking for more specific. An example of a prompt is shown below.

[0756] "Please tell me your return policy."

[0757] "When does the sale start?"

[0758] This allows users to quickly obtain clear and accurate information.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The user enters a question into the device. During input, the device uses a generative AI model to generate appropriate prompts and present them to the user. For example, if the user enters a question like "What is your return policy?", the generative AI model analyzes it and provides completions or suggestions. Input: User's text question. Output: Generated prompts and the user's final question.

[0762] Step 2:

[0763] The terminal receives the user's input question and performs sentiment analysis. The sentiment analysis algorithm analyzes the user's emotional state from the input text. For example, it determines emotions based on exclamation marks and question words. Input: User's final question. Output: Analyzed emotional state (e.g., anger, joy, neutral).

[0764] Step 3:

[0765] The terminal converts the question into JSON format and prepares an HTTP request to send to the server. For example, it will be formatted as {"question": "What is your return policy?"}. Input: User's final question and sentiment state. Output: HTTP request (in JSON format).

[0766] Step 4:

[0767] The server parses the received HTTP request and extracts the content of the question. The server then uses a natural language processing engine (e.g., Python's NLP library) to analyze the question and extract keywords. Input: HTTP request (JSON format). Output: Extracted keywords (e.g., "return policy").

[0768] Step 5:

[0769] The server sends queries to the database based on the extracted keywords and retrieves relevant information. For example, it retrieves information about the "return policy" from the database. Input: Extracted keywords. Output: Information retrieved from the database.

[0770] Step 6:

[0771] The server generates an answer to the user's question based on the information it has gathered. Based on the sentiment analysis results, it adjusts the tone and content of the answer. For example, it might be adjusted to something like, "We apologize for any inconvenience this may have caused. Please contact customer support for details on returns." Input: Information gathered from the database and analyzed sentiment. Output: Adjusted answer.

[0772] Step 7:

[0773] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response. For example, it might be formatted as {"answer": "Unused items can be returned within 30 days of purchase."}. Input: Generated response. Output: HTTP response (JSON format).

[0774] Step 8:

[0775] The terminal analyzes the received response and displays the reply text. The user can see the content, and the system also provides flexible responses tailored to their emotions. Input: HTTP response (JSON format). Output: Reply text displayed to the user.

[0776] 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.

[0777] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] 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.

[0779] [Third Embodiment]

[0780] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0781] 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.

[0782] 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).

[0783] 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.

[0784] 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.

[0785] 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).

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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".

[0792] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server.

[0793] The program for this system works as follows:

[0794] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0795] The server parses the received request and extracts the user's question from the request body. The server then uses a natural language processing engine to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the CrewNavi database to retrieve relevant information.

[0796] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0797] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question. This system significantly improves the efficiency of information retrieval and enhances user convenience.

[0798] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, an answer such as "Our business hours are from 9:00 AM to 6:00 PM on weekdays" is generated and displayed to the user. In this way, the user can easily obtain the information they are looking for.

[0799] The following describes the processing flow.

[0800] Step 1:

[0801] The user launches a dedicated app and enters the question in text format.

[0802] Example: Enter "Please tell me your product return policy."

[0803] Step 2:

[0804] The terminal receives user input and converts its contents into JSON format.

[0805] Example: {"question": "Please tell me your product return policy."}

[0806] Step 3:

[0807] The terminal prepares an HTTP request to the server's API endpoint.

[0808] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0809] Step 4:

[0810] The terminal sends an HTTP request to the server, which includes user input.

[0811] Example: Send it to the server as a POST request.

[0812] Step 5:

[0813] The server receives the HTTP request and extracts the user's question from the request body.

[0814] Example: Extract {"question": "Please tell me your product return policy"}.

[0815] Step 6:

[0816] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0817] Example: Extract using "return policy" as a keyword.

[0818] Step 7:

[0819] The server uses the extracted keywords to send a search query to the CrewNavi database.

[0820] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0821] Step 8:

[0822] The server retrieves the appropriate information from the database.

[0823] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0824] Step 9:

[0825] The server generates appropriate answers to the user's questions from the acquired information and formats them into JSON format.

[0826] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0827] Step 10:

[0828] The server sends the generated response back to the terminal as an HTTP response.

[0829] Example: The generated response is included in the response body and sent back with an HTTP status code of 200 (success).

[0830] Step 11:

[0831] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0832] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}} and display the result.

[0833] Step 12:

[0834] The user checks the answer displayed on the device and obtains the answer to the question.

[0835] Example: Confirm the response, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0836] (Example 1)

[0837] 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."

[0838] Modern users demand information quickly and accurately, but traditional information retrieval systems often take a long time to provide appropriate answers to user questions. Furthermore, there are concerns that search efficiency will decrease as the amount of data handled by the system increases. Additionally, systems capable of accurately analyzing and understanding the meaning of questions entered in natural language are limited, making it difficult to reliably obtain the information users seek.

[0839] 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.

[0840] In this invention, the server includes means for receiving user input, analyzing the user's question, and retrieving relevant information from a storage device; means for generating an appropriate answer from the search results and sending it back to the user terminal; and means for the user terminal to display the answer from the data processing device. This makes it possible to extract keywords using natural language processing based on user input and quickly provide an appropriate answer.

[0841] A "user terminal" is a device used by a user to input questions and display answers from the server.

[0842] A "data processing device" is a device that analyzes a user's question, retrieves relevant information from storage devices, and generates an appropriate answer.

[0843] "Natural language processing" is a technology that analyzes natural language text entered by a user and understands its meaning.

[0844] A "storage device" is a device used to store data that a data processing device will use to retrieve information.

[0845] "Keyword extraction" is the process of extracting important words or phrases from the entered questions.

[0846] A "standard data format" is a format commonly used for exchanging and storing data (e.g., JSON format).

[0847] "Search results" refer to relevant information related to the user's question, retrieved from the storage device.

[0848] "Answer generation" is the process of creating appropriate answers to a user's questions based on the information that has been searched.

[0849] The system according to the present invention comprises a user terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a data processing device. This system includes a data processing device that analyzes the user's question and retrieves related information from a storage device, and means for generating an appropriate answer from the search results and returning it to the user terminal. The user terminal also has means for displaying the answer from the data processing device.

[0850] The user launches a dedicated application and enters a question in text format. For example, the user might enter the question, "What is your product return policy?" The user's device receives this input, converts it to an appropriate format (e.g., JSON) for transmission to the data processing device, and prepares an HTTP request. The prepared request is then sent from the user's device to the data processing device.

[0851] The data processing unit parses the received request and extracts the user's question from the request body. The data processing unit then uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the data processing unit sends a query to the storage device to retrieve the relevant information.

[0852] Based on the acquired information, the data processing unit generates an appropriate answer to the user's question. In this case, the answer generated is, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The data processing unit formats the generated answer in JSON format and sends it back to the user's terminal as an HTTP response.

[0853] The user's device analyzes the received response, extracts the answer text, and displays it to the user. This allows the user to easily obtain the appropriate answer to the question.

[0854] To further illustrate the operation of this system, let's look at an example of how the generating AI model and prompt statements are used. For example, if a user enters the question "What are your business hours?", the prompt statement used will be "Please provide information regarding your business hours." The user's terminal sends this input to the data processing unit, which searches its storage using the keyword "business hours." Based on the retrieved information, the answer "Our business hours are from 9:00 to 18:00 on weekdays" is generated and displayed to the user.

[0855] This invention enables users to easily obtain appropriate answers to their questions, significantly improving the efficiency of information retrieval.

[0856] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0857] Step 1:

[0858] The user launches a dedicated app and enters the question in text format.

[0859] Input: The user types "What is your product return policy?".

[0860] Output: Question data in text format is generated.

[0861] Specific action: The user enters text into the app's input field and presses the submit button.

[0862] Step 2:

[0863] The terminal receives user input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares the HTTP request.

[0864] Input: Question data in text format.

[0865] Output: Question data and HTTP request converted to JSON format.

[0866] Specific operation: The terminal converts the text data into { "question": "Please tell me the product return policy"} and generates an HTTP request with the header information set.

[0867] Step 3:

[0868] The terminal sends the prepared HTTP request to the server.

[0869] Input: HTTP request containing question data in JSON format.

[0870] Output: HTTP request sent to the server.

[0871] Specific action: The device sends an HTTP POST request to the endpoint (e.g., https: / / api.example.com / questions).

[0872] Step 4:

[0873] The server parses the received HTTP request and extracts the user's question from the request body.

