System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving data inputted from a user, a means for analyzing the received data by natural language processing, a means for retrieving a database on the basis of the analyzed data, a means for generating an appropriate answer from the retrieval result, and a means for transmitting the generated answer to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the past, when users manually searched for information in the Answers database, it took time to find the relevant data, which was inefficient. Furthermore, the system sometimes failed to accurately understand the user's intent and provide an appropriate answer. The present invention aims to solve these problems and provide a system that can quickly and accurately respond to user questions. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for receiving data input by a user; means for analyzing the received data using natural language processing; means for searching a database based on the analyzed data; means for generating an appropriate answer from the search results; and means for sending the generated answer to the user. Furthermore, by including means for extracting keywords from the analyzed data and means for selecting the most relevant answer from the search results, the system is able to provide an appropriate and prompt answer to a user's question.

[0006] 1. "User" refers to a person who uses the system to input or search for information.

[0007] 2. "Input Data" refers to the text and questions provided by the user to the system.

[0008] 3. "Receiving means" refers to the function by which the system receives input data from the user.

[0009] 4. "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0010] 5. "Analysis means" refers to the function of analyzing received data using natural language processing.

[0011] 6. "Database" refers to a collection of information maintained by a system.

[0012] 7. "Search tool" refers to the function of locating information in a database based on analyzed data.

[0013] 8. "Answer generation means" refers to the function for creating appropriate answers from search results.

[0014] 9. "Transmission means" refers to the function for transmitting the generated answer to the user.

[0015] 10. "Keyword extraction means" refers to the function of extracting important words and phrases from analyzed data.

[0016] 11. "Answer selection tool" refers to the function that selects the most relevant answer from the search results. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system that provides prompt and appropriate answers based on user input data. This system consists of a series of processes that receive a question from a user, search a database using natural language processing technology, generate a relevant answer, and return it to the user.

[0039] Specific Embodiments

[0040] Input from the user

[0041] The user uses the chatbot interface provided on the device to input a question, which can be specific, such as "How do I return a product?"

[0042] Receiving input data

[0043] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0044] Data analysis

[0045] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0046] Searching the database

[0047] The server uses the extracted keywords to search a database containing past inquiries and their answers, and quickly finds the most appropriate answer based on the keywords.

[0048] Generate answers

[0049] Based on the searched information, the server generates the most appropriate answer for the user. For example, if the question is "How do I return a product?", the appropriate answer generated would be "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return."

[0050] Submit your answer

[0051] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0052] Specific examples

[0053] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0054] 1. The user enters a question and the device sends the data to the server.

[0055] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0056] 3. The database is searched and relevant information is retrieved.

[0057] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0058] 5. This response is sent to the terminal and confirmed by the user.

[0059] The system of the present invention allows users to quickly obtain appropriate information without having to manually search for information, thereby significantly improving user convenience.

[0060] The processing flow will be explained below.

[0061] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0062] Step 2: The device sends the entered question data to the chatbot server. Specifically, the data is converted into an appropriate format and sent to the server as an HTTP request.

[0063] Step 3: The server receives the data sent from the device and passes it to the natural language processing engine.

[0064] Step 4: The server's natural language processing engine analyzes the received data. As a result of the analysis, keywords such as "product," "return," and "procedure" are extracted.

[0065] Step 5: The server uses the extracted keywords to search the database, quickly finding relevant information and obtaining search results.

[0066] Step 6: The server generates the most appropriate answer based on the search results. For example, it creates a specific answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0067] Step 7: The server sends the generated response data to the terminal as an HTTP response.

[0068] Step 8: The device analyzes the received response data and displays it in a user-friendly format. The user interface is updated to display the response.

[0069] Step 9: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain the necessary information.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] Conventional systems have had difficulty in providing fast and accurate answers to user input. In particular, the process of properly formatting a user's question, analyzing it, and generating relevant answers is cumbersome, reducing the efficiency of the entire system. The present invention aims to solve these problems and provide a system that provides fast and appropriate answers to questions entered by a user.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes means for receiving data input by a user, means for arranging the received data into an appropriate format, means for analyzing the arranged data using a natural language processing engine, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating the most appropriate answer from the search results, means for arranging the generated answer into an appropriate format, and means for sending the generated answer to the user. This automates the generation and provision of answers to user input, making it possible to significantly improve the efficiency of the entire system.

[0075] A "user" is an individual or organization that interacts with the system and enters questions or data to obtain information.

[0076] A "terminal" is an electronic device, such as a computer or smartphone, that a user uses to access the system.

[0077] A "server" is a computer system that receives data input from a user, processes and analyzes the data, and generates and returns an appropriate response to the user.

[0078] "Means for receiving data" refers to a function that executes a process by which the server receives information sent by the user.

[0079] "Formatting means" refers to the processes or algorithms used to convert the raw data received from the user into a form that is easier to analyze.

[0080] A "natural language processing engine" is technology or software that allows a computer to analyze, understand, and generate human language.

[0081] "Keyword extraction" refers to the process of identifying and extracting important words and phrases from the analyzed data.

[0082] "Means for searching the database" refers to the process or algorithm that uses the extracted keywords to find relevant information within the database.

[0083] "Answer generation" refers to the process of constructing the most relevant answer to the user's question based on database search results.

[0084] "Formatting" refers to the process of reconstructing the generated answers into a form that is easy for the user to understand.

[0085] The "means for sending an answer" refers to a process for sending the generated answer to the user's terminal and displaying it.

[0086] A "scoring algorithm" is a computational method for evaluating search results and data and quantifying their relevance and importance.

[0087] The "HTTP request and response format" refers to the process of making a request and a response in a format widely used as a communication protocol between a user and a server.

[0088] The present invention is a system that provides quick and appropriate answers to questions entered by a user. This system receives user input data, analyzes it using natural language processing technology, searches for related information from a database, generates an answer, and returns it to the user. The system of the present invention is realized through multiple processing steps.

[0089] System installation and configuration

[0090] Users use devices such as computers or smartphones, which have an internet browser or dedicated application installed, through which they can access the chatbot interface.

[0091] A server is a computer system that runs in a cloud or on-premise environment and includes software such as a natural language processing engine (e.g., spaCy, NLTK), a database system (e.g., MySQL (registered trademark), PostgreSQL), etc. The server performs the task of analyzing input data received from users and generating appropriate answers.

[0092] Receiving and formatting data

[0093] A user uses the chatbot interface on their device to input a specific question, which the device then formats in an appropriate format (e.g., JSON) and sends as an HTTP request to the server. For example, a user might input, "How do I return an item?" This input data includes the question as well as other metadata such as the user ID and a timestamp.

[0094] Analysis and keyword extraction using natural language processing

[0095] The server receives the HTTP request sent from the device and analyzes the data. A natural language processing engine is used for the analysis, which first extracts sentences from the data, then tokenizes them and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0096] Database Search and Scoring Algorithms

[0097] Based on the extracted keywords, the server searches a database containing past queries and their answers, quickly finding the most relevant answer based on the keywords. If there are multiple search results, a scoring algorithm is used to select the most relevant one.

[0098] Answer generation and formatting

[0099] The server generates the most appropriate answer for the user based on the search results. For example, if the question is "How do I return a product?", the server might generate an answer such as "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return." This answer is then formatted and converted into a user-friendly format.

[0100] Submitting the answer and displaying it to the user

[0101] The generated answer is sent to the terminal again as an HTTP response, and the terminal receives this data and displays it in a format that is easy for the user to understand, allowing the user to quickly obtain an answer to their question.

[0102] Examples and prompts

[0103] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0104] 1. The user enters a question and the device sends the data to the server.

[0105] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0106] 3. The database is searched and relevant information is retrieved.

[0107] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0108] 5. This response is sent to the terminal and confirmed by the user.

[0109] An example of a prompt sentence is, "If a user asks about the return procedure for a product, how would you respond?" This makes it easier to understand the system's behavior in concrete terms.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] The user enters a question and the device sends the data to the server. The user enters a question using the chatbot interface on the device. As input, the user enters "Please tell me how to return a product," and the device formats this data into an appropriate format (e.g., JSON format). The formatted data is sent to the server as an HTTP POST request. As output, the user's input data is sent to the server.

[0113] Step 2:

[0114] The server receives the data and parses it using a natural language processing engine. The server receives the data sent as an HTTP request and parses the input data. It receives JSON format data as input and uses a natural language processing engine (e.g. spaCy, NLTK) to tokenize the data and parse the important parts from the sentence. For example, it parses "Please tell me the procedure for returning a product" and extracts the important parts. The parsed token data is obtained as output.

[0115] Step 3:

[0116] The server extracts keywords from the parsed data. The server is responsible for extracting key keywords from the parsed data. It takes as input the token data from the previous step and applies an algorithm to extract key keywords (e.g., "product", "return", "procedure"). The output is a list of keywords.

[0117] Step 4:

[0118] The server searches the database based on the keywords. The server uses the extracted keywords to search for relevant information in the database. It receives a list of keywords as input and performs a database search query. The database stores past queries and their answers, and retrieves the most relevant answer based on the keywords. The search results are obtained as the output.

[0119] Step 5:

[0120] The server generates the best answer. The server analyzes the search results from the database and generates the most appropriate answer for the user. It receives the search results as input, uses a scoring algorithm to select the most relevant answer, and formats it appropriately. For example, the server might generate an answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you." The generated answer is obtained as output.

[0121] Step 6:

[0122] The server sends the generated answer to the terminal, which displays it to the user. The server sends the generated answer to the terminal as an HTTP response. It receives the generated answer data as input, formats it as an HTTP response, and sends it to the terminal. The terminal receives this data and displays it in a format that is easy for the user to understand. The appropriate answer is displayed on the user's screen as output.

[0123] In this way, the system of the present invention can efficiently provide quick and appropriate responses to user input.

[0124] (Application example 1)

[0125] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] When customers in physical stores ask about products, how to purchase them, or details about return and exchange procedures, it is difficult to provide them with the right information quickly. This also places a heavy burden on store staff, which could lead to a decline in the quality of customer service.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0128] In this invention, the server includes a means for receiving data input by a user, a means for analyzing the received data using natural language processing, a means for searching a database based on the analyzed data, and a means for displaying a generated answer on a smartphone or smart glasses, thereby enabling prompt and appropriate answers to be provided in real time when customers ask questions in a physical store.

[0129] "User" means any person or entity that uses the System to receive information or services.

[0130] "Data" is information such as a question or request that a user provides to the system.

[0131] The "receiving means" refers to a function or device for receiving data input by a user.

[0132] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0133] "Means for analyzing" refers to a function or device for analyzing received data and understanding its meaning and structure.

[0134] A "database" is a collection of information that stores past inquiries and their answers.

[0135] "Searching means" refers to the function or device for searching a database based on the analyzed data and finding relevant information.

[0136] "Means for generating" refers to the functions and devices for generating appropriate answers from search results.

[0137] "Transmission means" refers to a function or device for delivering the generated answer to the user.

[0138] "Displaying means" refers to a function or device for displaying the generated answer on a smartphone or smart glasses.

[0139] "Keywords" are important words or phrases extracted from the analyzed data.

[0140] "Relevant answers" refer to answers that best fit the user's question.

[0141] A "smartphone" is a type of mobile phone that can access the Internet and use applications.

[0142] "Smart glasses" are a wearable device in the form of glasses that are equipped with a display and sensors and provide augmented reality functionality.

[0143] System Program

[0144] A program for implementing the system is generated with the following configuration.

[0145] 1. A means of receiving data entered by the user

[0146] The user uses a smartphone or smart glasses to enter a question, which is sent to the server as an HTTP request.

[0147] 2. A means of analyzing received data using natural language processing

[0148] The server analyzes the received data using the Python library spaCy and Transformers. As a natural language processing task, it understands the structure and meaning of the question and extracts keywords.

[0149] 3. A means of searching the database based on the analyzed data

[0150] Based on the extracted keywords, the server uses an SQL query to search an SQLite database, which stores past queries and their answers.

[0151] 4. A means of sending the generated answer to the user

[0152] The most appropriate answer is generated from the search results and sent back to the user's device as an HTTP response, where the user can view the answer through their smartphone or smart glasses.

[0153] 5. A means of displaying the generated answers on a smartphone or smart glasses

[0154] Submitted responses are properly formatted and displayed on the user's device in a visually friendly format.

[0155] Hardware and software used

[0156] Hardware: iPhone®, Android® smartphones, smart glasses (e.g., Google® Glass®).

[0157] Software: Python, Django (web framework), spaCy (natural language processing library), Transformers (NLP model library), SQLite (database).

[0158] Detailed explanation of the process

[0159] 1. User Input

[0160] Example: A user types the question "How do you use this product?" into their smartphone.

[0161] 2. Receiving input data

[0162] The question is sent from the device to the server as an HTTP request.

[0163] 3. Data Analysis

[0164] The server receives the question and analyzes it using spaCy and Transformers. For example, it extracts keywords such as "product" and "use" from the question.

[0165] 4. Database Search

[0166] It searches an SQLite database based on keywords to find the most relevant answer, such as "Please soak this product in 100°C water for 5 minutes before use."

[0167] 5. Generate and submit answers

[0168] The optimal answer is generated and sent to the user's device.

[0169] 6. View Answers

[0170] The answer will be displayed on your smartphone or smart glasses.

[0171] Examples of concrete examples and prompts

[0172] Specific examples

[0173] When a user asks "How do I use this product?" in a physical store, the following happens:

[0174] 1. User input: Type "How would you like to use this product?" into your smartphone.

[0175] 2. Server analysis: Extract keywords for "product" and "use."

[0176] 3. Database search: Search the database for documents on how to use the product.

[0177] 4. Answer generation: Generate specific answers such as "Please soak this product in 100 degree water for 5 minutes before use."

[0178] 5. Display answers: The answers are displayed to the user on their smartphone.

[0179] Prompt Sentence Examples

[0180] User: "How do you use this product?"

[0181] Server: "Please soak this product in 100°C water for 5 minutes before use. Please refer to the instruction manual for details."

[0182] With the above configuration, the present invention dramatically improves customer service in brick-and-mortar stores, making it possible to provide quick and appropriate responses.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The user inputs a question using a smartphone or smart glasses. For example, they input "How do I use this product?" The device sends the input question data to the server as an HTTP request. The input data is sent from the device to the server in the form of a request.

[0186] Step 2:

[0187] The server analyzes the received HTTP request and extracts the question data. The extracted question data is passed to a natural language processing engine (e.g., spaCy or Transformers). The input is the question data, and the output is the analyzed structured data. The natural language processing engine analyzes the meaning and structure of the question and extracts important keywords. For example, the keywords "product" and "use" are extracted.

[0188] Step 3:

[0189] The server searches a database (e.g., SQLite) based on the extracted keywords. The input is the keywords, and the output is a list of related answers. It executes the search query and retrieves the best answer candidates from the database. For example, it might find an answer such as, "Please soak this product in 100°C water for 5 minutes before use."

[0190] Step 4:

[0191] The server generates the most appropriate answer from the search results. The input is a list of search results, and the output is the optimal answer. The most relevant answer is automatically selected and formatted in a user-friendly format using natural language generation technology. For example, a specific answer such as "Please soak this product in 100°C water for 5 minutes before use" is generated.

[0192] Step 5:

[0193] The server sends the generated answer to the terminal as an HTTP response. The input is the generated answer, and the output is the sent HTTP response message. The server performs transmission processing so that the answer reaches the terminal.

[0194] Step 6:

[0195] The device processes the received HTTP response and generates an answer that is displayed on the display of the smartphone or smart glasses. The input is the HTTP response message, and the output is the visually displayed answer. The user can check the answer on the device screen. For example, the answer may read, "Please soak this product in 100°C water for 5 minutes before use."

[0196] Through these processing steps, users can get quick and appropriate answers in real time without having to ask questions directly at a physical store.

[0197] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0198] The present invention is a system that receives user input data, analyzes it using a combination of natural language processing technology and an emotion engine, and provides appropriate answers. The system aims to generate fast and emotionally sensitive responses to user questions and feedback.

