system

Abstract

We provide the system. [Solution] Means for receiving information requests from users, A means of analyzing received information requests using natural language processing and extracting keywords, A means of searching for materials based on extracted keywords, A means of suggesting a combination of relevant solutions based on the search results, A means of generating a proposal as a conversational response and sending it to the user, A means of displaying conversational responses to the user, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern business environment, it is important to obtain information and materials quickly and accurately. In particular, it is required to efficiently obtain the materials necessary for proposals to clients and in-house presentations and propose appropriate solutions. However, in conventional systems, there is a problem that users need to manually search for and customize a large number of materials, which is time-consuming and laborious. In addition, expertise is required to find an appropriate combination of solutions, which is inefficient. To solve these problems, an automated system that quickly searches for the materials required by users and proposes appropriate solutions is needed.

Means for Solving the Problems

[0005] This invention provides a means for receiving information requests from users, analyzing the received information requests using natural language processing, and extracting keywords. It also provides a system that includes means for searching for materials based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, and means for displaying the conversational response to the user. This system enables users to efficiently obtain necessary materials and receive appropriate solutions. Furthermore, by using a natural language processing engine in the analysis of information requests, the system accurately understands the user's intent, ranks search results in order of relevance, and presents them in a list format, thereby realizing a user-friendly system.

[0006] A "user" refers to a person who inputs an information request into the system and receives a response.

[0007] An "information request" refers to a user's request to the system to search for documents or information.

[0008] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0009] "Analysis" refers to the process of processing received information requests and extracting keywords.

[0010] "Keywords" refer to words or phrases that are important for document retrieval, extracted from information requests.

[0011] "Documents" refers to digital content such as documents, reports, and PDFs that are searched based on the user's information request.

[0012] "Searching" refers to the process of finding documents within a database or storage based on specified keywords.

[0013] A "combination of solutions" refers to a set of documents and proposals that respond to a user's information request.

[0014] "Conversational responses" refer to text messages generated to provide information and suggestions to users in a dialogue format.

[0015] "Means" refers to a method or apparatus for achieving a specific function or process. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The embodiments for carrying out the present invention will be described in detail below.

[0038] This invention relates to a system for efficiently obtaining the materials requested by a user. The main components of the system include a terminal where the user enters their request, and a server that analyzes the request and searches for the materials.

[0039] Feature Overview

[0040] The system receives information requests from users and analyzes them using natural language processing. Based on the analyzed keywords, it searches for relevant materials and suggests combinations of solutions based on the search results. The generated suggestions are sent to the user as a conversational response. This conversational response is displayed to the user on their device, allowing them to review the necessary materials and download them as needed.

[0041] Program processing flow

[0042] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I want to find a proposal document for a new product," the keywords extracted will be "new product," "proposal document," and "search."

[0043] Next, the server searches the database for relevant documents based on these keywords. For example, the database may contain files such as "New Product Proposal Document_2023.pdf" and "Proposal_New Product_Overview.docx". The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0044] Furthermore, based on the search results, the server suggests a combination of solutions suitable for the user's request. For example, in the case of a new product proposal, it might suggest a set of resources such as a "product data sheet," "competitor analysis report," and "case studies." This suggestion is formatted as a conversational response.

[0045] The server generates a conversational response, which is then sent to the terminal. The terminal displays this response to the user, allowing the user to view a list of materials and proposals on the screen. When the user selects a specific material, the terminal downloads it and provides it to the user.

[0046] Specific example

[0047] Suppose a user is looking for a proposal document for a new product. When the user types "Find a proposal document for a new product" into their device and sends it, the device sends this request to the server. The server parses the request and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Along with these search results, the server suggests a combination of solutions that includes a "product data sheet," "competitor analysis report," and "case studies."

[0048] The server generates a conversational response summarizing this information and sends it to the terminal. The terminal displays this information to the user, who can then select and download the necessary documents from the displayed list.

[0049] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Find me the proposal document for the new product."

[0053] Step 2:

[0054] The terminal sends a user information request to the server. The request content is transferred as text data.

[0055] Step 3:

[0056] The server passes the received information request to the natural language processing engine, which then begins the analysis.

[0057] Step 4:

[0058] The server uses a natural language processing engine to analyze the request and extract important keywords. For example, it might extract keywords such as "new product," "proposal document," and "search."

[0059] Step 5:

[0060] The server generates a database search query based on the keywords it extracts. This generated search query targets the data within the database.

[0061] Step 6:

[0062] The database is searched using a search query generated by the server. As a result of the search, relevant documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are obtained.

[0063] Step 7:

[0064] The server analyzes the search results and ranks them in order of relevance. This ensures that the most relevant information for the user is displayed at the top.