[0874] Input: The HTTP request sent to the server.

[0875] Output: Extracted question text.

[0876] Specific operation: The server uses a JSON parser to analyze the request body and obtain the question text ("What is your product return policy?").

[0877] Step 5:

[0878] The server uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract important keywords.

[0879] Input: Extracted question text.

[0880] Output: Extracted keywords.

[0881] Specific operation: The server sends text to the NLP engine, and the NLP engine extracts the keyword "return policy".

[0882] Step 6:

[0883] The server sends a query to the storage device based on the extracted keywords and retrieves the relevant information.

[0884] Input: Extracted keywords.

[0885] Output: Related information.

[0886] Specific operation: The server executes an SQL query (e.g., SELECT FROM policies WHERE keyword = 'return policy') to retrieve the corresponding entry from storage.

[0887] Step 7:

[0888] The server generates appropriate answers to user questions based on the information it has obtained.

[0889] Input: Related information.

[0890] Output: The generated answer text.

[0891] Specific operation: The server analyzes the acquired information and generates a response stating, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0892] Step 8:

[0893] The server formats the generated response into JSON format and sends it back to the user's device as an HTTP response.

[0894] Input: Generated response text.

[0895] Output: HTTP response containing answer data in JSON format.

[0896] Specific operation: The server converts the response text to { "answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."} and sends it back with HTTP status 200 (OK).

[0897] Step 9:

[0898] The system analyzes the HTTP response received by the user's device, extracts the response text, and displays it to the user.

[0899] Input: HTTP response containing response data in JSON format.

[0900] Output: Extracted response text.

[0901] Specific operation: The user's device parses the JSON data from the response body, retrieves the content of the "answer" field, and displays it in the app's UI.

[0902] (Application Example 1)

[0903] 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."

[0904] The objective of this invention is to efficiently provide staff with inventory status and work instructions at a logistics center, thereby improving operational efficiency and speeding up information retrieval. Conventional systems can cause delays in work due to the time required to search for information and obtain appropriate answers. To solve this problem, it is necessary to construct a highly efficient information provision system using natural language processing.

[0905] 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.

[0906] In this invention, the server includes means for analyzing a user's question and retrieving relevant information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, and means for the terminal to display the answer from the server. This makes it possible to install the system on equipment in a logistics center and quickly provide inventory status and work instructions.

[0907] A "user input device" is an electronic device used by users to input questions in text or voice format.

[0908] "Means for receiving user input and sending it to the server" refers to the system component that has the function of converting user questions entered on a terminal into an appropriate format and sending them to the server via a communication network.

[0909] The "server component" refers to the system part that analyzes the user's question received, searches for relevant information in the database, and generates an answer based on the retrieved information.

[0910] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to the system component that has the function of creating an answer based on the search results on the server and sending that answer back to the terminal.

[0911] "Means by which a terminal displays a response from a server" refers to a terminal function that displays the response sent back from the server to the user visually or audibly.

[0912] "A means of quickly providing inventory status and work instructions by being installed on equipment within the logistics center" refers to the system component that is installed on equipment used in the logistics center and provides appropriate information such as inventory status and work instructions quickly by allowing staff to input questions.

[0913] The system according to the present invention is configured to efficiently provide inventory status and work instructions within a logistics center. This system includes a terminal for users to input questions and means for receiving user input and transmitting it to a server. The server also has means for analyzing the user's questions, searching for relevant information in a database, generating an appropriate answer from the search results, and then returning it to the terminal. In addition, the terminal has means for displaying the answer from the server.

[0914] System Overview

[0915] 1. User terminal

[0916] The user terminal has the ability to accept questions in text format or voice input. This terminal is equipped with the ability to send user input to a server, specifically by transmitting data via the network.

[0917] 2. Server

[0918] The server analyzes the received question using a natural language processing engine. This engine uses a model such as BERT to extract important keywords and sends queries to a database. Based on the information obtained, the server generates an appropriate answer to the user's question, formats it in JSON format, and sends it back to the terminal.

[0919] 3. Database

[0920] The database to which the server sends search queries holds detailed data on inventory information and work orders. This database is frequently updated to ensure that it provides the most up-to-date information.

[0921] Specific example of processing

[0922] Suppose a user enters the question, "What is the stock status?" The device receives this input, converts it to JSON format, and sends it to the server as an HTTP request. The server parses this request and extracts the keyword "stock status." It then sends a query to the database to retrieve the relevant stock information. Based on the retrieved information, the server generates the answer, "There are currently 100 items in stock," and sends it back to the device in JSON format. The device parses this answer and displays it to the user.

[0923] Hardware and software to be used

[0924] User device: Smartphone, tablet, or PC

[0925] Server: A cloud server with high-performance computing resources (e.g., AWS or Google Cloud)

[0926] Natural language processing engines: BERT, GPT, etc.

[0927] Databases: Relational database management systems such as MySQL and PostgreSQL

[0928] Example of a prompt

[0929] As an example of a prompt, consider the text-based question, "What is the stock status?" In response to this question, the system performs the processing described above and provides a quick and appropriate answer.

[0930] As described above, the system of the present invention realizes increased efficiency and speed in providing information at logistics centers.

[0931] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0932] Step 1:

[0933] The user enters a question.

[0934] The user launches a dedicated app on their device and enters their question in text or voice format. For example, they might enter the question, "What is the stock status?" This input is processed as a prompt. The input data can be text or voice data.

[0935] Step 2:

[0936] The terminal sends user input to the server.

[0937] The terminal receives user input and converts it into an appropriate format (e.g., JSON). The converted data is prepared as an HTTP request and sent to the server. Specifically, the data is sent using the terminal's network capabilities. The input data is text data, and the output data is formatted JSON data.

[0938] Step 3:

[0939] The server analyzes the user's question.

[0940] The server parses the received request and extracts the user's question from the request body. Then, it uses a natural language processing engine (e.g., BERT or GPT) to analyze the question and extract important keywords. For example, the keyword "stock status" might be extracted. The input data in this step is JSON data, and the output data is the analyzed keywords.

[0941] Step 4:

[0942] The server searches the database.

[0943] The server uses the extracted keywords to send queries to the database and retrieve relevant information. For example, it searches the database for information related to "inventory status." At this time, the database query is executed, and inventory information is retrieved as a result. The input data is keywords, and the output data is the search results.

[0944] Step 5:

[0945] The server generates the appropriate answer.

[0946] The server generates an appropriate answer to the user's question based on the information it has obtained. For example, it might generate the answer, "We currently have 100 items in stock." The generated answer is formatted in JSON format. The input data is the search results, and the output data is the formatted answer in JSON format.

[0947] Step 6:

[0948] The server sends the response back to the terminal.

[0949] The server sends the generated response back to the terminal as an HTTP response. The terminal receives this response and performs analysis. The input data is the response JSON, and the output data is the HTTP response.

[0950] Step 7:

[0951] The device will display the answer.

[0952] The device analyzes the received response, extracts the response text, and displays it to the user. For example, the text "We currently have 100 items in stock" might be displayed on the device's screen. The input data is the HTTP response, and the output data is the displayed text.

[0953] 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.

[0954] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server. Furthermore, the system is equipped with an emotion engine that recognizes emotions from the user's input and means for adjusting the answer based on the user's emotions.

[0955] The program for this system works as follows:

[0956] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[0957] The server parses the received request and extracts the user's question from the request body. Then, the server uses a natural language processing engine to analyze the question and extract important keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the database to retrieve relevant information.

[0958] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[0959] Furthermore, the server uses an emotion engine to recognize emotions from user input. The emotion engine analyzes the question entered by the user and detects the user's emotional state. For example, if the user enters the question, "What do you mean I can't return it?", the emotion engine will detect that the user is angry. Based on this emotional state, the server adjusts the tone and content of the response. In this case, a response such as, "We apologize for the inconvenience. Please contact customer support for details on returns," would be generated.

[0960] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question, and the emotion engine enables even more appropriate responses. This system not only significantly improves the efficiency of information retrieval but also contributes to increased customer satisfaction by enabling flexible responses that respond to the user's emotions.

[0961] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, the server generates the answer, "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for using our service!" to the answer. This can increase the user's satisfaction with the service.

[0962] The following describes the processing flow.

[0963] Step 1:

[0964] The user launches a dedicated app and enters the question in text format.

[0965] Example: Enter "Please tell me your product return policy."

[0966] Step 2:

[0967] The terminal receives user input and converts its contents into JSON format.