[0199] Specific Embodiments

[0200] Input from the user

[0201] The user uses the chatbot interface provided on the device to input a question, for example, "Please tell me how to return a product."

[0202] Receiving input data

[0203] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0204] Data analysis

[0205] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0206] Emotion recognition

[0207] The server's emotion engine recognizes emotions from the user's input data. For example, if the user's text expresses anger or frustration, the emotion is extracted.

[0208] Searching the database

[0209] The server uses the extracted keywords and emotion information to search a database that stores past queries and their answers, and quickly finds the most appropriate answer based on the keywords and emotions.

[0210] Generate and refine answers

[0211] Based on the retrieved information, the server generates the most appropriate answer for the user. Furthermore, the emotion engine adjusts the tone and expression of the answer based on the emotion recognized by the server. For example, if the user expresses dissatisfaction, the answer may include an apology or sympathy.

[0212] Submit your answer

[0213] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0214] Specific examples

[0215] For example, if a user types "Please tell me how to return an item" into a terminal and the input contains an angry emotion, the system will act as follows:

[0216] 1. The user enters a question and the device sends the data to the server.

[0217] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0218] 3. The emotion engine recognizes the user's anger emotion.

[0219] 4. The database is searched and relevant information is retrieved.

[0220] 5. The server generates an emotionally sensitive response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[0221] 6. This response is sent to the terminal and confirmed by the user.

[0222] The system of the present invention saves users the trouble of manually searching for information and enables them to quickly obtain appropriate information. In addition, the incorporation of an emotion engine enables proactive responses that take into account the user's emotions, improving the user experience.

[0223] The processing flow will be explained below.

[0224] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0225] Step 2: The device sends the entered question data to the chatbot server, which converts the data into an appropriate format and sends it to the server as an HTTP request.

[0226] Step 3: The server receives the data sent from the device and passes it to a natural language processing engine for analysis.

[0227] Step 4: The server's natural language processing engine analyzes the received data. During the analysis, important keywords such as "product," "return," and "procedure" are extracted.

[0228] Step 5: The server's emotion engine recognizes emotions from the user's input data. If the user's text indicates anger or frustration, the emotion is extracted.

[0229] Step 6: The server uses the extracted keywords and sentiment information to search the database, quickly find relevant information, and obtain search results.

[0230] Step 7: The server generates an appropriate answer based on the search results retrieved from the database. The generated answer is adjusted based on the emotions recognized by the emotion engine. For example, if the user is dissatisfied, an apology will be added to the answer.

[0231] Step 8: The server generates a response and sends it to the terminal as an HTTP response. The response data is formatted in a user-friendly format.

[0232] Step 9: The device analyzes the received response data and updates the user interface, displaying the appropriate response to the user.

[0233] Step 10: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain an answer to their question.

[0234] Example 2

[0235] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0236] Conventional chatbot systems can sometimes have difficulty generating quick and appropriate answers to user questions. They also lack the functionality to provide answers that take the user's emotions into consideration, resulting in a poor user experience. Furthermore, the process of keyword extraction and emotion recognition, which are necessary to efficiently generate answers for multiple questions, is not automated, limiting the system's response speed and accuracy.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0238] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using a natural language processing device, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating an appropriate answer from the search results, means for recognizing emotions from the user's input data, means for adjusting the tone and expression of the answer based on the recognized emotions, and means for sending the generated answer to the user.

[0239] This enables quick and appropriate responses, and by providing answers that take the user's feelings into consideration, it is possible to improve the user experience and increase the system's response speed and accuracy.

[0240] "User-input data" refers to the text data of questions or requests that users enter into the chatbot interface.

[0241] "Means for receiving" refers to the functions or devices that allow the server to receive data sent from the user.

[0242] A "natural language processing system" is a device or software that analyzes text data and understands its meaning and structure.

[0243] "Means for analyzing" refers to the function of analyzing received data using a natural language processing device and extracting meaning and keywords.

[0244] "Keyword extraction means" is the ability to identify and extract important words and phrases from the analyzed data.

[0245] The "means for searching a database" is a function for searching a database for related information based on the extracted keywords.

[0246] "Means for generating appropriate answers" is a function that creates the most appropriate answer to the user's question based on the search results.

[0247] "Means for recognizing emotions" is a function that identifies emotions from the user's text data and extracts those emotions as data.

[0248] "Means for adjusting the tone and expression of responses" refers to a function that appropriately changes the wording and expression of generated responses based on the recognized sentiment.

[0249] "Transmitting means" refers to a function or device for transmitting the generated answer to the user's terminal and displaying it.

[0250] The present invention is a system that receives user input data, analyzes it using a natural language processing system and an emotion recognition engine, and provides appropriate answers. The system aims to generate fast and emotion-sensitive responses to user questions and feedback.

[0251] Hardware and software used

[0252] The system consists of a terminal that receives user input and a server that analyzes the data and generates responses. The terminal provides a chatbot interface using a web browser or mobile application. The server analyzes the data and recognizes emotions using a natural language processing system (e.g., SpaCy or NLTK) and an emotion recognition engine (e.g., Microsoft® Azure® Text Sentiment Analysis API).

[0253] Data processing and calculation methods

[0254] When a user enters a question and presses the send button, the device sends the input data to the server as an HTTP request. The server analyzes the received data using a natural language processing device and extracts important keywords. Next, an emotion recognition engine recognizes the user's emotions from the input data. Based on the extracted keywords and recognized emotion information, the server searches a database to retrieve related information.

[0255] The server then generates the most appropriate answer for the user based on the search results, adjusting the tone and expression of the answer based on the emotions recognized by the emotion recognition engine. Finally, the generated answer is sent to the device as an HTTP response and displayed to the user.

[0256] Specific examples

[0257] For example, if a user types "Please tell me how to return a product" into a terminal, and includes a feeling of dissatisfaction, the system will act as follows:

[0258] 1. The user enters a question into the terminal and presses the send button.

[0259] Input: "Please tell me the procedure for returning an item."

[0260] 2. The terminal sends the input data to the server as an HTTP request.

[0261] Data format: JSON

[0262] Content: {"question": "Please tell me how to return a product"}

[0263] 3. The server analyzes the received data and extracts keywords using a natural language processing device.

[0264] Extracted keywords: "product", "return", "procedure"

[0265] 4. The server's emotion recognition engine recognizes the user's emotion.

[0266] Extracted emotion: "Dissatisfied"

[0267] 5. The server searches the database and retrieves the relevant information.

[0268] SQL query: SELECT FROM responses WHERE keywords LIKE '%product%' AND keywords LIKE '%return%' AND keywords LIKE '%procedure%';

[0269] 6. The server generates an appropriate response and adjusts the expression to take sentiment into consideration.

[0270] Generated response: "If you bring the product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[0271] 7. The server generates an answer and sends it to the device as an HTTP response.

[0272] Data format: JSON

[0273] Content: {"response": "Please bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."}

[0274] 8. The terminal displays the received response to the user.

[0275] Display message: "We can assist you if you bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."

[0276] Prompt Sentence Examples

[0277] Design a system that uses natural language processing and emotion recognition to generate appropriate responses to user inputs such as "Please tell me about the procedure for returning a product." If the user is dissatisfied, the response should include an apology or expression of sympathy.

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1: Input from the user

[0280] The user uses the provided chatbot interface to enter a question, for example, "How do I return a product?"

[0281] Input: Text data entered by the user into the chatbot interface

[0282] Output: Input data from the user

[0283] Step 2: Receiving input data

[0284] When the user presses the send button, the device sends the input data as an HTTP request to the chatbot server, converting it into an appropriate format (e.g., JSON).

[0285] Input: Data entered by the user

[0286] Output: Data formatted as an HTTP request

[0287] Step 3: Analyze the data

[0288] The server analyzes the received data. First, the server uses a natural language processing system to analyze the text data and extract important keywords. Specifically, it uses an analysis engine (e.g., SpaCy, NLTK) to extract the keywords "product," "return," and "procedure."

[0289] Input: Data received as an HTTP request

[0290] Output: Extracted keywords (e.g., "product", "return", "procedure")

[0291] Step 4: Recognize emotions

[0292] The server's emotion recognition engine recognizes emotions from the user's input data. For example, it uses an emotion analysis engine (e.g., Microsoft Azure's text emotion analysis API) to extract emotions such as "dissatisfaction" or "anger."

[0293] Input: User-entered data

[0294] Output: Extracted emotion (e.g., "dissatisfied," "anger")

[0295] Step 5: Search the database

[0296] The server uses the extracted keywords and sentiment information to search a database that stores past queries and their answers. The server executes SQL queries to find the most relevant answers based on the keywords and sentiment.

[0297] Input: Extracted keywords and sentiment information

[0298] Output: Database search results (e.g. related answer information)

[0299] Step 6: Generate and refine answers

[0300] The server generates the most appropriate response for the user based on the search results. At this time, the tone and expression of the response are adjusted based on the emotion recognized by the emotion recognition engine. For example, it may generate a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[0301] Input: Database search results and sentiment information

[0302] Output: Adjusted answer text

[0303] Step 7: Submit your response

[0304] The server generates an answer and sends it back to the device as an HTTP response. The device receives this data and displays it in a user-friendly format, for example as a text message in a chat interface.

[0305] Input: Adjusted answer text

[0306] Output: The answer message that is displayed to the user

[0307] (Application example 2)

[0308] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] Providing fast and effective customer support in brick-and-mortar stores requires providing immediate and appropriate answers to customer questions. However, currently, responses to questions can be delayed, and it can be difficult to respond in a way that takes customer feelings into consideration. This can lead to lower customer satisfaction and a decline in the quality of service.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0311] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using natural language processing, means for searching a database based on the analyzed data, means for generating an appropriate answer from the search results, means for sending the generated answer to the user, means for analyzing emotions, means for adjusting the answer based on the analyzed emotions, and means for presenting the adjusted answer to the user. This enables quick and emotionally sensitive customer service even in physical stores.

[0312] The "means for receiving data input by a user" refers to a method or device for acquiring information input by a user via a terminal.

[0313] "Means for analyzing received data using natural language processing" refers to a method or device that uses natural language processing technology to understand and analyze received information.

[0314] "Means for searching a database based on analyzed data" refers to a method or device that uses the results of the analysis to find relevant information from a database.

[0315] "Means for generating appropriate answers from search results" refers to a method or device that creates an optimal response based on the search results of a database.

[0316] A "means for transmitting a generated answer to a user" is a method or device for communicating a constructed response to a user.

[0317] "Means for analyzing emotions" refers to methods or devices that identify and interpret user emotions from input data.

[0318] A "means for tailoring a response based on analyzed emotions" is a method or device that optimizes the content or expression of a response based on identified emotions.

[0319] A "means for presenting a tailored response to a user" is a method or device that visually or audibly indicates to a user the tailored response.

[0320] The present invention relates to a "smart customer support" system that streamlines customer support in brick-and-mortar stores. This system helps customer service staff respond to customer questions and feedback in real time through smart glasses worn by the staff.

[0321] System configuration:

[0322] 1. Hardware configuration:

[0323] Smart glasses: worn by wait staff, such as Google Glass.

[0324] Server: A central server that receives, analyzes, searches, and generates answers from data.

[0325] Network connection: An internet connection for data communication between the smart glasses and the server.

[0326] 2. Software used:

[0327] Python: A programming language for building the entire system.

[0328] textblob library: A natural language processing engine.

[0329] transformers library: A sentiment analysis engine.

[0330] Process flow:

[0331] 1. Receiving data from the user:

[0332] Users (customers) can voice their questions or feedback in the store, and the microphone in the smart glasses will capture the voice.

[0333] 2. Data Analysis:

[0334] The smart glasses convert the captured voice into text data and send it to the server, where the server performs natural language processing using the textblob library to extract keywords and analyze the sentiment of the input data using the transformers library.

[0335] 3. Search the database:

[0336] Based on the extracted keywords and emotional information, the server searches the database to find the most suitable answer candidate.

[0337] 4. Generate and refine answers:

[0338] The server then adjusts the answer based on the candidate answers retrieved from the database, taking into account the results of the emotion analysis. If negative emotions are detected, the server will take appropriate measures, such as writing an apology.

[0339] 5. Suggestion of Answer:

[0340] The adjusted answer is displayed on the smart glasses' display, and the wait staff can relay it to the customer.

[0341] Examples:

[0342] For example, if a customer types "Please tell me how to return a product" into the microphone of their smart glasses, the system will act as follows:

[0343] 1. The smart glasses convert the speech into text and send it to the server.

[0344] 2. The server uses a natural language processing engine to extract keywords such as "product," "return," and "procedure."

[0345] 3. The sentiment analysis engine detects negative sentiment in the user's text.

[0346] 4. Search the database to get possible answers such as, "If you bring the product and receipt to your nearest store within 30 days of purchase, we can help you."

[0347] 5. Add "We're sorry for the inconvenience" to your answers based on the negative sentiment detected.

[0348] 6. The adjusted answer is displayed on the smart glasses display and communicated to the customer by the staff.

[0349] Example prompt for a generative AI model:

[0350] Prompt: "What is the procedure for returning an item?"

[0351] Keywords: ["product", "return", "procedure"]

[0352] Emotion: "Negative"

[0353] Response: "Please bring your item and receipt to your nearest store within 30 days of purchase and we will assist you. We apologize for any inconvenience."

[0354] This allows customers to receive prompt and accurate responses, improving customer satisfaction and reducing the burden on customer service staff.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The user speaks into the microphone on the smart glasses.

[0358] Input: Customer voice input

[0359] Output: Audio data

[0360] How it works: Customers speak their questions about the return process into the microphone in the smart glasses, which captures their voice data.

[0361] Step 2:

[0362] The terminal converts the voice data into text data.

[0363] Input: Audio data

[0364] Output: Text data

[0365] What it does: It uses speech recognition software to convert the speech into text, for example, "How do I return a product?"

[0366] Step 3:

[0367] The terminal transmits the text data to the server.

[0368] Input: Text data

[0369] Output: Send request

[0370] Specific behavior: Converts text data into an appropriate format (e.g., HTTP request) and sends it to the server.

[0371] Step 4:

[0372] The server analyzes the text data using a natural language processing engine.

[0373] Input: Text data

[0374] Output: Keywords

[0375] What it does: Uses the textblob library to extract important keywords such as "product," "return," and "procedure" from text data.

[0376] Step 5:

[0377] The server analyzes the text data using a sentiment analysis engine.

[0378] Input: Text data

[0379] Output: Emotional information

[0380] What it does: Detect negative sentiment from text data using the transformers library.

[0381] Step 6:

[0382] The server searches the database based on extracted keywords and emotional information.

[0383] Input: Keywords, emotion information

[0384] Output: Search results

[0385] Specific behavior: Search the database and get an answer such as "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you."

[0386] Step 7:

[0387] The server generates answers based on the search results and adjusts them according to sentiment.

[0388] Input: Search results, emotion information

[0389] Output: Adjusted answer

[0390] What happens: Based on the search results, adjust the response, for example by adding an apology such as "We apologize for the inconvenience."

[0391] Step 8:

[0392] The server sends the adjusted response to the terminal.

[0393] Input: Adjusted answer

[0394] Output: Send request

[0395] Specific operation: The adjusted answer is sent to the terminal in text data format.

[0396] Step 9:

[0397] The device displays the adjusted answers on the smart glasses.

[0398] Input: Adjusted answer

[0399] Output: Display

[0400] What it does: The adjusted answer is displayed on the smart glasses display for the staff to review, with a message such as, "If you bring your product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[0401] Through these steps, you can provide fast and empathetic answers to your customers' questions.

[0402] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0408] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0410] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0413] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0414] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0415] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0417] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0418] The present invention is a system that provides prompt and appropriate answers based on user input data. This system consists of a series of processes that receive a question from a user, search a database using natural language processing technology, generate a relevant answer, and return it to the user.

[0419] Specific Embodiments

[0420] Input from the user

[0421] The user uses the chatbot interface provided on the device to input a question, which can be specific, such as "How do I return a product?"