[0065] Step 8:

[0066] The server suggests combinations of relevant solutions based on the search results. For example, it might suggest sets of resources such as "product data sheets," "competitor analysis reports," and "case study collections."

[0067] Step 9:

[0068] The server generates the proposal as a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. Also, the recommended solution set is 'Product Datasheet', 'Competitive Analysis Report', and 'Case Studies'."

[0069] Step 10:

[0070] The server generates a conversational response message and sends it to the terminal.

[0071] Step 11:

[0072] The terminal displays conversational response messages received from the server to the user.

[0073] Step 12:

[0074] The user selects the necessary documents from the displayed list and clicks the download button.

[0075] Step 13:

[0076] The terminal downloads the selected document from the server and provides it to the user.

[0077] In this way, the system of the present invention efficiently processes information requests from users and quickly provides appropriate materials and solutions.

[0078] (Example 1)

[0079] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] Conventional information retrieval systems often make it difficult for users to quickly and accurately obtain specific documents or information resources they require. Furthermore, search results can be overwhelming and contain a lot of irrelevant information, meaning users may spend a considerable amount of time finding the information they need. Moreover, most systems simply provide documents without offering appropriate solutions. This leads to users having to go through a lot of trial and error before achieving satisfaction, hindering efficient information gathering. Therefore, the present invention aims to provide a system that efficiently retrieves the documents users require and quickly proposes appropriate solutions.

[0081] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0082] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for information resources based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, and means for downloading information resources selected by the user. This enables the user to efficiently obtain the desired materials and be quickly proposed appropriate solutions based on them.

[0083] An "information request" is a request or question that a user enters regarding specific information or materials they are looking for.

[0084] "Natural language processing" refers to the technologies and methods that enable computers to understand, interpret, and generate human language.

[0085] "Keywords" are the main words or phrases extracted from an information request that are used for searching.

[0086] An "information resource" is a collection of information, such as documents, files, and reports, stored in a database or other storage location.

[0087] A "combination of solutions" is a suggestion of a series of highly relevant information resources or methods for addressing a specific problem.

[0088] A "conversational response" is a type of response message that provides information to the user in a natural, dialogue-like format.

[0089] "Downloading means" refers to functions and methods for transferring information resources from a server to a terminal, enabling users to access and save them.

[0090] "Ranking" is the process of ordering search results based on their relevance and importance.

[0091] "List format" refers to a format in which search results or suggestions are organized and displayed in bullet points or lists.

[0092] A "terminal" is an electronic device used by a user to input information requests and to receive and display search results and solution responses.

[0093] The embodiments for carrying out the present invention will be described in detail below. This invention relates to a system that efficiently obtains the materials requested by a user and proposes appropriate solutions. The main components include a terminal where the user enters a request and a server that analyzes the request and searches for materials. The specific implementation method of this system will be described below.

[0094] System Configuration

[0095] The system is comprised of the following hardware and software.

[0096] hardware

[0097] 1. Terminal: An electronic device used by a user, such as a personal computer or smartphone.

[0098] 2. Server: A computer device that processes requests, searches databases, and generates responses.

[0099] software

[0100] 1. Natural Language Processing Engine: Google's (registered trademark) Natural Language Processing API.

[0101] 2. Database management system: A database system such as MySQL (registered trademark).

[0102] 3. Communication protocol: HTTP and JSON format used for communication between the terminal and the server.

[0103] System operation

[0104] User operation

[0105] The user enters their information request in natural language into the input field on their device. For example, they might enter a request such as, "Find me the proposal document for the new product." The user then clicks the submit button to send this request to the server.

[0106] Server receives and parses requests

[0107] The server receives information requests sent from the terminal. These requests are received as text data in JSON format. The received requests are passed to a natural language processing engine (Google's Natural Language Processing API) and parsed. As a result of the parsing, important keywords are extracted. For example, the keywords "new product" and "proposal document" are extracted.

[0108] Searching for materials

[0109] The server searches the MySQL database based on the extracted keywords. It executes SQL queries to find relevant documents. For example, documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are found.

[0110] Solution proposal

[0111] Based on the search results, the server generates a combination of solutions that respond to the user's request. For example, a set of resources such as a "product data sheet," "competitor analysis report," and "case study collection" might be suggested. This suggestion is then formatted as a conversational response.

[0112] Response generation and transmission

[0113] The server generates a conversational response and sends it to the terminal in JSON format. This response includes a list of retrieved materials and a set of proposed solutions.

[0114] Displaying the response from the terminal

[0115] The terminal analyzes the response received from the server and displays it in a user-friendly format. Users can view document lists and proposals on the screen.