[0968] Example: {"question": "Please tell me your product return policy."}

[0969] Step 3:

[0970] The terminal prepares an HTTP request to the server's API endpoint.

[0971] The terminal adds authentication information to the request header and includes the user's question in the request body.

[0972] Step 4:

[0973] The terminal sends an HTTP request to the server, which includes user input.

[0974] Example: Send it to the server as a POST request.

[0975] Step 5:

[0976] The server receives the HTTP request and extracts the user's question from the request body.

[0977] Example: Extract {"question": "Please tell me your product return policy"}.

[0978] Step 6:

[0979] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[0980] Example: Extract using "return policy" as a keyword.

[0981] Step 7:

[0982] The server uses an emotion engine to analyze the user's question and detect the user's emotional state.

[0983] Example: Detect the user's emotion of "confusion" from the question.

[0984] Step 8:

[0985] The server uses the extracted keywords to send a search query to the information database.

[0986] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[0987] Step 9:

[0988] The server retrieves the appropriate information from the database.

[0989] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0990] Step 10:

[0991] The server uses an emotion engine to detect the user's emotional state and adjusts the tone and content of its response accordingly.

[0992] Example: To a user who appears confused, add a polite tone such as, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[0993] Step 11:

[0994] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response.

[0995] For example, send a response in the format of: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[0996] Step 12:

[0997] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[0998] Example: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."} is received and displayed to the user.

[0999] Step 13:

[1000] The user checks the answer displayed on the device and obtains the answer to the question.

[1001] Example: Check the response, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1002] (Example 2)

[1003] 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."

[1004] Conventional information retrieval systems have a problem not only in generating appropriate answers to user questions, but also in responding based on user emotions. Therefore, in order to improve user satisfaction, it is necessary to recognize user emotions and provide flexible answers that are tailored to them.

[1005] 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 analyzing the user's question and searching for relevant information from a database, means for generating an appropriate answer from the search results, and means for recognizing the user's emotions from the user's input and adjusting the answer. As a result, the user not only receives an appropriate answer to their question but also receives a flexible response that is in line with their emotions.

[1006] A "user" refers to a person who uses the system to input questions and obtain answers.

[1007] A "terminal" refers to a device used by a user to input questions and communicate with a server. Examples include smartphones and personal computers.

[1008] A "server" refers to a device or system that analyzes user input, searches for information in a database, generates an appropriate response, and sends it back to the terminal.

[1009] A "database" refers to a digital storage location where information is accumulated, and is the target of searches performed by a server using queries.

[1010] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[1011] An "emotion engine" refers to software or algorithms that recognize emotions from user input and enable responses that correspond to those emotions.

[1012] An "HTTP request" is a part of the communication protocol used by a terminal to request data from a server, and specifically refers to the method used in web-based communication.

[1013] JSON format is a format for representing data in text format and giving it structure, and is mainly used for sending and receiving data.

[1014] "Keywords" refer to vocabulary extracted from user questions that are considered important for database searches and answer generation.

[1015] "Emotion recognition" refers to technology that identifies emotions from user input and determines the emotional state.

[1016] This system includes a terminal where the user enters a question, and a means for receiving the user input and sending it to a server. The server has means for analyzing the user's question and searching for relevant information in a database. Furthermore, it has means for generating an appropriate answer from the search results and sending that answer back to the terminal. The terminal also has means for displaying the answer from the server, providing the user with appropriate information.

[1017] First, the user launches a dedicated application and enters the question in text format. This question is then captured by the device as text.

[1018] The terminal converts the question received from the user into an HTTP request and sends it to the server. Specifically, the terminal parses the question into JSON format and prepares the HTTP request as follows:

[1019] json

[1020] {

[1021] "Question": "Please tell me your product return policy."

[1022] }

[1023] This JSON data is then sent to the server's endpoint.

[1024] The server receives this request and extracts the question content from the request body. This question content is then analyzed using a natural language processing engine (such as SpaCy or BERT) to extract important keywords. For example, if the user asks "What is the product return policy?", the keyword "return policy" will be extracted.

[1025] The server uses these extracted keywords to send queries to a database (such as PostgreSQL or MySQL) and retrieve the relevant information. For example, the database might contain information such as, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1026] Based on the information obtained, the server generates an appropriate response for the user. This response is then formatted again into JSON and sent back to the terminal as an HTTP response.

[1027] Furthermore, the server uses an emotion engine (such as IBM Watson Tone Analyzer) to analyze the user's emotional state and provide a more appropriate response. For example, if a user asks, "What do you mean I can't return this?", the server uses the emotion engine to detect the user's anger. Based on this emotional state, the server adjusts the content and tone of its response, generating a reply such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[1028] The terminal analyzes the response received from the server and displays the answer text to the user. This entire process allows the user not only to easily obtain answers to their questions but also to receive flexible responses that are tailored to their emotions.

[1029] For example, if a user enters the question "What are your business hours?", this question is sent from the terminal to the server, which then searches its database using the keyword "business hours". Based on the retrieved information, the server generates the answer "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for your continued patronage!" to the answer.

[1030] Example of a prompt:

[1031] "When a user types 'What is your product return policy?', generate an HTTP request that converts this question into JSON format and sends it to the server. Also, describe the process by which the server generates an appropriate answer to this question."

[1032] This system provides detailed processing procedures and specific examples necessary to concretely implement the technical scope of the invention, and serves as a standard for others to accurately understand and practice this invention.

[1033] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1034] Processing steps:

[1035] Step 1:

[1036] The user enters a question.

[1037] Specific actions:

[1038] The user launches the dedicated app and enters the question into the text box.

[1039] Example: Enter "Please tell me your product return policy."

[1040] Input: User's question text.

[1041] Output: The entered question text.

[1042] Step 2:

[1043] The terminal sends the entered question to the server.

[1044] Specific actions:

[1045] The terminal receives the entered question and converts it to JSON format.

[1046] Example of converted JSON:

[1047] json

[1048] {

[1049] "Question": "Please tell me your product return policy."

[1050] }

[1051] The device sets this JSON in the body of the HTTP request and sends it to the server's endpoint.

[1052] Input: The entered question text.

[1053] Output: The HTTP request sent to the server.

[1054] Step 3:

[1055] The server analyzes the question.

[1056] Specific actions:

[1057] The server parses the received HTTP request and extracts the question from the request body.

[1058] The server uses a natural language processing engine (e.g., SpaCy or BERT) to analyze the question and extract important keywords.

[1059] Example: The keyword "return policy" is extracted.

[1060] Input: HTTP request (including the question in JSON format).

[1061] Output: Extracted keywords (e.g., "return policy").

[1062] Step 4:

[1063] The server searches the database.

[1064] Specific actions:

[1065] The server sends a query to the database based on the extracted keywords.

[1066] example:

[1067] sql

[1068] SELECT policy FROM return_policies WHERE keyword = 'return_policies';

[1069] Retrieve relevant information from the database.

[1070] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1071] Input: Extracted keywords (e.g., "return policy").

[1072] Output: Information retrieved from the database.

[1073] Step 5:

[1074] The server generates the answer.

[1075] Specific actions:

[1076] The server generates appropriate answers to the user's questions based on the information it has obtained.

[1077] The generated response is formatted into JSON and sent back to the terminal as an HTTP response.

[1078] example:

[1079] json

[1080] {

[1081] "Response": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1082] }

[1083] Input: Information retrieved from the database.

[1084] Output: Generated response (in JSON format).

[1085] Step 6:

[1086] The server uses an emotion engine to recognize emotions.

[1087] Specific actions:

[1088] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's question and recognize its sentiment.

[1089] Example: The system detects "anger" in response to the question, "What do you mean I can't return it?"

[1090] Input: User's question text.

[1091] Output: Detected emotional state (e.g., "anger").

[1092] Step 7:

[1093] The server adjusts its response based on emotions.

[1094] Specific actions:

[1095] The server adjusts the tone and content of the response based on the results of the emotion engine.

[1096] Example: Response when "anger" is detected:

[1097] "We apologize for any inconvenience this may have caused. Please contact customer support for details regarding returns."

[1098] Input: Detected emotional state.

[1099] Output: Adjusted response text.

[1100] Step 8:

[1101] The device will display the answer.

[1102] Specific actions:

[1103] The terminal analyzes the response received from the server and extracts the answer text.

[1104] The extracted response text is displayed to the user.

[1105] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1106] Input: Response received from the server (in JSON format).

[1107] Output: The response text displayed to the user.