[0422] Receiving input data

[0423] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0424] Data analysis

[0425] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0426] Searching the database

[0427] The server uses the extracted keywords to search a database containing past inquiries and their answers, and quickly finds the most appropriate answer based on the keywords.

[0428] Generate answers

[0429] Based on the searched information, the server generates the most appropriate answer for the user. For example, if the question is "How do I return a product?", the appropriate answer generated would be "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return."

[0430] Submit your answer

[0431] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0432] Specific examples

[0433] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0434] 1. The user enters a question and the device sends the data to the server.

[0435] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0436] 3. The database is searched and relevant information is retrieved.

[0437] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0438] 5. This response is sent to the terminal and confirmed by the user.

[0439] The system of the present invention allows users to quickly obtain appropriate information without having to manually search for information, thereby significantly improving user convenience.

[0440] The processing flow will be explained below.

[0441] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0442] Step 2: The device sends the entered question data to the chatbot server. Specifically, the data is converted into an appropriate format and sent to the server as an HTTP request.

[0443] Step 3: The server receives the data sent from the device and passes it to the natural language processing engine.

[0444] Step 4: The server's natural language processing engine analyzes the received data. As a result of the analysis, keywords such as "product," "return," and "procedure" are extracted.

[0445] Step 5: The server uses the extracted keywords to search the database, quickly finding relevant information and obtaining search results.

[0446] Step 6: The server generates the most appropriate answer based on the search results. For example, it creates a specific answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0447] Step 7: The server sends the generated response data to the terminal as an HTTP response.

[0448] Step 8: The device analyzes the received response data and displays it in a user-friendly format. The user interface is updated to display the response.

[0449] Step 9: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain the necessary information.

[0450] Example 1

[0451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0452] Conventional systems have had difficulty in providing fast and accurate answers to user input. In particular, the process of properly formatting a user's question, analyzing it, and generating relevant answers is cumbersome, reducing the efficiency of the entire system. The present invention aims to solve these problems and provide a system that provides fast and appropriate answers to questions entered by a user.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0454] In this invention, the server includes means for receiving data input by a user, means for arranging the received data into an appropriate format, means for analyzing the arranged data using a natural language processing engine, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating the most appropriate answer from the search results, means for arranging the generated answer into an appropriate format, and means for sending the generated answer to the user. This automates the generation and provision of answers to user input, making it possible to significantly improve the efficiency of the entire system.

[0455] A "user" is an individual or organization that interacts with the system and enters questions or data to obtain information.

[0456] A "terminal" is an electronic device, such as a computer or smartphone, that a user uses to access the system.

[0457] A "server" is a computer system that receives data input from a user, processes and analyzes the data, and generates and returns an appropriate response to the user.

[0458] "Means for receiving data" refers to a function that executes a process by which the server receives information sent by the user.

[0459] "Formatting means" refers to the processes or algorithms used to convert the raw data received from the user into a form that is easier to analyze.

[0460] A "natural language processing engine" is technology or software that allows a computer to analyze, understand, and generate human language.

[0461] "Keyword extraction" refers to the process of identifying and extracting important words and phrases from the analyzed data.

[0462] "Means for searching the database" refers to the process or algorithm that uses the extracted keywords to find relevant information within the database.

[0463] "Answer generation" refers to the process of constructing the most relevant answer to the user's question based on database search results.

[0464] "Formatting" refers to the process of reconstructing the generated answers into a form that is easy for the user to understand.

[0465] The "means for sending an answer" refers to a process for sending the generated answer to the user's terminal and displaying it.

[0466] A "scoring algorithm" is a computational method for evaluating search results and data and quantifying their relevance and importance.

[0467] The "HTTP request and response format" refers to the process of making a request and a response in a format widely used as a communication protocol between a user and a server.

[0468] The present invention is a system that provides quick and appropriate answers to questions entered by a user. This system receives user input data, analyzes it using natural language processing technology, searches for related information from a database, generates an answer, and returns it to the user. The system of the present invention is realized through multiple processing steps.

[0469] System installation and configuration

[0470] Users use devices such as computers or smartphones, which have an internet browser or dedicated application installed, through which they can access the chatbot interface.

[0471] A server is a computer system running in a cloud or on-premise environment, containing software such as a natural language processing engine (e.g., spaCy, NLTK), a database system (e.g., MySQL, PostgreSQL), etc. The server performs the task of parsing input data received from users and generating appropriate answers.

[0472] Receiving and formatting data

[0473] A user uses the chatbot interface on their device to input a specific question, which the device then formats in an appropriate format (e.g., JSON) and sends as an HTTP request to the server. For example, a user might input, "How do I return an item?" This input data includes the question as well as other metadata such as the user ID and a timestamp.

[0474] Analysis and keyword extraction using natural language processing

[0475] The server receives the HTTP request sent from the device and analyzes the data. A natural language processing engine is used for the analysis, which first extracts sentences from the data, then tokenizes them and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0476] Database Search and Scoring Algorithms

[0477] Based on the extracted keywords, the server searches a database containing past queries and their answers, quickly finding the most relevant answer based on the keywords. If there are multiple search results, a scoring algorithm is used to select the most relevant one.

[0478] Answer generation and formatting

[0479] The server generates the most appropriate answer for the user based on the search results. For example, if the question is "How do I return a product?", the server might generate an answer such as "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return." This answer is then formatted and converted into a user-friendly format.

[0480] Submitting the answer and displaying it to the user

[0481] The generated answer is sent to the terminal again as an HTTP response, and the terminal receives this data and displays it in a format that is easy for the user to understand, allowing the user to quickly obtain an answer to their question.

[0482] Examples and prompts

[0483] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0484] 1. The user enters a question and the device sends the data to the server.

[0485] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0486] 3. The database is searched and relevant information is retrieved.

[0487] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0488] 5. This response is sent to the terminal and confirmed by the user.

[0489] An example of a prompt sentence is, "If a user asks about the return procedure for a product, how would you respond?" This makes it easier to understand the system's behavior in concrete terms.

[0490] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0491] Step 1:

[0492] The user enters a question and the device sends the data to the server. The user enters a question using the chatbot interface on the device. As input, the user enters "Please tell me how to return a product," and the device formats this data into an appropriate format (e.g., JSON format). The formatted data is sent to the server as an HTTP POST request. As output, the user's input data is sent to the server.

[0493] Step 2:

[0494] The server receives the data and parses it using a natural language processing engine. The server receives the data sent as an HTTP request and parses the input data. It receives JSON format data as input and uses a natural language processing engine (e.g. spaCy, NLTK) to tokenize the data and parse the important parts from the sentence. For example, it parses "Please tell me the procedure for returning a product" and extracts the important parts. The parsed token data is obtained as output.

[0495] Step 3:

[0496] The server extracts keywords from the parsed data. The server is responsible for extracting key keywords from the parsed data. It takes as input the token data from the previous step and applies an algorithm to extract key keywords (e.g., "product", "return", "procedure"). The output is a list of keywords.

[0497] Step 4:

[0498] The server searches the database based on the keywords. The server uses the extracted keywords to search for relevant information in the database. It receives a list of keywords as input and performs a database search query. The database stores past queries and their answers, and retrieves the most relevant answer based on the keywords. The search results are obtained as the output.

[0499] Step 5:

[0500] The server generates the best answer. The server analyzes the search results from the database and generates the most appropriate answer for the user. It receives the search results as input, uses a scoring algorithm to select the most relevant answer, and formats it appropriately. For example, the server might generate an answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you." The generated answer is obtained as output.

[0501] Step 6:

[0502] The server sends the generated answer to the terminal, which displays it to the user. The server sends the generated answer to the terminal as an HTTP response. It receives the generated answer data as input, formats it as an HTTP response, and sends it to the terminal. The terminal receives this data and displays it in a format that is easy for the user to understand. The appropriate answer is displayed on the user's screen as output.

[0503] In this way, the system of the present invention can efficiently provide quick and appropriate responses to user input.

[0504] (Application example 1)

[0505] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0506] When customers in physical stores ask about products, how to purchase them, or details about return and exchange procedures, it is difficult to provide them with the right information quickly. This also places a heavy burden on store staff, which could lead to a decline in the quality of customer service.

[0507] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0508] In this invention, the server includes a means for receiving data input by a user, a means for analyzing the received data using natural language processing, a means for searching a database based on the analyzed data, and a means for displaying a generated answer on a smartphone or smart glasses, thereby enabling prompt and appropriate answers to be provided in real time when customers ask questions in a physical store.

[0509] "User" means any person or entity that uses the System to receive information or services.

[0510] "Data" is information such as a question or request that a user provides to the system.

[0511] The "receiving means" refers to a function or device for receiving data input by a user.

[0512] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0513] "Means for analyzing" refers to a function or device for analyzing received data and understanding its meaning and structure.

[0514] A "database" is a collection of information that stores past inquiries and their answers.

[0515] "Searching means" refers to the function or device for searching a database based on the analyzed data and finding relevant information.

[0516] "Means for generating" refers to the functions and devices for generating appropriate answers from search results.

[0517] "Transmission means" refers to a function or device for delivering the generated answer to the user.

[0518] "Displaying means" refers to a function or device for displaying the generated answer on a smartphone or smart glasses.

[0519] "Keywords" are important words or phrases extracted from the analyzed data.

[0520] "Relevant answers" refer to answers that best fit the user's question.

[0521] A "smartphone" is a type of mobile phone that can access the Internet and use applications.

[0522] "Smart glasses" are a wearable device in the form of glasses that are equipped with a display and sensors and provide augmented reality functionality.

[0523] System Program

[0524] A program for implementing the system is generated with the following configuration.

[0525] 1. A means of receiving data entered by the user

[0526] The user uses a smartphone or smart glasses to enter a question, which is sent to the server as an HTTP request.

[0527] 2. A means of analyzing received data using natural language processing

[0528] The server analyzes the received data using the Python library spaCy and Transformers. As a natural language processing task, it understands the structure and meaning of the question and extracts keywords.

[0529] 3. A means of searching the database based on the analyzed data

[0530] Based on the extracted keywords, the server uses an SQL query to search an SQLite database, which stores past queries and their answers.

[0531] 4. A means of sending the generated answer to the user

[0532] The most appropriate answer is generated from the search results and sent back to the user's device as an HTTP response, where the user can view the answer through their smartphone or smart glasses.

[0533] 5. A means of displaying the generated answers on a smartphone or smart glasses

[0534] Submitted responses are properly formatted and displayed on the user's device in a visually friendly format.

[0535] Hardware and software used

[0536] Hardware: iPhone or Android smartphone, smart glasses (e.g., Google Glass).

[0537] Software: Python, Django (web framework), spaCy (natural language processing library), Transformers (NLP model library), SQLite (database).

[0538] Detailed explanation of the process

[0539] 1. User Input

[0540] Example: A user types the question "How do you use this product?" into their smartphone.

[0541] 2. Receiving input data

[0542] The question is sent from the device to the server as an HTTP request.

[0543] 3. Data Analysis

[0544] The server receives the question and analyzes it using spaCy and Transformers. For example, it extracts keywords such as "product" and "use" from the question.

[0545] 4. Database Search

[0546] It searches an SQLite database based on keywords to find the most relevant answer, such as "Please soak this product in 100°C water for 5 minutes before use."

[0547] 5. Generate and submit answers

[0548] The optimal answer is generated and sent to the user's device.

[0549] 6. View Answers

[0550] The answer will be displayed on your smartphone or smart glasses.

[0551] Examples of concrete examples and prompts

[0552] Specific examples

[0553] When a user asks "How do I use this product?" in a physical store, the following happens:

[0554] 1. User input: Type "How would you like to use this product?" into your smartphone.

[0555] 2. Server analysis: Extract keywords for "product" and "use."

[0556] 3. Database search: Search the database for documents on how to use the product.

[0557] 4. Answer generation: Generate specific answers such as "Please soak this product in 100 degree water for 5 minutes before use."

[0558] 5. Display answers: The answers are displayed to the user on their smartphone.

[0559] Prompt Sentence Examples

[0560] User: "How do you use this product?"

[0561] Server: "Please soak this product in 100°C water for 5 minutes before use. Please refer to the instruction manual for details."

[0562] With the above configuration, the present invention dramatically improves customer service in brick-and-mortar stores, making it possible to provide quick and appropriate responses.

[0563] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0564] Step 1:

[0565] The user inputs a question using a smartphone or smart glasses. For example, they input "How do I use this product?" The device sends the input question data to the server as an HTTP request. The input data is sent from the device to the server in the form of a request.

[0566] Step 2:

[0567] The server analyzes the received HTTP request and extracts the question data. The extracted question data is passed to a natural language processing engine (e.g., spaCy or Transformers). The input is the question data, and the output is the analyzed structured data. The natural language processing engine analyzes the meaning and structure of the question and extracts important keywords. For example, the keywords "product" and "use" are extracted.

[0568] Step 3:

[0569] The server searches a database (e.g., SQLite) based on the extracted keywords. The input is the keywords, and the output is a list of related answers. It executes the search query and retrieves the best answer candidates from the database. For example, it might find an answer such as, "Please soak this product in 100°C water for 5 minutes before use."

[0570] Step 4:

[0571] The server generates the most appropriate answer from the search results. The input is a list of search results, and the output is the optimal answer. The most relevant answer is automatically selected and formatted in a user-friendly format using natural language generation technology. For example, a specific answer such as "Please soak this product in 100°C water for 5 minutes before use" is generated.

[0572] Step 5:

[0573] The server sends the generated answer to the terminal as an HTTP response. The input is the generated answer, and the output is the sent HTTP response message. The server performs transmission processing so that the answer reaches the terminal.

[0574] Step 6:

[0575] The device processes the received HTTP response and generates an answer that is displayed on the display of the smartphone or smart glasses. The input is the HTTP response message, and the output is the visually displayed answer. The user can check the answer on the device screen. For example, the answer may read, "Please soak this product in 100°C water for 5 minutes before use."

[0576] Through these processing steps, users can get quick and appropriate answers in real time without having to ask questions directly at a physical store.

[0577] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0578] The present invention is a system that receives user input data, analyzes it using a combination of natural language processing technology and an emotion engine, and provides appropriate answers. The system aims to generate fast and emotionally sensitive responses to user questions and feedback.

[0579] Specific Embodiments

[0580] Input from the user

[0581] The user uses the chatbot interface provided on the device to input a question, for example, "Please tell me how to return a product."

[0582] Receiving input data

[0583] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0584] Data analysis

[0585] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0586] Emotion recognition

[0587] The server's emotion engine recognizes emotions from the user's input data. For example, if the user's text expresses anger or frustration, the emotion is extracted.

[0588] Searching the database

[0589] The server uses the extracted keywords and emotion information to search a database that stores past queries and their answers, and quickly finds the most appropriate answer based on the keywords and emotions.

[0590] Generate and refine answers

[0591] Based on the retrieved information, the server generates the most appropriate answer for the user. Furthermore, the emotion engine adjusts the tone and expression of the answer based on the emotion recognized by the server. For example, if the user expresses dissatisfaction, the answer may include an apology or sympathy.

[0592] Submit your answer

[0593] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0594] Specific examples

[0595] For example, if a user types "Please tell me how to return an item" into a terminal and the input contains an angry emotion, the system will act as follows:

[0596] 1. The user enters a question and the device sends the data to the server.

[0597] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0598] 3. The emotion engine recognizes the user's anger emotion.

[0599] 4. The database is searched and relevant information is retrieved.

[0600] 5. The server generates an emotionally sensitive response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[0601] 6. This response is sent to the terminal and confirmed by the user.

[0602] The system of the present invention saves users the trouble of manually searching for information and enables them to quickly obtain appropriate information. In addition, the incorporation of an emotion engine enables proactive responses that take into account the user's emotions, improving the user experience.

[0603] The processing flow will be explained below.

[0604] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0605] Step 2: The device sends the entered question data to the chatbot server, which converts the data into an appropriate format and sends it to the server as an HTTP request.

[0606] Step 3: The server receives the data sent from the device and passes it to a natural language processing engine for analysis.