[0116] Download materials

[0117] When a user selects a specific document, the device sends a download request for that document to the server. The server then sends the selected document to the device, allowing the user to download and view it.

[0118] Specific example

[0119] Suppose a user is looking for a proposal document for a new product. The user types "Find a proposal document for a new product" into their device and clicks the send button. The device sends this request to the server in JSON format. The server receives the request, parses it using a natural language processing engine, and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Furthermore, the server suggests a solution combination that includes a "product data sheet," "competitor analysis report," and "case studies." The server generates a conversational response summarizing this information and sends it to the device. The device displays this information to the user, who can select and download the necessary documents from the displayed list.

[0120] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0121] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0122] Step 1:

[0123] The user enters an information request into the terminal. They enter natural language text about the documents or information they are seeking into the input field and click the submit button. The input is in text format, such as a prompt like "Find new product proposal documents." The output is the user's information request text.

[0124] Step 2:

[0125] The terminal sends an information request to the server. The terminal receives the user's request and sends it to the server as an HTTP POST request. This request is formatted in JSON format. The input is the text of the information request entered by the user, and the output is the JSON data sent to the server.

[0126] Step 3:

[0127] The server receives and parses the information request. The server receives the information request sent from the terminal and passes it to a natural language processing engine (e.g., a natural language processing API). The input is JSON data received from the terminal, and the output is the result of the analysis by the natural language processing engine.

[0128] Step 4:

[0129] The server extracts relevant keywords. The natural language processing engine then extracts important keywords from the results of its analysis. The input is the analysis results of the natural language processing engine, and the output is a set of extracted keywords. Specifically, keywords such as "new product" and "proposal document" are obtained.

[0130] Step 5:

[0131] The server searches the database to retrieve documents. The server searches the MySQL database using SQL queries based on the extracted keywords. The input is a set of extracted keywords, and the output is a list of related documents. For example, search results for "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" can be obtained.

[0132] Step 6:

[0133] The server generates a suitable combination of solutions. Based on the search results, the server proposes a combination of solutions that meets the user's request. The input is a list of documents retrieved from the database, and the output is a set of solutions. For example, combinations such as "product data sheets," "competitor analysis reports," and "case studies" are generated.

[0134] Step 7:

[0135] The server generates a response and sends it to the terminal. Based on the generated solution set and search results, the server creates a conversational response. The response is formatted in JSON format for easy understanding by the user and sent to the terminal. The input is the solution set and search results, and the output is the JSON data sent to the terminal.

[0136] Step 8:

[0137] The terminal displays the response to the user. The terminal parses the JSON data received from the server and displays the information in a user-friendly format. The input is the JSON data received from the server, and the output is a set of documents and solutions displayed to the user.

[0138] Step 9:

[0139] The user selects and downloads the necessary documents. The user clicks to select the desired documents from the displayed list. The terminal sends a download request to the server based on the selection. The server sends the selected documents to the terminal in response to the request, and the user can download and view the documents. The input is the documents selected by the user, and the output is the downloaded document file.

[0140] (Application Example 1)

[0141] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0142] The goal is to address the challenge of customers lacking an efficient way to acquire products and information in physical stores by providing a system that improves in-store engagement using smart glasses. Traditional methods often resulted in customers spending a considerable amount of time searching for products and requiring assistance from store staff.

[0143] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0144] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, means for the user to input a request using smart glasses in a physical store, and means for presenting products and information based on the request. This makes it possible for customers to efficiently obtain the products and information they are looking for.

[0145] "Means for receiving information requests from users" refers to devices or software that receive voice or text requests entered by users using smart glasses.

[0146] "Means for analyzing and extracting keywords using natural language processing" refers to devices or software that utilize natural language processing techniques to analyze received requests and identify important keywords or phrases.

[0147] "Means for searching for materials based on extracted keywords" refers to devices or software that use extracted keywords to search for related materials and information from a database.

[0148] "Means of suggesting combinations of related solutions" refers to devices or software that suggest the optimal set of materials and information to respond to a user's request based on search results.

[0149] "A means of generating and sending proposed content as a conversational response to the user" refers to a device or software that compiles proposed materials and information into a natural conversational format and sends it to the user.

[0150] "Means of displaying conversational responses to the user" refers to devices or software that display conversational responses to the user through smart glasses or other terminals.

[0151] "A means for users to input requests using smart glasses in a physical store" refers to devices and software that allow customers to input requests via voice or text through smart glasses within an actual store.

[0152] "Means of presenting products and information based on requests" refers to devices or software that display relevant products and information based on the user's request.