[1108] The above outlines the specific processing steps of this system. This allows users not only to receive appropriate answers to their questions but also to receive flexible responses that take their emotions into account.

[1109] (Application Example 2)

[1110] 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."

[1111] Traditional information retrieval systems have the drawback of only being able to provide a uniform answer to user-inputted questions, making it difficult to respond appropriately to users' emotions and circumstances. Furthermore, depending on the content of the question, it may not be possible to clearly define what the user wants to know, often resulting in a long wait to obtain an accurate answer.

[1112] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input and transmitting it to the server, means for analyzing the user's question and searching for relevant information from a database, means for recognizing emotions from the user's input and adjusting the tone and content of the response based on the recognized emotions, and means for generating and providing appropriate prompt sentences using a generative AI model. This makes it possible to quickly provide appropriate answers that are in line with the user's emotions and situation.

[1113] "The device on which the user enters the question" refers to a device used by the user to enter and submit a question in text format, and includes smartphones, tablets, or computers.

[1114] "Means for receiving user input and sending it to the server" refers to a software algorithm or protocol for transferring user-entered questions to a server.

[1115] A "server that analyzes user questions and retrieves relevant information from a database" refers to a server that uses natural language processing technology to analyze user-inputted questions and retrieves relevant information from a database.

[1116] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to a server-based processing function that generates an appropriate answer based on information obtained from a database and sends it back to the terminal from which the user entered the question.

[1117] "A means for a terminal to display a response from a server" refers to software that has the function of displaying the response received from the server on the screen of the terminal on which the user entered the question.

[1118] "A means of recognizing emotions from user input and adjusting responses" refers to an emotion analysis algorithm that analyzes the emotions from the questions entered by the user and adjusts the tone and content of the response based on the detected emotions.

[1119] "A means of analyzing user questions and extracting keywords using natural language processing" refers to an algorithm that uses natural language processing techniques to analyze user-entered questions and identify important words or phrases.

[1120] "A means of formatting the answers generated from search results into JSON format and sending them back to the device" refers to a function that formats the answers generated based on information obtained from the database into JSON format and sends them back to the device on which the user entered the question.

[1121] "Means for generating and providing appropriate prompt sentences using a generative AI model" refers to a function that uses a generative AI model to clarify the content of a question and derive an appropriate prompt sentence when a user inputs a question.

[1122]

[1123] This invention is a system that includes a terminal in which a user inputs a question, means for receiving user input and transmitting it to a server, server means for analyzing the user's question and searching for related information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, means for the terminal to display the answer from the server, means for recognizing emotions from the user's input and adjusting the answer, and means for generating and providing an appropriate prompt sentence using a generative AI model.

[1124] Description of the system's programs and processes:

[1125] 1. User question input and sentiment analysis

[1126] A smartphone, tablet, or computer is used as the terminal. Users input questions in text format using these devices. For example, a question such as "What is your product return policy?" This input is analyzed by an emotion analysis algorithm to determine the user's emotional state. Emotion analysis is performed based on specific keywords and symbols, such as "!" or "why," to detect emotions from the entered text.

[1127] 2. Question Analysis and Information Retrieval

[1128] After receiving user input, the terminal converts this input to JSON format and sends it to the server. The server parses the received input using natural language processing techniques (e.g., Python's natural language processing library) and extracts keywords. For example, the keyword "return policy" might be extracted. Based on this keyword, the server queries the database to retrieve relevant information.

[1129] 3. Generating and adjusting responses

[1130] The server generates an appropriate response based on information retrieved from the database. This response is then adjusted based on the results of sentiment analysis. For example, if a user enters "What do you mean I can't return it?", the server's sentiment engine detects that the user is angry and generates a response in a tone such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[1131] 4. Return and display of responses

[1132] The generated response is formatted in JSON and sent back to the terminal as an HTTP response. The terminal receives this response and displays it to the user in an appropriate format. This allows the user to get answers quickly and receive flexible responses that are sensitive to their emotions.

[1133] 5. Providing prompt sentences using a generative AI model

[1134] The system also includes a mechanism to generate and present appropriate prompts to the user using a generative AI model when a question is entered. For example, when a general question is entered, the model provides appropriate completion and suggestions to make the information the user is looking for more specific. An example of a prompt is shown below.

[1135] "Please tell me your return policy."

[1136] "When does the sale start?"

[1137] This allows users to quickly obtain clear and accurate information.

[1138] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1139] Step 1:

[1140] The user enters a question into the device. During input, the device uses a generative AI model to generate appropriate prompts and present them to the user. For example, if the user enters a question like "What is your return policy?", the generative AI model analyzes it and provides completions or suggestions. Input: User's text question. Output: Generated prompts and the user's final question.

[1141] Step 2:

[1142] The terminal receives the user's input question and performs sentiment analysis. The sentiment analysis algorithm analyzes the user's emotional state from the input text. For example, it determines emotions based on exclamation marks and question words. Input: User's final question. Output: Analyzed emotional state (e.g., anger, joy, neutral).

[1143] Step 3:

[1144] The terminal converts the question into JSON format and prepares an HTTP request to send to the server. For example, it will be formatted as {"question": "What is your return policy?"}. Input: User's final question and sentiment state. Output: HTTP request (in JSON format).

[1145] Step 4:

[1146] The server parses the received HTTP request and extracts the content of the question. The server then uses a natural language processing engine (e.g., Python's NLP library) to analyze the question and extract keywords. Input: HTTP request (JSON format). Output: Extracted keywords (e.g., "return policy").

[1147] Step 5:

[1148] The server sends queries to the database based on the extracted keywords and retrieves relevant information. For example, it retrieves information about the "return policy" from the database. Input: Extracted keywords. Output: Information retrieved from the database.

[1149] Step 6:

[1150] The server generates an answer to the user's question based on the information it has gathered. Based on the sentiment analysis results, it adjusts the tone and content of the answer. For example, it might be adjusted to something like, "We apologize for any inconvenience this may have caused. Please contact customer support for details on returns." Input: Information gathered from the database and analyzed sentiment. Output: Adjusted answer.

[1151] Step 7:

[1152] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response. For example, it might be formatted as {"answer": "Unused items can be returned within 30 days of purchase."}. Input: Generated response. Output: HTTP response (JSON format).

[1153] Step 8:

[1154] The terminal analyzes the received response and displays the reply text. The user can see the content, and the system also provides flexible responses tailored to their emotions. Input: HTTP response (JSON format). Output: Reply text displayed to the user.

[1155] 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.

[1156] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1157] 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.

[1158] [Fourth Embodiment]

[1159] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1160] 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.

[1161] 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).

[1162] 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.

[1163] 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.

[1164] 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).

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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".

[1172] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server.

[1173] The program for this system works as follows:

[1174] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[1175] The server parses the received request and extracts the user's question from the request body. The server then uses a natural language processing engine to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the CrewNavi database to retrieve relevant information.

[1176] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[1177] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question. This system significantly improves the efficiency of information retrieval and enhances user convenience.

[1178] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, an answer such as "Our business hours are from 9:00 AM to 6:00 PM on weekdays" is generated and displayed to the user. In this way, the user can easily obtain the information they are looking for.

[1179] The following describes the processing flow.

[1180] Step 1:

[1181] The user launches a dedicated app and enters the question in text format.

[1182] Example: Enter "Please tell me your product return policy."

[1183] Step 2:

[1184] The terminal receives user input and converts its contents into JSON format.

[1185] Example: {"question": "Please tell me your product return policy."}

[1186] Step 3:

[1187] The terminal prepares an HTTP request to the server's API endpoint.

[1188] The terminal adds authentication information to the request header and includes the user's question in the request body.

[1189] Step 4:

[1190] The terminal sends an HTTP request to the server, which includes user input.

[1191] Example: Send it to the server as a POST request.

[1192] Step 5:

[1193] The server receives the HTTP request and extracts the user's question from the request body.

[1194] Example: Extract {"question": "Please tell me your product return policy"}.

[1195] Step 6:

[1196] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[1197] Example: Extract using "return policy" as a keyword.

[1198] Step 7:

[1199] The server uses the extracted keywords to send a search query to the CrewNavi database.

[1200] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[1201] Step 8:

[1202] The server retrieves the appropriate information from the database.

[1203] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1204] Step 9:

[1205] The server generates appropriate answers to the user's questions from the acquired information and formats them into JSON format.

[1206] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[1207] Step 10:

[1208] The server sends the generated response back to the terminal as an HTTP response.