[0607] Step 4: The server's natural language processing engine analyzes the received data. During the analysis, important keywords such as "product," "return," and "procedure" are extracted.

[0608] Step 5: The server's emotion engine recognizes emotions from the user's input data. If the user's text indicates anger or frustration, the emotion is extracted.

[0609] Step 6: The server uses the extracted keywords and sentiment information to search the database, quickly find relevant information, and obtain search results.

[0610] Step 7: The server generates an appropriate answer based on the search results retrieved from the database. The generated answer is adjusted based on the emotions recognized by the emotion engine. For example, if the user is dissatisfied, an apology will be added to the answer.

[0611] Step 8: The server generates a response and sends it to the terminal as an HTTP response. The response data is formatted in a user-friendly format.

[0612] Step 9: The device analyzes the received response data and updates the user interface, displaying the appropriate response to the user.

[0613] Step 10: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain an answer to their question.

[0614] Example 2

[0615] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0616] Conventional chatbot systems can sometimes have difficulty generating quick and appropriate answers to user questions. They also lack the functionality to provide answers that take the user's emotions into consideration, resulting in a poor user experience. Furthermore, the process of keyword extraction and emotion recognition, which are necessary to efficiently generate answers for multiple questions, is not automated, limiting the system's response speed and accuracy.

[0617] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0618] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using a natural language processing device, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating an appropriate answer from the search results, means for recognizing emotions from the user's input data, means for adjusting the tone and expression of the answer based on the recognized emotions, and means for sending the generated answer to the user.

[0619] This enables quick and appropriate responses, and by providing answers that take the user's feelings into consideration, it is possible to improve the user experience and increase the system's response speed and accuracy.

[0620] "User-input data" refers to the text data of questions or requests that users enter into the chatbot interface.

[0621] "Means for receiving" refers to the functions or devices that allow the server to receive data sent from the user.

[0622] A "natural language processing system" is a device or software that analyzes text data and understands its meaning and structure.

[0623] "Means for analyzing" refers to the function of analyzing received data using a natural language processing device and extracting meaning and keywords.

[0624] "Keyword extraction means" is the ability to identify and extract important words and phrases from the analyzed data.

[0625] The "means for searching a database" is a function for searching a database for related information based on the extracted keywords.

[0626] "Means for generating appropriate answers" is a function that creates the most appropriate answer to the user's question based on the search results.

[0627] "Means for recognizing emotions" is a function that identifies emotions from the user's text data and extracts those emotions as data.

[0628] "Means for adjusting the tone and expression of responses" refers to a function that appropriately changes the wording and expression of generated responses based on the recognized sentiment.

[0629] "Transmitting means" refers to a function or device for transmitting the generated answer to the user's terminal and displaying it.

[0630] The present invention is a system that receives user input data, analyzes it using a natural language processing system and an emotion recognition engine, and provides appropriate answers. The system aims to generate fast and emotion-sensitive responses to user questions and feedback.

[0631] Hardware and software used

[0632] The system consists of a terminal that receives user input and a server that analyzes the data and generates responses. The terminal provides a chatbot interface using a web browser or mobile application. The server analyzes the data and recognizes emotions using a natural language processing engine (e.g., SpaCy or NLTK) and an emotion recognition engine (e.g., Microsoft Azure's Text Sentiment Analysis API).

[0633] Data processing and calculation methods

[0634] When a user enters a question and presses the send button, the device sends the input data to the server as an HTTP request. The server analyzes the received data using a natural language processing device and extracts important keywords. Next, an emotion recognition engine recognizes the user's emotions from the input data. Based on the extracted keywords and recognized emotion information, the server searches a database to retrieve related information.

[0635] The server then generates the most appropriate answer for the user based on the search results, adjusting the tone and expression of the answer based on the emotions recognized by the emotion recognition engine. Finally, the generated answer is sent to the device as an HTTP response and displayed to the user.

[0636] Specific examples

[0637] For example, if a user types "Please tell me how to return a product" into a terminal, and includes a feeling of dissatisfaction, the system will act as follows:

[0638] 1. The user enters a question into the terminal and presses the send button.

[0639] Input: "Please tell me the procedure for returning an item."

[0640] 2. The terminal sends the input data to the server as an HTTP request.

[0641] Data format: JSON

[0642] Content: {"question": "Please tell me how to return a product"}

[0643] 3. The server analyzes the received data and extracts keywords using a natural language processing device.

[0644] Extracted keywords: "product", "return", "procedure"

[0645] 4. The server's emotion recognition engine recognizes the user's emotion.

[0646] Extracted emotion: "Dissatisfied"

[0647] 5. The server searches the database and retrieves the relevant information.

[0648] SQL query: SELECT FROM responses WHERE keywords LIKE '%product%' AND keywords LIKE '%return%' AND keywords LIKE '%procedure%';

[0649] 6. The server generates an appropriate response and adjusts the expression to take sentiment into consideration.

[0650] Generated response: "If you bring the product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[0651] 7. The server generates an answer and sends it to the device as an HTTP response.

[0652] Data format: JSON

[0653] Content: {"response": "Please bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."}

[0654] 8. The terminal displays the received response to the user.

[0655] Display message: "We can assist you if you bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."

[0656] Prompt Sentence Examples

[0657] Design a system that uses natural language processing and emotion recognition to generate appropriate responses to user inputs such as "Please tell me about the procedure for returning a product." If the user is dissatisfied, the response should include an apology or expression of sympathy.

[0658] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0659] Step 1: Input from the user

[0660] The user uses the provided chatbot interface to enter a question, for example, "How do I return a product?"

[0661] Input: Text data entered by the user into the chatbot interface

[0662] Output: Input data from the user

[0663] Step 2: Receiving input data

[0664] When the user presses the send button, the device sends the input data as an HTTP request to the chatbot server, converting it into an appropriate format (e.g., JSON).

[0665] Input: Data entered by the user

[0666] Output: Data formatted as an HTTP request

[0667] Step 3: Analyze the data

[0668] The server analyzes the received data. First, the server uses a natural language processing system to analyze the text data and extract important keywords. Specifically, it uses an analysis engine (e.g., SpaCy, NLTK) to extract the keywords "product," "return," and "procedure."

[0669] Input: Data received as an HTTP request

[0670] Output: Extracted keywords (e.g., "product", "return", "procedure")

[0671] Step 4: Recognize emotions

[0672] The server's emotion recognition engine recognizes emotions from the user's input data. For example, it uses an emotion analysis engine (e.g., Microsoft Azure's text emotion analysis API) to extract emotions such as "dissatisfaction" or "anger."

[0673] Input: User-entered data

[0674] Output: Extracted emotion (e.g., "dissatisfied," "anger")

[0675] Step 5: Search the database

[0676] The server uses the extracted keywords and sentiment information to search a database that stores past queries and their answers. The server executes SQL queries to find the most relevant answers based on the keywords and sentiment.

[0677] Input: Extracted keywords and sentiment information

[0678] Output: Database search results (e.g. related answer information)

[0679] Step 6: Generate and refine answers

[0680] The server generates the most appropriate response for the user based on the search results. At this time, the tone and expression of the response are adjusted based on the emotion recognized by the emotion recognition engine. For example, it may generate a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[0681] Input: Database search results and sentiment information

[0682] Output: Adjusted answer text

[0683] Step 7: Submit your response

[0684] The server generates an answer and sends it back to the device as an HTTP response. The device receives this data and displays it in a user-friendly format, for example as a text message in a chat interface.

[0685] Input: Adjusted answer text

[0686] Output: The answer message that is displayed to the user

[0687] (Application example 2)

[0688] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] Providing fast and effective customer support in brick-and-mortar stores requires providing immediate and appropriate answers to customer questions. However, currently, responses to questions can be delayed, and it can be difficult to respond in a way that takes customer feelings into consideration. This can lead to lower customer satisfaction and a decline in the quality of service.

[0690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0691] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using natural language processing, means for searching a database based on the analyzed data, means for generating an appropriate answer from the search results, means for sending the generated answer to the user, means for analyzing emotions, means for adjusting the answer based on the analyzed emotions, and means for presenting the adjusted answer to the user. This enables quick and emotionally sensitive customer service even in physical stores.

[0692] The "means for receiving data input by a user" refers to a method or device for acquiring information input by a user via a terminal.

[0693] "Means for analyzing received data using natural language processing" refers to a method or device that uses natural language processing technology to understand and analyze received information.

[0694] "Means for searching a database based on analyzed data" refers to a method or device that uses the results of the analysis to find relevant information from a database.

[0695] "Means for generating appropriate answers from search results" refers to a method or device that creates an optimal response based on the search results of a database.

[0696] A "means for transmitting a generated answer to a user" is a method or device for communicating a constructed response to a user.

[0697] "Means for analyzing emotions" refers to methods or devices that identify and interpret user emotions from input data.

[0698] A "means for tailoring a response based on analyzed emotions" is a method or device that optimizes the content or expression of a response based on identified emotions.

[0699] A "means for presenting a tailored response to a user" is a method or device that visually or audibly indicates to a user the tailored response.

[0700] The present invention relates to a "smart customer support" system that streamlines customer support in brick-and-mortar stores. This system helps customer service staff respond to customer questions and feedback in real time through smart glasses worn by the staff.

[0701] System configuration:

[0702] 1. Hardware configuration:

[0703] Smart glasses: worn by wait staff, such as Google Glass.

[0704] Server: A central server that receives, analyzes, searches, and generates answers from data.

[0705] Network connection: An internet connection for data communication between the smart glasses and the server.

[0706] 2. Software used:

[0707] Python: A programming language for building the entire system.

[0708] textblob library: A natural language processing engine.

[0709] transformers library: A sentiment analysis engine.

[0710] Process flow:

[0711] 1. Receiving data from the user:

[0712] Users (customers) can voice their questions or feedback in the store, and the microphone in the smart glasses will capture the voice.

[0713] 2. Data Analysis:

[0714] The smart glasses convert the captured voice into text data and send it to the server, where the server performs natural language processing using the textblob library to extract keywords and analyze the sentiment of the input data using the transformers library.

[0715] 3. Search the database:

[0716] Based on the extracted keywords and emotional information, the server searches the database to find the most suitable answer candidate.

[0717] 4. Generate and refine answers:

[0718] The server then adjusts the answer based on the candidate answers retrieved from the database, taking into account the results of the emotion analysis. If negative emotions are detected, the server will take appropriate measures, such as writing an apology.

[0719] 5. Suggestion of Answer:

[0720] The adjusted answer is displayed on the smart glasses' display, and the wait staff can relay it to the customer.

[0721] Examples:

[0722] For example, if a customer types "Please tell me how to return a product" into the microphone of their smart glasses, the system will act as follows:

[0723] 1. The smart glasses convert the speech into text and send it to the server.

[0724] 2. The server uses a natural language processing engine to extract keywords such as "product," "return," and "procedure."

[0725] 3. The sentiment analysis engine detects negative sentiment in the user's text.

[0726] 4. Search the database to get possible answers such as, "If you bring the product and receipt to your nearest store within 30 days of purchase, we can help you."

[0727] 5. Add "We're sorry for the inconvenience" to your answers based on the negative sentiment detected.

[0728] 6. The adjusted answer is displayed on the smart glasses display and communicated to the customer by the staff.

[0729] Example prompt for a generative AI model:

[0730] Prompt: "What is the procedure for returning an item?"

[0731] Keywords: ["product", "return", "procedure"]

[0732] Emotion: "Negative"

[0733] Response: "Please bring your item and receipt to your nearest store within 30 days of purchase and we will assist you. We apologize for any inconvenience."

[0734] This allows customers to receive prompt and accurate responses, improving customer satisfaction and reducing the burden on customer service staff.

[0735] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0736] Step 1:

[0737] The user speaks into the microphone on the smart glasses.

[0738] Input: Customer voice input

[0739] Output: Audio data

[0740] How it works: Customers speak their questions about the return process into the microphone in the smart glasses, which captures their voice data.

[0741] Step 2:

[0742] The terminal converts the voice data into text data.

[0743] Input: Audio data

[0744] Output: Text data

[0745] What it does: It uses speech recognition software to convert the speech into text, for example, "How do I return a product?"

[0746] Step 3:

[0747] The terminal transmits the text data to the server.

[0748] Input: Text data

[0749] Output: Send request

[0750] Specific behavior: Converts text data into an appropriate format (e.g., HTTP request) and sends it to the server.

[0751] Step 4:

[0752] The server analyzes the text data using a natural language processing engine.

[0753] Input: Text data

[0754] Output: Keywords

[0755] What it does: Uses the textblob library to extract important keywords such as "product," "return," and "procedure" from text data.

[0756] Step 5:

[0757] The server analyzes the text data using a sentiment analysis engine.

[0758] Input: Text data

[0759] Output: Emotional information

[0760] What it does: Detect negative sentiment from text data using the transformers library.

[0761] Step 6:

[0762] The server searches the database based on extracted keywords and emotional information.

[0763] Input: Keywords, emotion information

[0764] Output: Search results

[0765] Specific behavior: Search the database and get an answer such as "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you."

[0766] Step 7:

[0767] The server generates answers based on the search results and adjusts them according to sentiment.

[0768] Input: Search results, emotion information

[0769] Output: Adjusted answer

[0770] What happens: Based on the search results, adjust the response, for example by adding an apology such as "We apologize for the inconvenience."

[0771] Step 8:

[0772] The server sends the adjusted response to the terminal.

[0773] Input: Adjusted answer

[0774] Output: Send request

[0775] Specific operation: The adjusted answer is sent to the terminal in text data format.

[0776] Step 9:

[0777] The device displays the adjusted answers on the smart glasses.

[0778] Input: Adjusted answer

[0779] Output: Display

[0780] What it does: The adjusted answer is displayed on the smart glasses display for the staff to review, with a message such as, "If you bring your product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[0781] Through these steps, you can provide fast and empathetic answers to your customers' questions.

[0782] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0783] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0784] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0785] [Third embodiment]

[0786] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0787] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0788] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0789] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0790] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0791] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0792] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0793] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0794] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0795] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0796] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0797] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0798] The present invention is a system that provides prompt and appropriate answers based on user input data. This system consists of a series of processes that receive a question from a user, search a database using natural language processing technology, generate a relevant answer, and return it to the user.

[0799] Specific Embodiments

[0800] Input from the user

[0801] The user uses the chatbot interface provided on the device to input a question, which can be specific, such as "How do I return a product?"

[0802] Receiving input data

[0803] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0804] Data analysis

[0805] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0806] Searching the database

[0807] The server uses the extracted keywords to search a database containing past inquiries and their answers, and quickly finds the most appropriate answer based on the keywords.

[0808] Generate answers

[0809] Based on the searched information, the server generates the most appropriate answer for the user. For example, if the question is "How do I return a product?", the appropriate answer generated would be "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return."

[0810] Submit your answer

[0811] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0812] Specific examples

[0813] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0814] 1. The user enters a question and the device sends the data to the server.

[0815] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0816] 3. The database is searched and relevant information is retrieved.

[0817] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0818] 5. This response is sent to the terminal and confirmed by the user.

[0819] The system of the present invention allows users to quickly obtain appropriate information without having to manually search for information, thereby significantly improving user convenience.

[0820] The processing flow will be explained below.

[0821] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0822] Step 2: The device sends the entered question data to the chatbot server. Specifically, the data is converted into an appropriate format and sent to the server as an HTTP request.

[0823] Step 3: The server receives the data sent from the device and passes it to the natural language processing engine.

[0824] Step 4: The server's natural language processing engine analyzes the received data. As a result of the analysis, keywords such as "product," "return," and "procedure" are extracted.

[0825] Step 5: The server uses the extracted keywords to search the database, quickly finding relevant information and obtaining search results.

[0826] Step 6: The server generates the most appropriate answer based on the search results. For example, it creates a specific answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0827] Step 7: The server sends the generated response data to the terminal as an HTTP response.

[0828] Step 8: The device analyzes the received response data and displays it in a user-friendly format. The user interface is updated to display the response.