[1209] Example: The generated response is included in the response body and sent back with an HTTP status code of 200 (success).

[1210] Step 11:

[1211] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[1212] Example: {"answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."}} and display the result.

[1213] Step 12:

[1214] The user checks the answer displayed on the device and obtains the answer to the question.

[1215] Example: Confirm the response, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1216] (Example 1)

[1217] 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".

[1218] Modern users demand information quickly and accurately, but traditional information retrieval systems often take a long time to provide appropriate answers to user questions. Furthermore, there are concerns that search efficiency will decrease as the amount of data handled by the system increases. Additionally, systems capable of accurately analyzing and understanding the meaning of questions entered in natural language are limited, making it difficult to reliably obtain the information users seek.

[1219] 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.

[1220] In this invention, the server includes means for receiving user input, analyzing the user's question, and retrieving relevant information from a storage device; means for generating an appropriate answer from the search results and sending it back to the user terminal; and means for the user terminal to display the answer from the data processing device. This makes it possible to extract keywords using natural language processing based on user input and quickly provide an appropriate answer.

[1221] A "user terminal" is a device used by a user to input questions and display answers from the server.

[1222] A "data processing device" is a device that analyzes a user's question, retrieves relevant information from storage devices, and generates an appropriate answer.

[1223] "Natural language processing" is a technology that analyzes natural language text entered by a user and understands its meaning.

[1224] A "storage device" is a device used to store data that a data processing device will use to retrieve information.

[1225] "Keyword extraction" is the process of extracting important words or phrases from the entered questions.

[1226] A "standard data format" is a format commonly used for exchanging and storing data (e.g., JSON format).

[1227] "Search results" refer to relevant information related to the user's question, retrieved from the storage device.

[1228] "Answer generation" is the process of creating appropriate answers to a user's questions based on the information that has been searched.

[1229] The system according to the present invention comprises a user terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a data processing device. This system includes a data processing device that analyzes the user's question and retrieves related information from a storage device, and means for generating an appropriate answer from the search results and returning it to the user terminal. The user terminal also has means for displaying the answer from the data processing device.

[1230] The user launches a dedicated application and enters a question in text format. For example, the user might enter the question, "What is your product return policy?" The user's device receives this input, converts it to an appropriate format (e.g., JSON) for transmission to the data processing device, and prepares an HTTP request. The prepared request is then sent from the user's device to the data processing device.

[1231] The data processing unit parses the received request and extracts the user's question from the request body. The data processing unit then uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract key keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the data processing unit sends a query to the storage device to retrieve the relevant information.

[1232] Based on the acquired information, the data processing unit generates an appropriate answer to the user's question. In this case, the answer generated is, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The data processing unit formats the generated answer in JSON format and sends it back to the user's terminal as an HTTP response.

[1233] The user's device analyzes the received response, extracts the answer text, and displays it to the user. This allows the user to easily obtain the appropriate answer to the question.

[1234] To further illustrate the operation of this system, let's look at an example of how the generating AI model and prompt statements are used. For example, if a user enters the question "What are your business hours?", the prompt statement used will be "Please provide information regarding your business hours." The user's terminal sends this input to the data processing unit, which searches its storage using the keyword "business hours." Based on the retrieved information, the answer "Our business hours are from 9:00 to 18:00 on weekdays" is generated and displayed to the user.

[1235] This invention enables users to easily obtain appropriate answers to their questions, significantly improving the efficiency of information retrieval.

[1236] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1237] Step 1:

[1238] The user launches a dedicated app and enters the question in text format.

[1239] Input: The user types "What is your product return policy?".

[1240] Output: Question data in text format is generated.

[1241] Specific action: The user enters text into the app's input field and presses the submit button.

[1242] Step 2:

[1243] The terminal receives user input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares the HTTP request.

[1244] Input: Question data in text format.

[1245] Output: Question data and HTTP request converted to JSON format.

[1246] Specific operation: The terminal converts the text data into { "question": "Please tell me the product return policy"} and generates an HTTP request with the header information set.

[1247] Step 3:

[1248] The terminal sends the prepared HTTP request to the server.

[1249] Input: HTTP request containing question data in JSON format.

[1250] Output: HTTP request sent to the server.

[1251] Specific action: The device sends an HTTP POST request to the endpoint (e.g., https: / / api.example.com / questions).

[1252] Step 4:

[1253] The server parses the received HTTP request and extracts the user's question from the request body.

[1254] Input: The HTTP request sent to the server.

[1255] Output: Extracted question text.

[1256] Specific operation: The server uses a JSON parser to analyze the request body and obtain the question text ("What is your product return policy?").

[1257] Step 5:

[1258] The server uses a natural language processing engine (e.g., Google NLP API or SpaCy) to analyze the question and extract important keywords.

[1259] Input: Extracted question text.

[1260] Output: Extracted keywords.

[1261] Specific operation: The server sends text to the NLP engine, and the NLP engine extracts the keyword "return policy".

[1262] Step 6:

[1263] The server sends a query to the storage device based on the extracted keywords and retrieves the relevant information.

[1264] Input: Extracted keywords.

[1265] Output: Related information.

[1266] Specific operation: The server executes an SQL query (e.g., SELECT FROM policies WHERE keyword = 'return policy') to retrieve the corresponding entry from storage.

[1267] Step 7:

[1268] The server generates appropriate answers to user questions based on the information it has obtained.

[1269] Input: Related information.

[1270] Output: The generated answer text.

[1271] Specific operation: The server analyzes the acquired information and generates a response stating, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1272] Step 8:

[1273] The server formats the generated response into JSON format and sends it back to the user's device as an HTTP response.

[1274] Input: Generated response text.

[1275] Output: HTTP response containing answer data in JSON format.

[1276] Specific operation: The server converts the response text to { "answer": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."} and sends it back with HTTP status 200 (OK).

[1277] Step 9:

[1278] The system analyzes the HTTP response received by the user's device, extracts the response text, and displays it to the user.

[1279] Input: HTTP response containing response data in JSON format.

[1280] Output: Extracted response text.

[1281] Specific operation: The user's device parses the JSON data from the response body, retrieves the content of the "answer" field, and displays it in the app's UI.

[1282] (Application Example 1)

[1283] 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".

[1284] The objective of this invention is to efficiently provide staff with inventory status and work instructions at a logistics center, thereby improving operational efficiency and speeding up information retrieval. Conventional systems can cause delays in work due to the time required to search for information and obtain appropriate answers. To solve this problem, it is necessary to construct a highly efficient information provision system using natural language processing.

[1285] 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.

[1286] In this invention, the server includes means for analyzing a user's question and retrieving relevant information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, and means for the terminal to display the answer from the server. This makes it possible to install the system on equipment in a logistics center and quickly provide inventory status and work instructions.

[1287] A "user input device" is an electronic device used by users to input questions in text or voice format.

[1288] "Means for receiving user input and sending it to the server" refers to the system component that has the function of converting user questions entered on a terminal into an appropriate format and sending them to the server via a communication network.

[1289] The "server component" refers to the system part that analyzes the user's question received, searches for relevant information in the database, and generates an answer based on the retrieved information.

[1290] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to the system component that has the function of creating an answer based on the search results on the server and sending that answer back to the terminal.

[1291] "Means by which a terminal displays a response from a server" refers to a terminal function that displays the response sent back from the server to the user visually or audibly.

[1292] "A means of quickly providing inventory status and work instructions by being installed on equipment within the logistics center" refers to the system component that is installed on equipment used in the logistics center and provides appropriate information such as inventory status and work instructions quickly by allowing staff to input questions.

[1293] The system according to the present invention is configured to efficiently provide inventory status and work instructions within a logistics center. This system includes a terminal for users to input questions and means for receiving user input and transmitting it to a server. The server also has means for analyzing the user's questions, searching for relevant information in a database, generating an appropriate answer from the search results, and then returning it to the terminal. In addition, the terminal has means for displaying the answer from the server.

[1294] System Overview

[1295] 1. User terminal

[1296] The user terminal has the ability to accept questions in text format or voice input. This terminal is equipped with the ability to send user input to a server, specifically by transmitting data via the network.

[1297] 2. Server

[1298] The server analyzes the received question using a natural language processing engine. This engine uses a model such as BERT to extract important keywords and sends queries to a database. Based on the information obtained, the server generates an appropriate answer to the user's question, formats it in JSON format, and sends it back to the terminal.

[1299] 3. Database

[1300] The database to which the server sends search queries holds detailed data on inventory information and work orders. This database is frequently updated to ensure that it provides the most up-to-date information.

[1301] Specific example of processing

[1302] Suppose a user enters the question, "What is the stock status?" The device receives this input, converts it to JSON format, and sends it to the server as an HTTP request. The server parses this request and extracts the keyword "stock status." It then sends a query to the database to retrieve the relevant stock information. Based on the retrieved information, the server generates the answer, "There are currently 100 items in stock," and sends it back to the device in JSON format. The device parses this answer and displays it to the user.

[1303] Hardware and software to be used

[1304] User device: Smartphone, tablet, or PC

[1305] Server: A cloud server with high-performance computing resources (e.g., AWS or Google Cloud)

[1306] Natural language processing engines: BERT, GPT, etc.

[1307] Databases: Relational database management systems such as MySQL and PostgreSQL

[1308] Example of a prompt

[1309] As an example of a prompt, consider the text-based question, "What is the stock status?" In response to this question, the system performs the processing described above and provides a quick and appropriate answer.

[1310] As described above, the system of the present invention realizes increased efficiency and speed in providing information at logistics centers.

[1311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1312] Step 1:

[1313] The user enters a question.

[1314] The user launches a dedicated app on their device and enters their question in text or voice format. For example, they might enter the question, "What is the stock status?" This input is processed as a prompt. The input data can be text or voice data.

[1315] Step 2:

[1316] The terminal sends user input to the server.

[1317] The terminal receives user input and converts it into an appropriate format (e.g., JSON). The converted data is prepared as an HTTP request and sent to the server. Specifically, the data is sent using the terminal's network capabilities. The input data is text data, and the output data is formatted JSON data.

[1318] Step 3:

[1319] The server analyzes the user's question.

[1320] The server parses the received request and extracts the user's question from the request body. Then, it uses a natural language processing engine (e.g., BERT or GPT) to analyze the question and extract important keywords. For example, the keyword "stock status" might be extracted. The input data in this step is JSON data, and the output data is the analyzed keywords.

[1321] Step 4:

[1322] The server searches the database.

[1323] The server uses the extracted keywords to send queries to the database and retrieve relevant information. For example, it searches the database for information related to "inventory status." At this time, the database query is executed, and inventory information is retrieved as a result. The input data is keywords, and the output data is the search results.

[1324] Step 5:

[1325] The server generates the appropriate answer.

[1326] The server generates an appropriate answer to the user's question based on the information it has obtained. For example, it might generate the answer, "We currently have 100 items in stock." The generated answer is formatted in JSON format. The input data is the search results, and the output data is the formatted answer in JSON format.

[1327] Step 6:

[1328] The server sends the response back to the terminal.

[1329] The server sends the generated response back to the terminal as an HTTP response. The terminal receives this response and performs analysis. The input data is the response JSON, and the output data is the HTTP response.

[1330] Step 7:

[1331] The device will display the answer.

[1332] The device analyzes the received response, extracts the response text, and displays it to the user. For example, the text "We currently have 100 items in stock" might be displayed on the device's screen. The input data is the HTTP response, and the output data is the displayed text.

[1333] 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.

[1334] The system according to the present invention comprises a terminal in which the user inputs a question, and means for receiving the user input and transmitting it to a server. The server has means for analyzing the user's question, searching for relevant information from a database, generating an appropriate answer from the search results, and then sending it back to the terminal. The terminal also has means for displaying the answer from the server. Furthermore, the system is equipped with an emotion engine that recognizes emotions from the user's input and means for adjusting the answer based on the user's emotions.

[1335] The program for this system works as follows:

[1336] The user launches a dedicated app and enters their question in text format. For example, suppose the user enters the question, "What is your product return policy?" The device receives the user's input, converts it to an appropriate format (e.g., JSON) for transmission to the server, and prepares an HTTP request. The prepared request is then sent from the device to the server.

[1337] The server parses the received request and extracts the user's question from the request body. Then, the server uses a natural language processing engine to analyze the question and extract important keywords. For example, "return policy" might be extracted. Based on the extracted keywords, the server sends a query to the database to retrieve relevant information.

[1338] Based on the information obtained, the server generates an appropriate answer to the user's question. In this case, the answer generated would be, "Unused items can be returned within 30 days of purchase. A receipt is required for returns." The server formats the generated answer in JSON format and sends it back to the terminal as an HTTP response.

[1339] Furthermore, the server uses an emotion engine to recognize emotions from user input. The emotion engine analyzes the question entered by the user and detects the user's emotional state. For example, if the user enters the question, "What do you mean I can't return it?", the emotion engine will detect that the user is angry. Based on this emotional state, the server adjusts the tone and content of the response. In this case, a response such as, "We apologize for the inconvenience. Please contact customer support for details on returns," would be generated.

[1340] The terminal analyzes the received response, extracts the answer text, and displays it to the user. This entire process allows the user to easily obtain the appropriate answer to their question, and the emotion engine enables even more appropriate responses. This system not only significantly improves the efficiency of information retrieval but also contributes to increased customer satisfaction by enabling flexible responses that respond to the user's emotions.

[1341] As a concrete example, suppose a user enters the question, "What are your business hours?" The terminal sends this input to the server, which searches its database using the keyword "business hours." Based on the retrieved information, the server generates the answer, "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for using our service!" to the answer. This can increase the user's satisfaction with the service.

[1342] The following describes the processing flow.

[1343] Step 1:

[1344] The user launches a dedicated app and enters the question in text format.

[1345] Example: Enter "Please tell me your product return policy."

[1346] Step 2:

[1347] The terminal receives user input and converts its contents into JSON format.

[1348] Example: {"question": "Please tell me your product return policy."}

[1349] Step 3:

[1350] The terminal prepares an HTTP request to the server's API endpoint.

[1351] The terminal adds authentication information to the request header and includes the user's question in the request body.

[1352] Step 4:

[1353] The terminal sends an HTTP request to the server, which includes user input.

[1354] Example: Send it to the server as a POST request.

[1355] Step 5:

[1356] The server receives the HTTP request and extracts the user's question from the request body.

[1357] Example: Extract {"question": "Please tell me your product return policy"}.

[1358] Step 6:

[1359] The server uses a natural language processing (NLP) engine to tokenize the user's question and extract important keywords.

[1360] Example: Extract using "return policy" as a keyword.

[1361] Step 7:

[1362] The server uses an emotion engine to analyze the user's question and detect the user's emotional state.

[1363] Example: Detect the user's emotion of "confusion" from the question.

[1364] Step 8:

[1365] The server uses the extracted keywords to send a search query to the information database.

[1366] Example: Send the query "SELECT FROM policies WHERE policy_type = 'returns'" to the database.

[1367] Step 9:

[1368] The server retrieves the appropriate information from the database.

[1369] Example: Retrieve the following information from the database: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1370] Step 10:

[1371] The server uses an emotion engine to detect the user's emotional state and adjusts the tone and content of its response accordingly.

[1372] Example: To a user who appears confused, add a polite tone such as, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1373] Step 11:

[1374] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response.

[1375] For example, send a response in the format of: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."}

[1376] Step 12:

[1377] The terminal receives an HTTP response from the server, parses the response body, and displays it to the user.

[1378] Example: {"answer": "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."} is received and displayed to the user.

[1379] Step 13:

[1380] The user checks the answer displayed on the device and obtains the answer to the question.

[1381] Example: Check the response, "Thank you for your question. Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1382] (Example 2)

[1383] 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".

[1384] Conventional information retrieval systems have a problem not only in generating appropriate answers to user questions, but also in responding based on user emotions. Therefore, in order to improve user satisfaction, it is necessary to recognize user emotions and provide flexible answers that are tailored to them.

[1385] 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 analyzing the user's question and searching for relevant information from a database, means for generating an appropriate answer from the search results, and means for recognizing the user's emotions from the user's input and adjusting the answer. As a result, the user not only receives an appropriate answer to their question but also receives a flexible response that is in line with their emotions.

[1386] A "user" refers to a person who uses the system to input questions and obtain answers.

[1387] A "terminal" refers to a device used by a user to input questions and communicate with a server. Examples include smartphones and personal computers.

[1388] A "server" refers to a device or system that analyzes user input, searches for information in a database, generates an appropriate response, and sends it back to the terminal.

[1389] A "database" refers to a digital storage location where information is accumulated, and is the target of searches performed by a server using queries.

[1390] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[1391] An "emotion engine" refers to software or algorithms that recognize emotions from user input and enable responses that correspond to those emotions.