[0829] Step 9: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain the necessary information.

[0830] Example 1

[0831] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0832] Conventional systems have had difficulty in providing fast and accurate answers to user input. In particular, the process of properly formatting a user's question, analyzing it, and generating relevant answers is cumbersome, reducing the efficiency of the entire system. The present invention aims to solve these problems and provide a system that provides fast and appropriate answers to questions entered by a user.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0834] In this invention, the server includes means for receiving data input by a user, means for arranging the received data into an appropriate format, means for analyzing the arranged data using a natural language processing engine, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating the most appropriate answer from the search results, means for arranging the generated answer into an appropriate format, and means for sending the generated answer to the user. This automates the generation and provision of answers to user input, making it possible to significantly improve the efficiency of the entire system.

[0835] A "user" is an individual or organization that interacts with the system and enters questions or data to obtain information.

[0836] A "terminal" is an electronic device, such as a computer or smartphone, that a user uses to access the system.

[0837] A "server" is a computer system that receives data input from a user, processes and analyzes the data, and generates and returns an appropriate response to the user.

[0838] "Means for receiving data" refers to a function that executes a process by which the server receives information sent by the user.

[0839] "Formatting means" refers to the processes or algorithms used to convert the raw data received from the user into a form that is easier to analyze.

[0840] A "natural language processing engine" is technology or software that allows a computer to analyze, understand, and generate human language.

[0841] "Keyword extraction" refers to the process of identifying and extracting important words and phrases from the analyzed data.

[0842] "Means for searching the database" refers to the process or algorithm that uses the extracted keywords to find relevant information within the database.

[0843] "Answer generation" refers to the process of constructing the most relevant answer to the user's question based on database search results.

[0844] "Formatting" refers to the process of reconstructing the generated answers into a form that is easy for the user to understand.

[0845] The "means for sending an answer" refers to a process for sending the generated answer to the user's terminal and displaying it.

[0846] A "scoring algorithm" is a computational method for evaluating search results and data and quantifying their relevance and importance.

[0847] The "HTTP request and response format" refers to the process of making a request and a response in a format widely used as a communication protocol between a user and a server.

[0848] The present invention is a system that provides quick and appropriate answers to questions entered by a user. This system receives user input data, analyzes it using natural language processing technology, searches for related information from a database, generates an answer, and returns it to the user. The system of the present invention is realized through multiple processing steps.

[0849] System installation and configuration

[0850] Users use devices such as computers or smartphones, which have an internet browser or dedicated application installed, through which they can access the chatbot interface.

[0851] A server is a computer system running in a cloud or on-premise environment, containing software such as a natural language processing engine (e.g., spaCy, NLTK), a database system (e.g., MySQL, PostgreSQL), etc. The server performs the task of parsing input data received from users and generating appropriate answers.

[0852] Receiving and formatting data

[0853] A user uses the chatbot interface on their device to input a specific question, which the device then formats in an appropriate format (e.g., JSON) and sends as an HTTP request to the server. For example, a user might input, "How do I return an item?" This input data includes the question as well as other metadata such as the user ID and a timestamp.

[0854] Analysis and keyword extraction using natural language processing

[0855] The server receives the HTTP request sent from the device and analyzes the data. A natural language processing engine is used for the analysis, which first extracts sentences from the data, then tokenizes them and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0856] Database Search and Scoring Algorithms

[0857] Based on the extracted keywords, the server searches a database containing past queries and their answers, quickly finding the most relevant answer based on the keywords. If there are multiple search results, a scoring algorithm is used to select the most relevant one.

[0858] Answer generation and formatting

[0859] The server generates the most appropriate answer for the user based on the search results. For example, if the question is "How do I return a product?", the server might generate an answer such as "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return." This answer is then formatted and converted into a user-friendly format.

[0860] Submitting the answer and displaying it to the user

[0861] The generated answer is sent to the terminal again as an HTTP response, and the terminal receives this data and displays it in a format that is easy for the user to understand, allowing the user to quickly obtain an answer to their question.

[0862] Examples and prompts

[0863] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[0864] 1. The user enters a question and the device sends the data to the server.

[0865] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0866] 3. The database is searched and relevant information is retrieved.

[0867] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[0868] 5. This response is sent to the terminal and confirmed by the user.

[0869] An example of a prompt sentence is, "If a user asks about the return procedure for a product, how would you respond?" This makes it easier to understand the system's behavior in concrete terms.

[0870] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0871] Step 1:

[0872] The user enters a question and the device sends the data to the server. The user enters a question using the chatbot interface on the device. As input, the user enters "Please tell me how to return a product," and the device formats this data into an appropriate format (e.g., JSON format). The formatted data is sent to the server as an HTTP POST request. As output, the user's input data is sent to the server.

[0873] Step 2:

[0874] The server receives the data and parses it using a natural language processing engine. The server receives the data sent as an HTTP request and parses the input data. It receives JSON format data as input and uses a natural language processing engine (e.g. spaCy, NLTK) to tokenize the data and parse the important parts from the sentence. For example, it parses "Please tell me the procedure for returning a product" and extracts the important parts. The parsed token data is obtained as output.

[0875] Step 3:

[0876] The server extracts keywords from the parsed data. The server is responsible for extracting key keywords from the parsed data. It takes as input the token data from the previous step and applies an algorithm to extract key keywords (e.g., "product", "return", "procedure"). The output is a list of keywords.

[0877] Step 4:

[0878] The server searches the database based on the keywords. The server uses the extracted keywords to search for relevant information in the database. It receives a list of keywords as input and performs a database search query. The database stores past queries and their answers, and retrieves the most relevant answer based on the keywords. The search results are obtained as the output.

[0879] Step 5:

[0880] The server generates the best answer. The server analyzes the search results from the database and generates the most appropriate answer for the user. It receives the search results as input, uses a scoring algorithm to select the most relevant answer, and formats it appropriately. For example, the server might generate an answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you." The generated answer is obtained as output.

[0881] Step 6:

[0882] The server sends the generated answer to the terminal, which displays it to the user. The server sends the generated answer to the terminal as an HTTP response. It receives the generated answer data as input, formats it as an HTTP response, and sends it to the terminal. The terminal receives this data and displays it in a format that is easy for the user to understand. The appropriate answer is displayed on the user's screen as output.

[0883] In this way, the system of the present invention can efficiently provide quick and appropriate responses to user input.

[0884] (Application example 1)

[0885] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0886] When customers in physical stores ask about products, how to purchase them, or details about return and exchange procedures, it is difficult to provide them with the right information quickly. This also places a heavy burden on store staff, which could lead to a decline in the quality of customer service.

[0887] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0888] In this invention, the server includes a means for receiving data input by a user, a means for analyzing the received data using natural language processing, a means for searching a database based on the analyzed data, and a means for displaying a generated answer on a smartphone or smart glasses, thereby enabling prompt and appropriate answers to be provided in real time when customers ask questions in a physical store.

[0889] "User" means any person or entity that uses the System to receive information or services.

[0890] "Data" is information such as a question or request that a user provides to the system.

[0891] The "receiving means" refers to a function or device for receiving data input by a user.

[0892] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0893] "Means for analyzing" refers to a function or device for analyzing received data and understanding its meaning and structure.

[0894] A "database" is a collection of information that stores past inquiries and their answers.

[0895] "Searching means" refers to the function or device for searching a database based on the analyzed data and finding relevant information.

[0896] "Means for generating" refers to the functions and devices for generating appropriate answers from search results.

[0897] "Transmission means" refers to a function or device for delivering the generated answer to the user.

[0898] "Displaying means" refers to a function or device for displaying the generated answer on a smartphone or smart glasses.

[0899] "Keywords" are important words or phrases extracted from the analyzed data.

[0900] "Relevant answers" refer to answers that best fit the user's question.

[0901] A "smartphone" is a type of mobile phone that can access the Internet and use applications.

[0902] "Smart glasses" are a wearable device in the form of glasses that are equipped with a display and sensors and provide augmented reality functionality.

[0903] System Program

[0904] A program for implementing the system is generated with the following configuration.

[0905] 1. A means of receiving data entered by the user

[0906] The user uses a smartphone or smart glasses to enter a question, which is sent to the server as an HTTP request.

[0907] 2. A means of analyzing received data using natural language processing

[0908] The server analyzes the received data using the Python library spaCy and Transformers. As a natural language processing task, it understands the structure and meaning of the question and extracts keywords.

[0909] 3. A means of searching the database based on the analyzed data

[0910] Based on the extracted keywords, the server uses an SQL query to search an SQLite database, which stores past queries and their answers.

[0911] 4. A means of sending the generated answer to the user

[0912] The most appropriate answer is generated from the search results and sent back to the user's device as an HTTP response, where the user can view the answer through their smartphone or smart glasses.

[0913] 5. A means of displaying the generated answers on a smartphone or smart glasses

[0914] Submitted responses are properly formatted and displayed on the user's device in a visually friendly format.

[0915] Hardware and software used

[0916] Hardware: iPhone or Android smartphone, smart glasses (e.g., Google Glass).

[0917] Software: Python, Django (web framework), spaCy (natural language processing library), Transformers (NLP model library), SQLite (database).

[0918] Detailed explanation of the process

[0919] 1. User Input

[0920] Example: A user types the question "How do you use this product?" into their smartphone.

[0921] 2. Receiving input data

[0922] The question is sent from the device to the server as an HTTP request.

[0923] 3. Data Analysis

[0924] The server receives the question and analyzes it using spaCy and Transformers. For example, it extracts keywords such as "product" and "use" from the question.

[0925] 4. Database Search

[0926] It searches an SQLite database based on keywords to find the most relevant answer, such as "Please soak this product in 100°C water for 5 minutes before use."

[0927] 5. Generate and submit answers

[0928] The optimal answer is generated and sent to the user's device.

[0929] 6. View Answers

[0930] The answer will be displayed on your smartphone or smart glasses.

[0931] Examples of concrete examples and prompts

[0932] Specific examples

[0933] When a user asks "How do I use this product?" in a physical store, the following happens:

[0934] 1. User input: Type "How would you like to use this product?" into your smartphone.

[0935] 2. Server analysis: Extract keywords for "product" and "use."

[0936] 3. Database search: Search the database for documents on how to use the product.

[0937] 4. Answer generation: Generate specific answers such as "Please soak this product in 100 degree water for 5 minutes before use."

[0938] 5. Display answers: The answers are displayed to the user on their smartphone.

[0939] Prompt Sentence Examples

[0940] User: "How do you use this product?"

[0941] Server: "Please soak this product in 100°C water for 5 minutes before use. Please refer to the instruction manual for details."

[0942] With the above configuration, the present invention dramatically improves customer service in brick-and-mortar stores, making it possible to provide quick and appropriate responses.

[0943] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0944] Step 1:

[0945] The user inputs a question using a smartphone or smart glasses. For example, they input "How do I use this product?" The device sends the input question data to the server as an HTTP request. The input data is sent from the device to the server in the form of a request.

[0946] Step 2:

[0947] The server analyzes the received HTTP request and extracts the question data. The extracted question data is passed to a natural language processing engine (e.g., spaCy or Transformers). The input is the question data, and the output is the analyzed structured data. The natural language processing engine analyzes the meaning and structure of the question and extracts important keywords. For example, the keywords "product" and "use" are extracted.

[0948] Step 3:

[0949] The server searches a database (e.g., SQLite) based on the extracted keywords. The input is the keywords, and the output is a list of related answers. It executes the search query and retrieves the best answer candidates from the database. For example, it might find an answer such as, "Please soak this product in 100°C water for 5 minutes before use."

[0950] Step 4:

[0951] The server generates the most appropriate answer from the search results. The input is a list of search results, and the output is the optimal answer. The most relevant answer is automatically selected and formatted in a user-friendly format using natural language generation technology. For example, a specific answer such as "Please soak this product in 100°C water for 5 minutes before use" is generated.

[0952] Step 5:

[0953] The server sends the generated answer to the terminal as an HTTP response. The input is the generated answer, and the output is the sent HTTP response message. The server performs transmission processing so that the answer reaches the terminal.

[0954] Step 6:

[0955] The device processes the received HTTP response and generates an answer that is displayed on the display of the smartphone or smart glasses. The input is the HTTP response message, and the output is the visually displayed answer. The user can check the answer on the device screen. For example, the answer may read, "Please soak this product in 100°C water for 5 minutes before use."

[0956] Through these processing steps, users can get quick and appropriate answers in real time without having to ask questions directly at a physical store.

[0957] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0958] The present invention is a system that receives user input data, analyzes it using a combination of natural language processing technology and an emotion engine, and provides appropriate answers. The system aims to generate fast and emotionally sensitive responses to user questions and feedback.

[0959] Specific Embodiments

[0960] Input from the user

[0961] The user uses the chatbot interface provided on the device to input a question, for example, "Please tell me how to return a product."

[0962] Receiving input data

[0963] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[0964] Data analysis

[0965] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[0966] Emotion recognition

[0967] The server's emotion engine recognizes emotions from the user's input data. For example, if the user's text expresses anger or frustration, the emotion is extracted.

[0968] Searching the database

[0969] The server uses the extracted keywords and emotion information to search a database that stores past queries and their answers, and quickly finds the most appropriate answer based on the keywords and emotions.

[0970] Generate and refine answers

[0971] Based on the retrieved information, the server generates the most appropriate answer for the user. Furthermore, the emotion engine adjusts the tone and expression of the answer based on the emotion recognized by the server. For example, if the user expresses dissatisfaction, the answer may include an apology or sympathy.

[0972] Submit your answer

[0973] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[0974] Specific examples

[0975] For example, if a user types "Please tell me how to return an item" into a terminal and the input contains an angry emotion, the system will act as follows:

[0976] 1. The user enters a question and the device sends the data to the server.

[0977] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[0978] 3. The emotion engine recognizes the user's anger emotion.

[0979] 4. The database is searched and relevant information is retrieved.

[0980] 5. The server generates an emotionally sensitive response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[0981] 6. This response is sent to the terminal and confirmed by the user.

[0982] The system of the present invention saves users the trouble of manually searching for information and enables them to quickly obtain appropriate information. In addition, the incorporation of an emotion engine enables proactive responses that take into account the user's emotions, improving the user experience.

[0983] The processing flow will be explained below.

[0984] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[0985] Step 2: The device sends the entered question data to the chatbot server, which converts the data into an appropriate format and sends it to the server as an HTTP request.

[0986] Step 3: The server receives the data sent from the device and passes it to a natural language processing engine for analysis.

[0987] Step 4: The server's natural language processing engine analyzes the received data. During the analysis, important keywords such as "product," "return," and "procedure" are extracted.

[0988] Step 5: The server's emotion engine recognizes emotions from the user's input data. If the user's text indicates anger or frustration, the emotion is extracted.

[0989] Step 6: The server uses the extracted keywords and sentiment information to search the database, quickly find relevant information, and obtain search results.

[0990] Step 7: The server generates an appropriate answer based on the search results retrieved from the database. The generated answer is adjusted based on the emotions recognized by the emotion engine. For example, if the user is dissatisfied, an apology will be added to the answer.

[0991] Step 8: The server generates a response and sends it to the terminal as an HTTP response. The response data is formatted in a user-friendly format.

[0992] Step 9: The device analyzes the received response data and updates the user interface, displaying the appropriate response to the user.

[0993] Step 10: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain an answer to their question.

[0994] Example 2

[0995] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0996] Conventional chatbot systems can sometimes have difficulty generating quick and appropriate answers to user questions. They also lack the functionality to provide answers that take the user's emotions into consideration, resulting in a poor user experience. Furthermore, the process of keyword extraction and emotion recognition, which are necessary to efficiently generate answers for multiple questions, is not automated, limiting the system's response speed and accuracy.

[0997] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0998] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using a natural language processing device, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating an appropriate answer from the search results, means for recognizing emotions from the user's input data, means for adjusting the tone and expression of the answer based on the recognized emotions, and means for sending the generated answer to the user.

[0999] This enables quick and appropriate responses, and by providing answers that take the user's feelings into consideration, it is possible to improve the user experience and increase the system's response speed and accuracy.