[1392] An "HTTP request" is a part of the communication protocol used by a terminal to request data from a server, and specifically refers to the method used in web-based communication.

[1393] JSON format is a format for representing data in text format and giving it structure, and is mainly used for sending and receiving data.

[1394] "Keywords" refer to vocabulary extracted from user questions that are considered important for database searches and answer generation.

[1395] "Emotion recognition" refers to technology that identifies emotions from user input and determines the emotional state.

[1396] This system includes a terminal where the user enters a question, and a means for receiving the user input and sending it to a server. The server has means for analyzing the user's question and searching for relevant information in a database. Furthermore, it has means for generating an appropriate answer from the search results and sending that answer back to the terminal. The terminal also has means for displaying the answer from the server, providing the user with appropriate information.

[1397] First, the user launches a dedicated application and enters the question in text format. This question is then captured by the device as text.

[1398] The terminal converts the question received from the user into an HTTP request and sends it to the server. Specifically, the terminal parses the question into JSON format and prepares the HTTP request as follows:

[1399] json

[1400] {

[1401] "Question": "Please tell me your product return policy."

[1402] }

[1403] This JSON data is then sent to the server's endpoint.

[1404] The server receives this request and extracts the question content from the request body. This question content is then analyzed using a natural language processing engine (such as SpaCy or BERT) to extract important keywords. For example, if the user asks "What is the product return policy?", the keyword "return policy" will be extracted.

[1405] The server uses these extracted keywords to send queries to a database (such as PostgreSQL or MySQL) and retrieve the relevant information. For example, the database might contain information such as, "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1406] Based on the information obtained, the server generates an appropriate response for the user. This response is then formatted again into JSON and sent back to the terminal as an HTTP response.

[1407] Furthermore, the server uses an emotion engine (such as IBM Watson Tone Analyzer) to analyze the user's emotional state and provide a more appropriate response. For example, if a user asks, "What do you mean I can't return this?", the server uses the emotion engine to detect the user's anger. Based on this emotional state, the server adjusts the content and tone of its response, generating a reply such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[1408] The terminal analyzes the response received from the server and displays the answer text to the user. This entire process allows the user not only to easily obtain answers to their questions but also to receive flexible responses that are tailored to their emotions.

[1409] For example, if a user enters the question "What are your business hours?", this question is sent from the terminal to the server, which then searches its database using the keyword "business hours". Based on the retrieved information, the server generates the answer "Our business hours are from 9:00 AM to 6:00 PM on weekdays." Furthermore, if the user's emotion is detected as joyful, the server adds a tone such as "Thank you for your continued patronage!" to the answer.

[1410] Example of a prompt:

[1411] "When a user types 'What is your product return policy?', generate an HTTP request that converts this question into JSON format and sends it to the server. Also, describe the process by which the server generates an appropriate answer to this question."

[1412] This system provides detailed processing procedures and specific examples necessary to concretely implement the technical scope of the invention, and serves as a standard for others to accurately understand and practice this invention.

[1413] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1414] Processing steps:

[1415] Step 1:

[1416] The user enters a question.

[1417] Specific actions:

[1418] The user launches the dedicated app and enters the question into the text box.

[1419] Example: Enter "Please tell me your product return policy."

[1420] Input: User's question text.

[1421] Output: The entered question text.

[1422] Step 2:

[1423] The terminal sends the entered question to the server.

[1424] Specific actions:

[1425] The terminal receives the entered question and converts it to JSON format.

[1426] Example of converted JSON:

[1427] json

[1428] {

[1429] "Question": "Please tell me your product return policy."

[1430] }

[1431] The device sets this JSON in the body of the HTTP request and sends it to the server's endpoint.

[1432] Input: The entered question text.

[1433] Output: The HTTP request sent to the server.

[1434] Step 3:

[1435] The server analyzes the question.

[1436] Specific actions:

[1437] The server parses the received HTTP request and extracts the question from the request body.

[1438] The server uses a natural language processing engine (e.g., SpaCy or BERT) to analyze the question and extract important keywords.

[1439] Example: The keyword "return policy" is extracted.

[1440] Input: HTTP request (including the question in JSON format).

[1441] Output: Extracted keywords (e.g., "return policy").

[1442] Step 4:

[1443] The server searches the database.

[1444] Specific actions:

[1445] The server sends a query to the database based on the extracted keywords.

[1446] example:

[1447] sql

[1448] SELECT policy FROM return_policies WHERE keyword = 'return_policies';

[1449] Retrieve relevant information from the database.

[1450] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1451] Input: Extracted keywords (e.g., "return policy").

[1452] Output: Information retrieved from the database.

[1453] Step 5:

[1454] The server generates the answer.

[1455] Specific actions:

[1456] The server generates appropriate answers to the user's questions based on the information it has obtained.

[1457] The generated response is formatted into JSON and sent back to the terminal as an HTTP response.

[1458] example:

[1459] json

[1460] {

[1461] "Response": "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1462] }

[1463] Input: Information retrieved from the database.

[1464] Output: Generated response (in JSON format).

[1465] Step 6:

[1466] The server uses an emotion engine to recognize emotions.

[1467] Specific actions:

[1468] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's question and recognize its sentiment.

[1469] Example: The system detects "anger" in response to the question, "What do you mean I can't return it?"

[1470] Input: User's question text.

[1471] Output: Detected emotional state (e.g., "anger").

[1472] Step 7:

[1473] The server adjusts its response based on emotions.

[1474] Specific actions:

[1475] The server adjusts the tone and content of the response based on the results of the emotion engine.

[1476] Example: Response when "anger" is detected:

[1477] "We apologize for any inconvenience this may have caused. Please contact customer support for details regarding returns."

[1478] Input: Detected emotional state.

[1479] Output: Adjusted response text.

[1480] Step 8:

[1481] The device will display the answer.

[1482] Specific actions:

[1483] The terminal analyzes the response received from the server and extracts the answer text.

[1484] The extracted response text is displayed to the user.

[1485] Example: "Unused items can be returned within 30 days of purchase. A receipt is required for returns."

[1486] Input: Response received from the server (in JSON format).

[1487] Output: The response text displayed to the user.

[1488] The above outlines the specific processing steps of this system. This allows users not only to receive appropriate answers to their questions but also to receive flexible responses that take their emotions into account.

[1489] (Application Example 2)

[1490] 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".

[1491] Traditional information retrieval systems have the drawback of only being able to provide a uniform answer to user-inputted questions, making it difficult to respond appropriately to users' emotions and circumstances. Furthermore, depending on the content of the question, it may not be possible to clearly define what the user wants to know, often resulting in a long wait to obtain an accurate answer.

[1492] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input and transmitting it to the server, means for analyzing the user's question and searching for relevant information from a database, means for recognizing emotions from the user's input and adjusting the tone and content of the response based on the recognized emotions, and means for generating and providing appropriate prompt sentences using a generative AI model. This makes it possible to quickly provide appropriate answers that are in line with the user's emotions and situation.

[1493] "The device on which the user enters the question" refers to a device used by the user to enter and submit a question in text format, and includes smartphones, tablets, or computers.

[1494] "Means for receiving user input and sending it to the server" refers to a software algorithm or protocol for transferring user-entered questions to a server.

[1495] A "server that analyzes user questions and retrieves relevant information from a database" refers to a server that uses natural language processing technology to analyze user-inputted questions and retrieves relevant information from a database.

[1496] "A means of generating an appropriate answer from search results and sending it back to the terminal" refers to a server-based processing function that generates an appropriate answer based on information obtained from a database and sends it back to the terminal from which the user entered the question.

[1497] "A means for a terminal to display a response from a server" refers to software that has the function of displaying the response received from the server on the screen of the terminal on which the user entered the question.

[1498] "A means of recognizing emotions from user input and adjusting responses" refers to an emotion analysis algorithm that analyzes the emotions from the questions entered by the user and adjusts the tone and content of the response based on the detected emotions.

[1499] "A means of analyzing user questions and extracting keywords using natural language processing" refers to an algorithm that uses natural language processing techniques to analyze user-entered questions and identify important words or phrases.

[1500] "A means of formatting the answers generated from search results into JSON format and sending them back to the device" refers to a function that formats the answers generated based on information obtained from the database into JSON format and sends them back to the device on which the user entered the question.

[1501] "Means for generating and providing appropriate prompt sentences using a generative AI model" refers to a function that uses a generative AI model to clarify the content of a question and derive an appropriate prompt sentence when a user inputs a question.