[1000] "User-input data" refers to the text data of questions or requests that users enter into the chatbot interface.

[1001] "Means for receiving" refers to the functions or devices that allow the server to receive data sent from the user.

[1002] A "natural language processing system" is a device or software that analyzes text data and understands its meaning and structure.

[1003] "Means for analyzing" refers to the function of analyzing received data using a natural language processing device and extracting meaning and keywords.

[1004] "Keyword extraction means" is the ability to identify and extract important words and phrases from the analyzed data.

[1005] The "means for searching a database" is a function for searching a database for related information based on the extracted keywords.

[1006] "Means for generating appropriate answers" is a function that creates the most appropriate answer to the user's question based on the search results.

[1007] "Means for recognizing emotions" is a function that identifies emotions from the user's text data and extracts those emotions as data.

[1008] "Means for adjusting the tone and expression of responses" refers to a function that appropriately changes the wording and expression of generated responses based on the recognized sentiment.

[1009] "Transmitting means" refers to a function or device for transmitting the generated answer to the user's terminal and displaying it.

[1010] The present invention is a system that receives user input data, analyzes it using a natural language processing system and an emotion recognition engine, and provides appropriate answers. The system aims to generate fast and emotion-sensitive responses to user questions and feedback.

[1011] Hardware and software used

[1012] The system consists of a terminal that receives user input and a server that analyzes the data and generates responses. The terminal provides a chatbot interface using a web browser or mobile application. The server analyzes the data and recognizes emotions using a natural language processing engine (e.g., SpaCy or NLTK) and an emotion recognition engine (e.g., Microsoft Azure's Text Sentiment Analysis API).

[1013] Data processing and calculation methods

[1014] When a user enters a question and presses the send button, the device sends the input data to the server as an HTTP request. The server analyzes the received data using a natural language processing device and extracts important keywords. Next, an emotion recognition engine recognizes the user's emotions from the input data. Based on the extracted keywords and recognized emotion information, the server searches a database to retrieve related information.

[1015] The server then generates the most appropriate answer for the user based on the search results, adjusting the tone and expression of the answer based on the emotions recognized by the emotion recognition engine. Finally, the generated answer is sent to the device as an HTTP response and displayed to the user.

[1016] Specific examples

[1017] For example, if a user types "Please tell me how to return a product" into a terminal, and includes a feeling of dissatisfaction, the system will act as follows:

[1018] 1. The user enters a question into the terminal and presses the send button.

[1019] Input: "Please tell me the procedure for returning an item."

[1020] 2. The terminal sends the input data to the server as an HTTP request.

[1021] Data format: JSON

[1022] Content: {"question": "Please tell me how to return a product"}

[1023] 3. The server analyzes the received data and extracts keywords using a natural language processing device.

[1024] Extracted keywords: "product", "return", "procedure"

[1025] 4. The server's emotion recognition engine recognizes the user's emotion.

[1026] Extracted emotion: "Dissatisfied"

[1027] 5. The server searches the database and retrieves the relevant information.

[1028] SQL query: SELECT FROM responses WHERE keywords LIKE '%product%' AND keywords LIKE '%return%' AND keywords LIKE '%procedure%';

[1029] 6. The server generates an appropriate response and adjusts the expression to take sentiment into consideration.

[1030] Generated response: "If you bring the product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[1031] 7. The server generates an answer and sends it to the device as an HTTP response.

[1032] Data format: JSON

[1033] Content: {"response": "Please bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."}

[1034] 8. The terminal displays the received response to the user.

[1035] Display message: "We can assist you if you bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."

[1036] Prompt Sentence Examples

[1037] Design a system that uses natural language processing and emotion recognition to generate appropriate responses to user inputs such as "Please tell me about the procedure for returning a product." If the user is dissatisfied, the response should include an apology or expression of sympathy.

[1038] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1039] Step 1: Input from the user

[1040] The user uses the provided chatbot interface to enter a question, for example, "How do I return a product?"

[1041] Input: Text data entered by the user into the chatbot interface

[1042] Output: Input data from the user

[1043] Step 2: Receiving input data

[1044] When the user presses the send button, the device sends the input data as an HTTP request to the chatbot server, converting it into an appropriate format (e.g., JSON).

[1045] Input: Data entered by the user

[1046] Output: Data formatted as an HTTP request

[1047] Step 3: Analyze the data

[1048] The server analyzes the received data. First, the server uses a natural language processing system to analyze the text data and extract important keywords. Specifically, it uses an analysis engine (e.g., SpaCy, NLTK) to extract the keywords "product," "return," and "procedure."

[1049] Input: Data received as an HTTP request

[1050] Output: Extracted keywords (e.g., "product", "return", "procedure")

[1051] Step 4: Recognize emotions

[1052] The server's emotion recognition engine recognizes emotions from the user's input data. For example, it uses an emotion analysis engine (e.g., Microsoft Azure's text emotion analysis API) to extract emotions such as "dissatisfaction" or "anger."

[1053] Input: User-entered data

[1054] Output: Extracted emotion (e.g., "dissatisfied," "anger")

[1055] Step 5: Search the database

[1056] The server uses the extracted keywords and sentiment information to search a database that stores past queries and their answers. The server executes SQL queries to find the most relevant answers based on the keywords and sentiment.

[1057] Input: Extracted keywords and sentiment information

[1058] Output: Database search results (e.g. related answer information)

[1059] Step 6: Generate and refine answers

[1060] The server generates the most appropriate response for the user based on the search results. At this time, the tone and expression of the response are adjusted based on the emotion recognized by the emotion recognition engine. For example, it may generate a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[1061] Input: Database search results and sentiment information

[1062] Output: Adjusted answer text

[1063] Step 7: Submit your response

[1064] The server generates an answer and sends it back to the device as an HTTP response. The device receives this data and displays it in a user-friendly format, for example as a text message in a chat interface.

[1065] Input: Adjusted answer text

[1066] Output: The answer message that is displayed to the user

[1067] (Application example 2)

[1068] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1069] Providing fast and effective customer support in brick-and-mortar stores requires providing immediate and appropriate answers to customer questions. However, currently, responses to questions can be delayed, and it can be difficult to respond in a way that takes customer feelings into consideration. This can lead to lower customer satisfaction and a decline in the quality of service.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1071] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using natural language processing, means for searching a database based on the analyzed data, means for generating an appropriate answer from the search results, means for sending the generated answer to the user, means for analyzing emotions, means for adjusting the answer based on the analyzed emotions, and means for presenting the adjusted answer to the user. This enables quick and emotionally sensitive customer service even in physical stores.

[1072] The "means for receiving data input by a user" refers to a method or device for acquiring information input by a user via a terminal.

[1073] "Means for analyzing received data using natural language processing" refers to a method or device that uses natural language processing technology to understand and analyze received information.

[1074] "Means for searching a database based on analyzed data" refers to a method or device that uses the results of the analysis to find relevant information from a database.

[1075] "Means for generating appropriate answers from search results" refers to a method or device that creates an optimal response based on the search results of a database.

[1076] A "means for transmitting a generated answer to a user" is a method or device for communicating a constructed response to a user.

[1077] "Means for analyzing emotions" refers to methods or devices that identify and interpret user emotions from input data.

[1078] A "means for tailoring a response based on analyzed emotions" is a method or device that optimizes the content or expression of a response based on identified emotions.

[1079] A "means for presenting a tailored response to a user" is a method or device that visually or audibly indicates to a user the tailored response.

[1080] The present invention relates to a "smart customer support" system that streamlines customer support in brick-and-mortar stores. This system helps customer service staff respond to customer questions and feedback in real time through smart glasses worn by the staff.

[1081] System configuration:

[1082] 1. Hardware configuration:

[1083] Smart glasses: worn by wait staff, such as Google Glass.

[1084] Server: A central server that receives, analyzes, searches, and generates answers from data.

[1085] Network connection: An internet connection for data communication between the smart glasses and the server.

[1086] 2. Software used:

[1087] Python: A programming language for building the entire system.

[1088] textblob library: A natural language processing engine.

[1089] transformers library: A sentiment analysis engine.

[1090] Process flow:

[1091] 1. Receiving data from the user:

[1092] Users (customers) can voice their questions or feedback in the store, and the microphone in the smart glasses will capture the voice.

[1093] 2. Data Analysis:

[1094] The smart glasses convert the captured voice into text data and send it to the server, where the server performs natural language processing using the textblob library to extract keywords and analyze the sentiment of the input data using the transformers library.

[1095] 3. Search the database:

[1096] Based on the extracted keywords and emotional information, the server searches the database to find the most suitable answer candidate.

[1097] 4. Generate and refine answers:

[1098] The server then adjusts the answer based on the candidate answers retrieved from the database, taking into account the results of the emotion analysis. If negative emotions are detected, the server will take appropriate measures, such as writing an apology.

[1099] 5. Suggestion of Answer:

[1100] The adjusted answer is displayed on the smart glasses' display, and the wait staff can relay it to the customer.

[1101] Examples:

[1102] For example, if a customer types "Please tell me how to return a product" into the microphone of their smart glasses, the system will act as follows:

[1103] 1. The smart glasses convert the speech into text and send it to the server.

[1104] 2. The server uses a natural language processing engine to extract keywords such as "product," "return," and "procedure."

[1105] 3. The sentiment analysis engine detects negative sentiment in the user's text.

[1106] 4. Search the database to get possible answers such as, "If you bring the product and receipt to your nearest store within 30 days of purchase, we can help you."

[1107] 5. Add "We're sorry for the inconvenience" to your answers based on the negative sentiment detected.

[1108] 6. The adjusted answer is displayed on the smart glasses display and communicated to the customer by the staff.

[1109] Example prompt for a generative AI model:

[1110] Prompt: "What is the procedure for returning an item?"

[1111] Keywords: ["product", "return", "procedure"]

[1112] Emotion: "Negative"

[1113] Response: "Please bring your item and receipt to your nearest store within 30 days of purchase and we will assist you. We apologize for any inconvenience."

[1114] This allows customers to receive prompt and accurate responses, improving customer satisfaction and reducing the burden on customer service staff.

[1115] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1116] Step 1:

[1117] The user speaks into the microphone on the smart glasses.

[1118] Input: Customer voice input

[1119] Output: Audio data

[1120] How it works: Customers speak their questions about the return process into the microphone in the smart glasses, which captures their voice data.

[1121] Step 2:

[1122] The terminal converts the voice data into text data.

[1123] Input: Audio data

[1124] Output: Text data

[1125] What it does: It uses speech recognition software to convert the speech into text, for example, "How do I return a product?"

[1126] Step 3:

[1127] The terminal transmits the text data to the server.

[1128] Input: Text data

[1129] Output: Send request

[1130] Specific behavior: Converts text data into an appropriate format (e.g., HTTP request) and sends it to the server.

[1131] Step 4:

[1132] The server analyzes the text data using a natural language processing engine.

[1133] Input: Text data

[1134] Output: Keywords

[1135] What it does: Uses the textblob library to extract important keywords such as "product," "return," and "procedure" from text data.

[1136] Step 5:

[1137] The server analyzes the text data using a sentiment analysis engine.

[1138] Input: Text data

[1139] Output: Emotional information

[1140] What it does: Detect negative sentiment from text data using the transformers library.

[1141] Step 6:

[1142] The server searches the database based on extracted keywords and emotional information.

[1143] Input: Keywords, emotion information

[1144] Output: Search results

[1145] Specific behavior: Search the database and get an answer such as "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you."

[1146] Step 7:

[1147] The server generates answers based on the search results and adjusts them according to sentiment.

[1148] Input: Search results, emotion information

[1149] Output: Adjusted answer

[1150] What happens: Based on the search results, adjust the response, for example by adding an apology such as "We apologize for the inconvenience."

[1151] Step 8:

[1152] The server sends the adjusted response to the terminal.

[1153] Input: Adjusted answer

[1154] Output: Send request

[1155] Specific operation: The adjusted answer is sent to the terminal in text data format.

[1156] Step 9:

[1157] The device displays the adjusted answers on the smart glasses.

[1158] Input: Adjusted answer

[1159] Output: Display

[1160] What it does: The adjusted answer is displayed on the smart glasses display for the staff to review, with a message such as, "If you bring your product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[1161] Through these steps, you can provide fast and empathetic answers to your customers' questions.

[1162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1163] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1164] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1165] [Fourth embodiment]

[1166] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1167] 7, a 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.

[1168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1170] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1173] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1175] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1177] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1178] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1179] The present invention is a system that provides prompt and appropriate answers based on user input data. This system consists of a series of processes that receive a question from a user, search a database using natural language processing technology, generate a relevant answer, and return it to the user.

[1180] Specific Embodiments

[1181] Input from the user

[1182] The user uses the chatbot interface provided on the device to input a question, which can be specific, such as "How do I return a product?"

[1183] Receiving input data

[1184] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[1185] Data analysis

[1186] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[1187] Searching the database

[1188] The server uses the extracted keywords to search a database containing past inquiries and their answers, and quickly finds the most appropriate answer based on the keywords.

[1189] Generate answers

[1190] Based on the searched information, the server generates the most appropriate answer for the user. For example, if the question is "How do I return a product?", the appropriate answer generated would be "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return."

[1191] Submit your answer

[1192] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[1193] Specific examples

[1194] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[1195] 1. The user enters a question and the device sends the data to the server.

[1196] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[1197] 3. The database is searched and relevant information is retrieved.

[1198] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[1199] 5. This response is sent to the terminal and confirmed by the user.

[1200] The system of the present invention allows users to quickly obtain appropriate information without having to manually search for information, thereby significantly improving user convenience.

[1201] The processing flow will be explained below.

[1202] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[1203] Step 2: The device sends the entered question data to the chatbot server. Specifically, the data is converted into an appropriate format and sent to the server as an HTTP request.

[1204] Step 3: The server receives the data sent from the device and passes it to the natural language processing engine.

[1205] Step 4: The server's natural language processing engine analyzes the received data. As a result of the analysis, keywords such as "product," "return," and "procedure" are extracted.

[1206] Step 5: The server uses the extracted keywords to search the database, quickly finding relevant information and obtaining search results.

[1207] Step 6: The server generates the most appropriate answer based on the search results. For example, it creates a specific answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[1208] Step 7: The server sends the generated response data to the terminal as an HTTP response.

[1209] Step 8: The device analyzes the received response data and displays it in a user-friendly format. The user interface is updated to display the response.

[1210] Step 9: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain the necessary information.

[1211] Example 1

[1212] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1213] Conventional systems have had difficulty in providing fast and accurate answers to user input. In particular, the process of properly formatting a user's question, analyzing it, and generating relevant answers is cumbersome, reducing the efficiency of the entire system. The present invention aims to solve these problems and provide a system that provides fast and appropriate answers to questions entered by a user.

[1214] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1215] In this invention, the server includes means for receiving data input by a user, means for arranging the received data into an appropriate format, means for analyzing the arranged data using a natural language processing engine, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating the most appropriate answer from the search results, means for arranging the generated answer into an appropriate format, and means for sending the generated answer to the user. This automates the generation and provision of answers to user input, making it possible to significantly improve the efficiency of the entire system.

[1216] A "user" is an individual or organization that interacts with the system and enters questions or data to obtain information.

[1217] A "terminal" is an electronic device, such as a computer or smartphone, that a user uses to access the system.

[1218] A "server" is a computer system that receives data input from a user, processes and analyzes the data, and generates and returns an appropriate response to the user.

[1219] "Means for receiving data" refers to a function that executes a process by which the server receives information sent by the user.

[1220] "Formatting means" refers to the processes or algorithms used to convert the raw data received from the user into a form that is easier to analyze.

[1221] A "natural language processing engine" is technology or software that allows a computer to analyze, understand, and generate human language.

[1222] "Keyword extraction" refers to the process of identifying and extracting important words and phrases from the analyzed data.

[1223] "Means for searching the database" refers to the process or algorithm that uses the extracted keywords to find relevant information within the database.

[1224] "Answer generation" refers to the process of constructing the most relevant answer to the user's question based on database search results.

[1225] "Formatting" refers to the process of reconstructing the generated answers into a form that is easy for the user to understand.

[1226] The "means for sending an answer" refers to a process for sending the generated answer to the user's terminal and displaying it.

[1227] A "scoring algorithm" is a computational method for evaluating search results and data and quantifying their relevance and importance.

[1228] The "HTTP request and response format" refers to the process of making a request and a response in a format widely used as a communication protocol between a user and a server.

[1229] The present invention is a system that provides quick and appropriate answers to questions entered by a user. This system receives user input data, analyzes it using natural language processing technology, searches for related information from a database, generates an answer, and returns it to the user. The system of the present invention is realized through multiple processing steps.

[1230] System installation and configuration

[1231] Users use devices such as computers or smartphones, which have an internet browser or dedicated application installed, through which they can access the chatbot interface.

[1232] A server is a computer system running in a cloud or on-premise environment, containing software such as a natural language processing engine (e.g., spaCy, NLTK), a database system (e.g., MySQL, PostgreSQL), etc. The server performs the task of parsing input data received from users and generating appropriate answers.

[1233] Receiving and formatting data

[1234] A user uses the chatbot interface on their device to input a specific question, which the device then formats in an appropriate format (e.g., JSON) and sends as an HTTP request to the server. For example, a user might input, "How do I return an item?" This input data includes the question as well as other metadata such as the user ID and a timestamp.

[1235] Analysis and keyword extraction using natural language processing

[1236] The server receives the HTTP request sent from the device and analyzes the data. A natural language processing engine is used for the analysis, which first extracts sentences from the data, then tokenizes them and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[1237] Database Search and Scoring Algorithms

[1238] Based on the extracted keywords, the server searches a database containing past queries and their answers, quickly finding the most relevant answer based on the keywords. If there are multiple search results, a scoring algorithm is used to select the most relevant one.

[1239] Answer generation and formatting

[1240] The server generates the most appropriate answer for the user based on the search results. For example, if the question is "How do I return a product?", the server might generate an answer such as "If you bring the product and receipt to your nearest store within 30 days of purchase, we can process your return." This answer is then formatted and converted into a user-friendly format.

[1241] Submitting the answer and displaying it to the user

[1242] The generated answer is sent to the terminal again as an HTTP response, and the terminal receives this data and displays it in a format that is easy for the user to understand, allowing the user to quickly obtain an answer to their question.

[1243] Examples and prompts

[1244] For example, if a user types "How do I return an item?" into a terminal, the system will do the following:

[1245] 1. The user enters a question and the device sends the data to the server.

[1246] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[1247] 3. The database is searched and relevant information is retrieved.

[1248] 4. The server generates a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you."

[1249] 5. This response is sent to the terminal and confirmed by the user.

[1250] An example of a prompt sentence is, "If a user asks about the return procedure for a product, how would you respond?" This makes it easier to understand the system's behavior in concrete terms.

[1251] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1252] Step 1:

[1253] The user enters a question and the device sends the data to the server. The user enters a question using the chatbot interface on the device. As input, the user enters "Please tell me how to return a product," and the device formats this data into an appropriate format (e.g., JSON format). The formatted data is sent to the server as an HTTP POST request. As output, the user's input data is sent to the server.

[1254] Step 2:

[1255] The server receives the data and parses it using a natural language processing engine. The server receives the data sent as an HTTP request and parses the input data. It receives JSON format data as input and uses a natural language processing engine (e.g. spaCy, NLTK) to tokenize the data and parse the important parts from the sentence. For example, it parses "Please tell me the procedure for returning a product" and extracts the important parts. The parsed token data is obtained as output.

[1256] Step 3:

[1257] The server extracts keywords from the parsed data. The server is responsible for extracting key keywords from the parsed data. It takes as input the token data from the previous step and applies an algorithm to extract key keywords (e.g., "product", "return", "procedure"). The output is a list of keywords.

[1258] Step 4:

[1259] The server searches the database based on the keywords. The server uses the extracted keywords to search for relevant information in the database. It receives a list of keywords as input and performs a database search query. The database stores past queries and their answers, and retrieves the most relevant answer based on the keywords. The search results are obtained as the output.

[1260] Step 5:

[1261] The server generates the best answer. The server analyzes the search results from the database and generates the most appropriate answer for the user. It receives the search results as input, uses a scoring algorithm to select the most relevant answer, and formats it appropriately. For example, the server might generate an answer such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you." The generated answer is obtained as output.

[1262] Step 6:

[1263] The server sends the generated answer to the terminal, which displays it to the user. The server sends the generated answer to the terminal as an HTTP response. It receives the generated answer data as input, formats it as an HTTP response, and sends it to the terminal. The terminal receives this data and displays it in a format that is easy for the user to understand. The appropriate answer is displayed on the user's screen as output.

[1264] In this way, the system of the present invention can efficiently provide quick and appropriate responses to user input.

[1265] (Application example 1)

[1266] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1267] When customers in physical stores ask about products, how to purchase them, or details about return and exchange procedures, it is difficult to provide them with the right information quickly. This also places a heavy burden on store staff, which could lead to a decline in the quality of customer service.

[1268] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1269] In this invention, the server includes a means for receiving data input by a user, a means for analyzing the received data using natural language processing, a means for searching a database based on the analyzed data, and a means for displaying a generated answer on a smartphone or smart glasses, thereby enabling prompt and appropriate answers to be provided in real time when customers ask questions in a physical store.

[1270] "User" means any person or entity that uses the System to receive information or services.

[1271] "Data" is information such as a question or request that a user provides to the system.

[1272] The "receiving means" refers to a function or device for receiving data input by a user.

[1273] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1274] "Means for analyzing" refers to a function or device for analyzing received data and understanding its meaning and structure.

[1275] A "database" is a collection of information that stores past inquiries and their answers.

[1276] "Searching means" refers to the function or device for searching a database based on the analyzed data and finding relevant information.

[1277] "Means for generating" refers to the functions and devices for generating appropriate answers from search results.

[1278] "Transmission means" refers to a function or device for delivering the generated answer to the user.

[1279] "Displaying means" refers to a function or device for displaying the generated answer on a smartphone or smart glasses.

[1280] "Keywords" are important words or phrases extracted from the analyzed data.

[1281] "Relevant answers" refer to answers that best fit the user's question.

[1282] A "smartphone" is a type of mobile phone that can access the Internet and use applications.

[1283] "Smart glasses" are a wearable device in the form of glasses that are equipped with a display and sensors and provide augmented reality functionality.

[1284] System Program

[1285] A program for implementing the system is generated with the following configuration.

[1286] 1. A means of receiving data entered by the user

[1287] The user uses a smartphone or smart glasses to enter a question, which is sent to the server as an HTTP request.

[1288] 2. A means of analyzing received data using natural language processing

[1289] The server analyzes the received data using the Python library spaCy and Transformers. As a natural language processing task, it understands the structure and meaning of the question and extracts keywords.

[1290] 3. A means of searching the database based on the analyzed data

[1291] Based on the extracted keywords, the server uses an SQL query to search an SQLite database, which stores past queries and their answers.

[1292] 4. A means of sending the generated answer to the user

[1293] The most appropriate answer is generated from the search results and sent back to the user's device as an HTTP response, where the user can view the answer through their smartphone or smart glasses.

[1294] 5. A means of displaying the generated answers on a smartphone or smart glasses

[1295] Submitted responses are properly formatted and displayed on the user's device in a visually friendly format.

[1296] Hardware and software used

[1297] Hardware: iPhone or Android smartphone, smart glasses (e.g., Google Glass).

[1298] Software: Python, Django (web framework), spaCy (natural language processing library), Transformers (NLP model library), SQLite (database).

[1299] Detailed explanation of the process

[1300] 1. User Input

[1301] Example: A user types the question "How do you use this product?" into their smartphone.

[1302] 2. Receiving input data

[1303] The question is sent from the device to the server as an HTTP request.

[1304] 3. Data Analysis

[1305] The server receives the question and analyzes it using spaCy and Transformers. For example, it extracts keywords such as "product" and "use" from the question.

[1306] 4. Database Search

[1307] It searches an SQLite database based on keywords to find the most relevant answer, such as "Please soak this product in 100°C water for 5 minutes before use."

[1308] 5. Generate and submit answers

[1309] The optimal answer is generated and sent to the user's device.

[1310] 6. View Answers

[1311] The answer will be displayed on your smartphone or smart glasses.

[1312] Examples of concrete examples and prompts

[1313] Specific examples

[1314] When a user asks "How do I use this product?" in a physical store, the following happens:

[1315] 1. User input: Type "How would you like to use this product?" into your smartphone.

[1316] 2. Server analysis: Extract keywords for "product" and "use."

[1317] 3. Database search: Search the database for documents on how to use the product.

[1318] 4. Answer generation: Generate specific answers such as "Please soak this product in 100 degree water for 5 minutes before use."

[1319] 5. Display answers: The answers are displayed to the user on their smartphone.

[1320] Prompt Sentence Examples

[1321] User: "How do you use this product?"

[1322] Server: "Please soak this product in 100°C water for 5 minutes before use. Please refer to the instruction manual for details."

[1323] With the above configuration, the present invention dramatically improves customer service in brick-and-mortar stores, making it possible to provide quick and appropriate responses.

[1324] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1325] Step 1:

[1326] The user inputs a question using a smartphone or smart glasses. For example, they input "How do I use this product?" The device sends the input question data to the server as an HTTP request. The input data is sent from the device to the server in the form of a request.

[1327] Step 2:

[1328] The server analyzes the received HTTP request and extracts the question data. The extracted question data is passed to a natural language processing engine (e.g., spaCy or Transformers). The input is the question data, and the output is the analyzed structured data. The natural language processing engine analyzes the meaning and structure of the question and extracts important keywords. For example, the keywords "product" and "use" are extracted.

[1329] Step 3:

[1330] The server searches a database (e.g., SQLite) based on the extracted keywords. The input is the keywords, and the output is a list of related answers. It executes the search query and retrieves the best answer candidates from the database. For example, it might find an answer such as, "Please soak this product in 100°C water for 5 minutes before use."

[1331] Step 4:

[1332] The server generates the most appropriate answer from the search results. The input is a list of search results, and the output is the optimal answer. The most relevant answer is automatically selected and formatted in a user-friendly format using natural language generation technology. For example, a specific answer such as "Please soak this product in 100°C water for 5 minutes before use" is generated.

[1333] Step 5:

[1334] The server sends the generated answer to the terminal as an HTTP response. The input is the generated answer, and the output is the sent HTTP response message. The server performs transmission processing so that the answer reaches the terminal.

[1335] Step 6:

[1336] The device processes the received HTTP response and generates an answer that is displayed on the display of the smartphone or smart glasses. The input is the HTTP response message, and the output is the visually displayed answer. The user can check the answer on the device screen. For example, the answer may read, "Please soak this product in 100°C water for 5 minutes before use."

[1337] Through these processing steps, users can get quick and appropriate answers in real time without having to ask questions directly at a physical store.

[1338] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1339] The present invention is a system that receives user input data, analyzes it using a combination of natural language processing technology and an emotion engine, and provides appropriate answers. The system aims to generate fast and emotionally sensitive responses to user questions and feedback.

[1340] Specific Embodiments

[1341] Input from the user

[1342] The user uses the chatbot interface provided on the device to input a question, for example, "Please tell me how to return a product."

[1343] Receiving input data

[1344] When a user submits a question, the device sends the input data to the chatbot server, where it is formatted appropriately and sent as an HTTP request.

[1345] Data analysis

[1346] The server analyzes the received data. First, a natural language processing engine analyzes the data and extracts important keywords. For example, keywords such as "product," "return," and "procedure" are extracted.

[1347] Emotion recognition

[1348] The server's emotion engine recognizes emotions from the user's input data. For example, if the user's text expresses anger or frustration, the emotion is extracted.

[1349] Searching the database

[1350] The server uses the extracted keywords and emotion information to search a database that stores past queries and their answers, and quickly finds the most appropriate answer based on the keywords and emotions.

[1351] Generate and refine answers

[1352] Based on the retrieved information, the server generates the most appropriate answer for the user. Furthermore, the emotion engine adjusts the tone and expression of the answer based on the emotion recognized by the server. For example, if the user expresses dissatisfaction, the answer may include an apology or sympathy.

[1353] Submit your answer

[1354] The answer generated by the server is sent to the terminal as an HTTP response. The terminal receives this data and displays it in a format that is easy for the user to understand. This allows the user to efficiently obtain an answer to their question.

[1355] Specific examples

[1356] For example, if a user types "Please tell me how to return an item" into a terminal and the input contains an angry emotion, the system will act as follows:

[1357] 1. The user enters a question and the device sends the data to the server.

[1358] 2. The server analyzes the data and extracts the keywords "product," "return," and "procedure."

[1359] 3. The emotion engine recognizes the user's anger emotion.

[1360] 4. The database is searched and relevant information is retrieved.

[1361] 5. The server generates an emotionally sensitive response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[1362] 6. This response is sent to the terminal and confirmed by the user.

[1363] The system of the present invention saves users the trouble of manually searching for information and enables them to quickly obtain appropriate information. In addition, the incorporation of an emotion engine enables proactive responses that take into account the user's emotions, improving the user experience.

[1364] The processing flow will be explained below.

[1365] Step 1: A user opens the chatbot on their device and types a question. For example, "Please tell me how to return a product."

[1366] Step 2: The device sends the entered question data to the chatbot server, which converts the data into an appropriate format and sends it to the server as an HTTP request.

[1367] Step 3: The server receives the data sent from the device and passes it to a natural language processing engine for analysis.

[1368] Step 4: The server's natural language processing engine analyzes the received data. During the analysis, important keywords such as "product," "return," and "procedure" are extracted.

[1369] Step 5: The server's emotion engine recognizes emotions from the user's input data. If the user's text indicates anger or frustration, the emotion is extracted.

[1370] Step 6: The server uses the extracted keywords and sentiment information to search the database, quickly find relevant information, and obtain search results.

[1371] Step 7: The server generates an appropriate answer based on the search results retrieved from the database. The generated answer is adjusted based on the emotions recognized by the emotion engine. For example, if the user is dissatisfied, an apology will be added to the answer.

[1372] Step 8: The server generates a response and sends it to the terminal as an HTTP response. The response data is formatted in a user-friendly format.

[1373] Step 9: The device analyzes the received response data and updates the user interface, displaying the appropriate response to the user.

[1374] Step 10: The user checks the answer displayed on the terminal, allowing the user to efficiently obtain an answer to their question.

[1375] Example 2

[1376] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1377] Conventional chatbot systems can sometimes have difficulty generating quick and appropriate answers to user questions. They also lack the functionality to provide answers that take the user's emotions into consideration, resulting in a poor user experience. Furthermore, the process of keyword extraction and emotion recognition, which are necessary to efficiently generate answers for multiple questions, is not automated, limiting the system's response speed and accuracy.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1379] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using a natural language processing device, means for extracting keywords from the analyzed data, means for searching a database based on the extracted keywords, means for generating an appropriate answer from the search results, means for recognizing emotions from the user's input data, means for adjusting the tone and expression of the answer based on the recognized emotions, and means for sending the generated answer to the user.

[1380] This enables quick and appropriate responses, and by providing answers that take the user's feelings into consideration, it is possible to improve the user experience and increase the system's response speed and accuracy.

[1381] "User-input data" refers to the text data of questions or requests that users enter into the chatbot interface.

[1382] "Means for receiving" refers to the functions or devices that allow the server to receive data sent from the user.

[1383] A "natural language processing system" is a device or software that analyzes text data and understands its meaning and structure.

[1384] "Means for analyzing" refers to the function of analyzing received data using a natural language processing device and extracting meaning and keywords.

[1385] "Keyword extraction means" is the ability to identify and extract important words and phrases from the analyzed data.

[1386] The "means for searching a database" is a function for searching a database for related information based on the extracted keywords.

[1387] "Means for generating appropriate answers" is a function that creates the most appropriate answer to the user's question based on the search results.