[1502]

[1503] This invention is a system that includes a terminal in which a user inputs a question, means for receiving user input and transmitting it to a server, server means for analyzing the user's question and searching for related information from a database, means for generating an appropriate answer from the search results and sending it back to the terminal, means for the terminal to display the answer from the server, means for recognizing emotions from the user's input and adjusting the answer, and means for generating and providing an appropriate prompt sentence using a generative AI model.

[1504] Description of the system's programs and processes:

[1505] 1. User question input and sentiment analysis

[1506] A smartphone, tablet, or computer is used as the terminal. Users input questions in text format using these devices. For example, a question such as "What is your product return policy?" This input is analyzed by an emotion analysis algorithm to determine the user's emotional state. Emotion analysis is performed based on specific keywords and symbols, such as "!" or "why," to detect emotions from the entered text.

[1507] 2. Question Analysis and Information Retrieval

[1508] After receiving user input, the terminal converts this input to JSON format and sends it to the server. The server parses the received input using natural language processing techniques (e.g., Python's natural language processing library) and extracts keywords. For example, the keyword "return policy" might be extracted. Based on this keyword, the server queries the database to retrieve relevant information.

[1509] 3. Generating and adjusting responses

[1510] The server generates an appropriate response based on information retrieved from the database. This response is then adjusted based on the results of sentiment analysis. For example, if a user enters "What do you mean I can't return it?", the server's sentiment engine detects that the user is angry and generates a response in a tone such as, "We apologize for the inconvenience. Please contact customer support for details on returns."

[1511] 4. Return and display of responses

[1512] The generated response is formatted in JSON and sent back to the terminal as an HTTP response. The terminal receives this response and displays it to the user in an appropriate format. This allows the user to get answers quickly and receive flexible responses that are sensitive to their emotions.

[1513] 5. Providing prompt sentences using a generative AI model

[1514] The system also includes a mechanism to generate and present appropriate prompts to the user using a generative AI model when a question is entered. For example, when a general question is entered, the model provides appropriate completion and suggestions to make the information the user is looking for more specific. An example of a prompt is shown below.

[1515] "Please tell me your return policy."

[1516] "When does the sale start?"

[1517] This allows users to quickly obtain clear and accurate information.

[1518] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1519] Step 1:

[1520] The user enters a question into the device. During input, the device uses a generative AI model to generate appropriate prompts and present them to the user. For example, if the user enters a question like "What is your return policy?", the generative AI model analyzes it and provides completions or suggestions. Input: User's text question. Output: Generated prompts and the user's final question.

[1521] Step 2:

[1522] The terminal receives the user's input question and performs sentiment analysis. The sentiment analysis algorithm analyzes the user's emotional state from the input text. For example, it determines emotions based on exclamation marks and question words. Input: User's final question. Output: Analyzed emotional state (e.g., anger, joy, neutral).

[1523] Step 3:

[1524] The terminal converts the question into JSON format and prepares an HTTP request to send to the server. For example, it will be formatted as {"question": "What is your return policy?"}. Input: User's final question and sentiment state. Output: HTTP request (in JSON format).

[1525] Step 4:

[1526] The server parses the received HTTP request and extracts the content of the question. The server then uses a natural language processing engine (e.g., Python's NLP library) to analyze the question and extract keywords. Input: HTTP request (JSON format). Output: Extracted keywords (e.g., "return policy").

[1527] Step 5:

[1528] The server sends queries to the database based on the extracted keywords and retrieves relevant information. For example, it retrieves information about the "return policy" from the database. Input: Extracted keywords. Output: Information retrieved from the database.

[1529] Step 6:

[1530] The server generates an answer to the user's question based on the information it has gathered. Based on the sentiment analysis results, it adjusts the tone and content of the answer. For example, it might be adjusted to something like, "We apologize for any inconvenience this may have caused. Please contact customer support for details on returns." Input: Information gathered from the database and analyzed sentiment. Output: Adjusted answer.

[1531] Step 7:

[1532] The server formats the generated response into JSON format and sends it back to the terminal as an HTTP response. For example, it might be formatted as {"answer": "Unused items can be returned within 30 days of purchase."}. Input: Generated response. Output: HTTP response (JSON format).

[1533] Step 8:

[1534] The terminal analyzes the received response and displays the reply text. The user can see the content, and the system also provides flexible responses tailored to their emotions. Input: HTTP response (JSON format). Output: Reply text displayed to the user.

[1535] 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.

[1536] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1537] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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."

[1544] 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.

[1545] 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] The following is further disclosed regarding the embodiments described above.

[1557] (Claim 1)

[1558] A terminal where the user enters a question,

[1559] A means of receiving user input and sending it to the server,

[1560] A server that analyzes user questions and retrieves relevant information from a database,

[1561] A means of generating an appropriate answer from the search results and sending it back to the device,

[1562] A means by which the terminal displays the response from the server,

[1563] A system that includes this.

[1564] (Claim 2)

[1565] The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

[1566] (Claim 3)

[1567] The system according to claim 1, further comprising means for formatting the response generated from the search results in JSON format and returning it to the terminal.

[1568] "Example 1"

[1569] (Claim 1)

[1570] The device used by the user to enter the question,

[1571] A means for receiving user input and transmitting it to a data processing device,

[1572] A data processing device that analyzes user questions and retrieves relevant information from storage devices,

[1573] A means of generating an appropriate answer from the search results and sending it back to the user's device,

[1574] A means by which the user's terminal displays the response from the data processing device,

[1575] A system that includes this.

[1576] (Claim 2)

[1577] The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

[1578] (Claim 3)

[1579] The system according to claim 1, further comprising means for formatting the answers generated from the search results into a standard data format and returning them to the user's terminal.

[1580] "Application Example 1"

[1581] (Claim 1)

[1582] A terminal where the user enters a question,

[1583] A means of receiving user input and sending it to the server,

[1584] A server that analyzes user questions and retrieves relevant information from a database,

[1585] A means of generating an appropriate answer from the search results and sending it back to the device,

[1586] A means by which the terminal displays the response from the server,

[1587] A means of quickly providing inventory status and work instructions, installed on equipment within the logistics center.

[1588] A system that includes this.

[1589] (Claim 2)

[1590] The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

[1591] (Claim 3)

[1592] The system according to claim 1, further comprising means for formatting the response generated from the search results in JSON format and returning it to the terminal.

[1593] "Example 2 of combining an emotion engine"

[1594] (Claim 1)

[1595] A terminal where the user enters a question,

[1596] A means of receiving user input and sending it to the server,

[1597] A server that analyzes user questions and retrieves relevant information from a database,

[1598] A means of generating an appropriate answer from the search results and sending it back to the device,

[1599] A means by which the terminal displays the response from the server,

[1600] A server mechanism that recognizes emotions from user input and adjusts responses accordingly,

[1601] A system that includes this.

[1602] (Claim 2)

[1603] The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

[1604] (Claim 3)

[1605] The system according to claim 1, further comprising means for formatting the response generated from the search results in JSON format and returning it to the terminal.

[1606] "Application example 2 when combining with an emotional engine"

[1607] (Claim 1)

[1608] A terminal where the user enters a question,

[1609] A means of receiving user input and sending it to the server,

[1610] A server that analyzes user questions and retrieves relevant information from a database,

[1611] A means of generating an appropriate answer from the search results and sending it back to the device,

[1612] A means by which the terminal displays the response from the server,

[1613] A means of recognizing emotions from user input and adjusting responses accordingly,

[1614] A system that includes this.

[1615] (Claim 2)

[1616] The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

[1617] (Claim 3)

[1618] The system according to claim 1, further comprising means for formatting the response generated from the search results in JSON format and returning it to the terminal.

[1619] (Claim 4)

[1620] The system according to claim 1, further comprising means for recognizing emotions from user input and adjusting the tone and content of the response based on the recognized emotions.

[1621] (Claim 5)

[1622] The system according to claim 1, comprising means for generating and providing appropriate prompt sentences using a generative AI model when a user inputs a question. [Explanation of symbols]

[1623] 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 terminal where the user enters a question, A means of receiving user input and sending it to the server, A server that analyzes user questions and retrieves relevant information from a database, A means of generating an appropriate answer from the search results and sending it back to the device, A means by which the terminal displays the response from the server, A system that includes this.

2. The system according to claim 1, comprising means for analyzing a user's question using natural language processing and extracting keywords.

3. The system according to claim 1, further comprising means for formatting the response generated from the search results in JSON format and returning it to the terminal.

Images