[1388] "Means for recognizing emotions" is a function that identifies emotions from the user's text data and extracts those emotions as data.

[1389] "Means for adjusting the tone and expression of responses" refers to a function that appropriately changes the wording and expression of generated responses based on the recognized sentiment.

[1390] "Transmitting means" refers to a function or device for transmitting the generated answer to the user's terminal and displaying it.

[1391] The present invention is a system that receives user input data, analyzes it using a natural language processing system and an emotion recognition engine, and provides appropriate answers. The system aims to generate fast and emotion-sensitive responses to user questions and feedback.

[1392] Hardware and software used

[1393] The system consists of a terminal that receives user input and a server that analyzes the data and generates responses. The terminal provides a chatbot interface using a web browser or mobile application. The server analyzes the data and recognizes emotions using a natural language processing engine (e.g., SpaCy or NLTK) and an emotion recognition engine (e.g., Microsoft Azure's Text Sentiment Analysis API).

[1394] Data processing and calculation methods

[1395] When a user enters a question and presses the send button, the device sends the input data to the server as an HTTP request. The server analyzes the received data using a natural language processing device and extracts important keywords. Next, an emotion recognition engine recognizes the user's emotions from the input data. Based on the extracted keywords and recognized emotion information, the server searches a database to retrieve related information.

[1396] The server then generates the most appropriate answer for the user based on the search results, adjusting the tone and expression of the answer based on the emotions recognized by the emotion recognition engine. Finally, the generated answer is sent to the device as an HTTP response and displayed to the user.

[1397] Specific examples

[1398] For example, if a user types "Please tell me how to return a product" into a terminal, and includes a feeling of dissatisfaction, the system will act as follows:

[1399] 1. The user enters a question into the terminal and presses the send button.

[1400] Input: "Please tell me the procedure for returning an item."

[1401] 2. The terminal sends the input data to the server as an HTTP request.

[1402] Data format: JSON

[1403] Content: {"question": "Please tell me how to return a product"}

[1404] 3. The server analyzes the received data and extracts keywords using a natural language processing device.

[1405] Extracted keywords: "product", "return", "procedure"

[1406] 4. The server's emotion recognition engine recognizes the user's emotion.

[1407] Extracted emotion: "Dissatisfied"

[1408] 5. The server searches the database and retrieves the relevant information.

[1409] SQL query: SELECT FROM responses WHERE keywords LIKE '%product%' AND keywords LIKE '%return%' AND keywords LIKE '%procedure%';

[1410] 6. The server generates an appropriate response and adjusts the expression to take sentiment into consideration.

[1411] Generated response: "If you bring the product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[1412] 7. The server generates an answer and sends it to the device as an HTTP response.

[1413] Data format: JSON

[1414] Content: {"response": "Please bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."}

[1415] 8. The terminal displays the received response to the user.

[1416] Display message: "We can assist you if you bring the product and receipt to your nearest store within 30 days of purchase. We apologize for any inconvenience."

[1417] Prompt Sentence Examples

[1418] Design a system that uses natural language processing and emotion recognition to generate appropriate responses to user inputs such as "Please tell me about the procedure for returning a product." If the user is dissatisfied, the response should include an apology or expression of sympathy.

[1419] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1420] Step 1: Input from the user

[1421] The user uses the provided chatbot interface to enter a question, for example, "How do I return a product?"

[1422] Input: Text data entered by the user into the chatbot interface

[1423] Output: Input data from the user

[1424] Step 2: Receiving input data

[1425] When the user presses the send button, the device sends the input data as an HTTP request to the chatbot server, converting it into an appropriate format (e.g., JSON).

[1426] Input: Data entered by the user

[1427] Output: Data formatted as an HTTP request

[1428] Step 3: Analyze the data

[1429] The server analyzes the received data. First, the server uses a natural language processing system to analyze the text data and extract important keywords. Specifically, it uses an analysis engine (e.g., SpaCy, NLTK) to extract the keywords "product," "return," and "procedure."

[1430] Input: Data received as an HTTP request

[1431] Output: Extracted keywords (e.g., "product", "return", "procedure")

[1432] Step 4: Recognize emotions

[1433] The server's emotion recognition engine recognizes emotions from the user's input data. For example, it uses an emotion analysis engine (e.g., Microsoft Azure's text emotion analysis API) to extract emotions such as "dissatisfaction" or "anger."

[1434] Input: User-entered data

[1435] Output: Extracted emotion (e.g., "dissatisfied," "anger")

[1436] Step 5: Search the database

[1437] The server uses the extracted keywords and sentiment information to search a database that stores past queries and their answers. The server executes SQL queries to find the most relevant answers based on the keywords and sentiment.

[1438] Input: Extracted keywords and sentiment information

[1439] Output: Database search results (e.g. related answer information)

[1440] Step 6: Generate and refine answers

[1441] The server generates the most appropriate response for the user based on the search results. At this time, the tone and expression of the response are adjusted based on the emotion recognized by the emotion recognition engine. For example, it may generate a response such as, "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can assist you. We apologize for any inconvenience."

[1442] Input: Database search results and sentiment information

[1443] Output: Adjusted answer text

[1444] Step 7: Submit your response

[1445] The server generates an answer and sends it back to the device as an HTTP response. The device receives this data and displays it in a user-friendly format, for example as a text message in a chat interface.

[1446] Input: Adjusted answer text

[1447] Output: The answer message that is displayed to the user

[1448] (Application example 2)

[1449] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1450] Providing fast and effective customer support in brick-and-mortar stores requires providing immediate and appropriate answers to customer questions. However, currently, responses to questions can be delayed, and it can be difficult to respond in a way that takes customer feelings into consideration. This can lead to lower customer satisfaction and a decline in the quality of service.

[1451] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1452] In this invention, the server includes means for receiving data input by a user, means for analyzing the received data using natural language processing, means for searching a database based on the analyzed data, means for generating an appropriate answer from the search results, means for sending the generated answer to the user, means for analyzing emotions, means for adjusting the answer based on the analyzed emotions, and means for presenting the adjusted answer to the user. This enables quick and emotionally sensitive customer service even in physical stores.

[1453] The "means for receiving data input by a user" refers to a method or device for acquiring information input by a user via a terminal.

[1454] "Means for analyzing received data using natural language processing" refers to a method or device that uses natural language processing technology to understand and analyze received information.

[1455] "Means for searching a database based on analyzed data" refers to a method or device that uses the results of the analysis to find relevant information from a database.

[1456] "Means for generating appropriate answers from search results" refers to a method or device that creates an optimal response based on the search results of a database.

[1457] A "means for transmitting a generated answer to a user" is a method or device for communicating a constructed response to a user.

[1458] "Means for analyzing emotions" refers to methods or devices that identify and interpret user emotions from input data.

[1459] A "means for tailoring a response based on analyzed emotions" is a method or device that optimizes the content or expression of a response based on identified emotions.

[1460] A "means for presenting a tailored response to a user" is a method or device that visually or audibly indicates to a user the tailored response.

[1461] The present invention relates to a "smart customer support" system that streamlines customer support in brick-and-mortar stores. This system helps customer service staff respond to customer questions and feedback in real time through smart glasses worn by the staff.

[1462] System configuration:

[1463] 1. Hardware configuration:

[1464] Smart glasses: worn by wait staff, such as Google Glass.

[1465] Server: A central server that receives, analyzes, searches, and generates answers from data.

[1466] Network connection: An internet connection for data communication between the smart glasses and the server.

[1467] 2. Software used:

[1468] Python: A programming language for building the entire system.

[1469] textblob library: A natural language processing engine.

[1470] transformers library: A sentiment analysis engine.

[1471] Process flow:

[1472] 1. Receiving data from the user:

[1473] Users (customers) can voice their questions or feedback in the store, and the microphone in the smart glasses will capture the voice.

[1474] 2. Data Analysis:

[1475] The smart glasses convert the captured voice into text data and send it to the server, where the server performs natural language processing using the textblob library to extract keywords and analyze the sentiment of the input data using the transformers library.

[1476] 3. Search the database:

[1477] Based on the extracted keywords and emotional information, the server searches the database to find the most suitable answer candidate.

[1478] 4. Generate and refine answers:

[1479] The server then adjusts the answer based on the candidate answers retrieved from the database, taking into account the results of the emotion analysis. If negative emotions are detected, the server will take appropriate measures, such as writing an apology.

[1480] 5. Suggestion of Answer:

[1481] The adjusted answer is displayed on the smart glasses' display, and the wait staff can relay it to the customer.

[1482] Examples:

[1483] For example, if a customer types "Please tell me how to return a product" into the microphone of their smart glasses, the system will act as follows:

[1484] 1. The smart glasses convert the speech into text and send it to the server.

[1485] 2. The server uses a natural language processing engine to extract keywords such as "product," "return," and "procedure."

[1486] 3. The sentiment analysis engine detects negative sentiment in the user's text.

[1487] 4. Search the database to get possible answers such as, "If you bring the product and receipt to your nearest store within 30 days of purchase, we can help you."

[1488] 5. Add "We're sorry for the inconvenience" to your answers based on the negative sentiment detected.

[1489] 6. The adjusted answer is displayed on the smart glasses display and communicated to the customer by the staff.

[1490] Example prompt for a generative AI model:

[1491] Prompt: "What is the procedure for returning an item?"

[1492] Keywords: ["product", "return", "procedure"]

[1493] Emotion: "Negative"

[1494] Response: "Please bring your item and receipt to your nearest store within 30 days of purchase and we will assist you. We apologize for any inconvenience."

[1495] This allows customers to receive prompt and accurate responses, improving customer satisfaction and reducing the burden on customer service staff.

[1496] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1497] Step 1:

[1498] The user speaks into the microphone on the smart glasses.

[1499] Input: Customer voice input

[1500] Output: Audio data

[1501] How it works: Customers speak their questions about the return process into the microphone in the smart glasses, which captures their voice data.

[1502] Step 2:

[1503] The terminal converts the voice data into text data.

[1504] Input: Audio data

[1505] Output: Text data

[1506] What it does: It uses speech recognition software to convert the speech into text, for example, "How do I return a product?"

[1507] Step 3:

[1508] The terminal transmits the text data to the server.

[1509] Input: Text data

[1510] Output: Send request

[1511] Specific behavior: Converts text data into an appropriate format (e.g., HTTP request) and sends it to the server.

[1512] Step 4:

[1513] The server analyzes the text data using a natural language processing engine.

[1514] Input: Text data

[1515] Output: Keywords

[1516] What it does: Uses the textblob library to extract important keywords such as "product," "return," and "procedure" from text data.

[1517] Step 5:

[1518] The server analyzes the text data using a sentiment analysis engine.

[1519] Input: Text data

[1520] Output: Emotional information

[1521] What it does: Detect negative sentiment from text data using the transformers library.

[1522] Step 6:

[1523] The server searches the database based on extracted keywords and emotional information.

[1524] Input: Keywords, emotion information

[1525] Output: Search results

[1526] Specific behavior: Search the database and get an answer such as "If you bring the product and receipt to your nearest store within 30 days of the purchase date, we can help you."

[1527] Step 7:

[1528] The server generates answers based on the search results and adjusts them according to sentiment.

[1529] Input: Search results, emotion information

[1530] Output: Adjusted answer

[1531] What happens: Based on the search results, adjust the response, for example by adding an apology such as "We apologize for the inconvenience."

[1532] Step 8:

[1533] The server sends the adjusted response to the terminal.

[1534] Input: Adjusted answer

[1535] Output: Send request

[1536] Specific operation: The adjusted answer is sent to the terminal in text data format.

[1537] Step 9:

[1538] The device displays the adjusted answers on the smart glasses.

[1539] Input: Adjusted answer

[1540] Output: Display

[1541] What it does: The adjusted answer is displayed on the smart glasses display for the staff to review, with a message such as, "If you bring your product and receipt to your nearest store within 30 days of purchase, we can assist you. We apologize for any inconvenience."

[1542] Through these steps, you can provide fast and empathetic answers to your customers' questions.

[1543] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1544] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1545] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1546] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1547] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1548] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1549] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1550] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1551] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1552] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1553] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1554] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1555] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1556] 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.

[1557] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1558] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1559] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1560] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1561] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1562] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1563] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1564] The following is further disclosed regarding the above embodiment.

[1565] (Claim 1)

[1566] means for receiving data input from a user;

[1567] means for analyzing the received data by natural language processing;

[1568] means for searching a database based on the analyzed data;

[1569] a means for generating relevant answers from the search results;

[1570] means for transmitting the generated answer to the user;

[1571] A system including:

[1572] (Claim 2)

[1573] 10. The system of claim 1, further comprising: means for extracting keywords from the analyzed data.

[1574] (Claim 3)

[1575] 10. The system of claim 1, further comprising means for selecting a most relevant answer from the search results.

[1576] "Example 1"

[1577] (Claim 1)

[1578] means for receiving data input from a user;

[1579] means for properly formatting the received data;

[1580] A means for analyzing the prepared data using a natural language processing engine;

[1581] means for extracting keywords from the analyzed data;

[1582] a means for searching a database based on the extracted keywords;

[1583] A means for generating the most relevant answer from the search results;

[1584] a means for formatting the generated answers into an appropriate format;

[1585] means for transmitting the generated answer to the user;

[1586] A system including:

[1587] (Claim 2)

[1588] 10. The system of claim 1, further comprising means for selecting the most relevant answers from the search results using a scoring algorithm.

[1589] (Claim 3)

[1590] 10. The system of claim 1, further comprising means for processing in the form of HTTP requests and responses.

[1591] "Application Example 1"

[1592] (Claim 1)

[1593] means for receiving data input from a user;

[1594] means for analyzing the received data by natural language processing;

[1595] means for searching a database based on the analyzed data;

[1596] a means for generating relevant answers from the search results;

[1597] means for transmitting the generated answer to the user;

[1598] means for displaying the generated answers on a smartphone or smart glasses;

[1599] A system including:

[1600] (Claim 2)

[1601] 10. The system of claim 1, further comprising: means for extracting keywords from the analyzed data.

[1602] (Claim 3)

[1603] 10. The system of claim 1, further comprising means for selecting a most relevant answer from the search results.

[1604] "Example 2: Combining Emotion Engines"

[1605] (Claim 1)

[1606] means for receiving data input from a user;

[1607] means for analyzing the received data by a natural language processing device;

[1608] means for extracting keywords from the analyzed data;

[1609] a means for searching a database based on the extracted keywords;

[1610] a means for generating relevant answers from the search results;

[1611] means for recognizing emotions from user input data;

[1612] a means of adjusting the tone and expression of responses based on perceived emotions;

[1613] means for transmitting the generated answer to the user;

[1614] A system including:

[1615] (Claim 2)

[1616] 10. The system of claim 1, wherein the natural language processing device is a text analysis device.

[1617] (Claim 3)

[1618] 10. The system of claim 1, wherein the device for recognizing emotions comprises a sentiment analyzer.

[1619] "Application example 2 when combining emotion engines"

[1620] (Claim 1)

[1621] means for receiving data input from a user;

[1622] means for analyzing the received data by natural language processing;

[1623] means for searching a database based on the analyzed data;

[1624] a means for generating relevant answers from the search results;

[1625] means for transmitting the generated answer to the user;

[1626] A means of analyzing emotions,

[1627] a means for adjusting responses based on the analyzed sentiment;

[1628] means for presenting the tailored answers to the user;

[1629] A system including:

[1630] (Claim 2)

[1631] 10. The system of claim 1, further comprising: means for extracting keywords from the analyzed data.

[1632] (Claim 3)

[1633] 10. The system of claim 1, further comprising means for selecting a most relevant answer from the search results. [Explanation of symbols]

[1634] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving data input from a user; means for analyzing the received data by natural language processing; means for searching a database based on the analyzed data; a means for generating relevant answers from the search results; means for transmitting the generated answer to the user; A system including:

2. The system of claim 1 further comprising means for extracting keywords from the analyzed data.

3. The system of claim 1 further comprising means for selecting the most relevant answer from the search results.

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