[0153] To implement this invention, a terminal including smart glasses, a server that analyzes requests and searches for data, and an application installed on the smart glasses are required. Specifically, the embodiments for implementing the invention are described below.

[0154] System Configuration

[0155] The main components are as follows:

[0156] 1. Smart glasses: A device for users to input requests and display information.

[0157] 2. Server: The central processing unit that parses requests, searches for materials, and provides relevant information.

[0158] 3. Natural Language Processing Engine: Software that analyzes user requests and extracts keywords (e.g., Google Cloud Natural Language API).

[0159] 4. Document search server: Software that searches for documents from a database based on extracted keywords (e.g., ElasticSearch®).

[0160] Program processing flow

[0161] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I'm looking for a new smartphone," the keywords "new" and "smartphone" will be extracted.

[0162] Next, the server searches for relevant products and information from the data retrieval server based on these keywords. The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0163] Furthermore, the server suggests the best product combinations for the user's request based on the search results. For example, if the user is looking for a "new smartphone," the server might suggest a set including "the latest smartphone model," "special offers," and "accessories." This suggestion is formatted as a conversational response.

[0164] The conversational responses generated by the server are sent to the smart glasses. The smart glasses display these responses to the user, allowing the user to view product lists and suggestions on the screen. When the user selects a specific document or product, that information is displayed in detail.

[0165] Specific example

[0166] For example, a user might voice-input "I'm looking for a new smartphone" through smart glasses. This request is sent to the server, where a natural language processing engine extracts the keywords "new" and "smartphone." The server then uses a data search server to retrieve related product information and obtains a list of smartphones, such as "iPhone(registered trademark) 13 - 128GB" and "Samsung Galaxy S21 - 256GB." This list is presented to the user in a conversational format, for example, "Here are some recommended products: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0167] Example of a prompt

[0168] When a user enters "I'm looking for a new smartphone" into their smart glasses, the system uses an NLP process to extract the keywords "new" and "smartphone," and then retrieves related products from a search server based on those keywords. Related products include "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB," among others. The results are as follows:

[0169] Our recommended products are as follows:

[0170] iPhone 13 - 128GB

[0171] Samsung Galaxy S21 - 256GB

[0172] In this way, the present invention realizes a system that allows users to efficiently obtain the products and materials they desire and to quickly propose appropriate solutions based on them.

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

[0174] Step 1:

[0175] Receiving a request

[0176] The user enters a request into the smart glasses via voice or text. For example, the request might say, "I'm looking for a new smartphone." This request is then sent from the smart glasses to the server.

[0177] Input: User's voice or text request

[0178] Output: Request data sent to the server

[0179] Step 2:

[0180] Request parsing

[0181] The server passes the received request to the natural language processing engine. The natural language processing engine analyzes the request and extracts important keywords. For example, the keywords "new" and "smartphone" might be extracted.

[0182] Input: Received request data

[0183] Output: Extracted keywords (e.g., "new", "smartphone")

[0184] Step 3:

[0185] Searching for materials

[0186] The server uses the extracted keywords to send a query to the data retrieval server. The data retrieval server searches the database and retrieves relevant products and information. For example, it might retrieve a list of smartphones such as "iPhone 13 - 128GB" or "Samsung Galaxy S21 - 256GB".

[0187] Input: Extracted keywords

[0188] Output: Search results (e.g., "iPhone 13 - 128GB", "Samsung Galaxy S21 - 256GB")

[0189] Step 4:

[0190] Response generation

[0191] The server generates conversational responses to provide to the user based on the search results. For example, it might generate text such as, "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0192] Input: Search Results

[0193] Output: Conversational response (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0194] Step 5:

[0195] Sending and displaying responses

[0196] The server sends the generated conversational response to the smart glasses. The smart glasses display the received response to the user. The user can review the information displayed on the smart glasses' screen and select the products or information they need.

[0197] Input: Conversational response

[0198] Output: Response displayed on smart glasses (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0199] By executing these processing steps sequentially, users can efficiently obtain the products and information they need using smart glasses in physical stores.

[0200] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0201] The embodiments for carrying out the present invention will be described in detail below.

[0202] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. The system includes a function that provides personalized responses based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0203] Feature Overview

[0204] The system receives information requests from users and analyzes them using natural language processing and an emotion engine. Based on the analysis results, it searches for materials, ranks the search results in order of relevance, and suggests a combination of solutions that correspond to the user's emotions. The generated suggestions are sent to the user as a conversational response and displayed on their device.

[0205] Program processing flow

[0206] When the server receives an information request from a user, it first uses an emotion engine to analyze the user's emotional state. It analyzes emotional expressions, characters, and punctuation in the request text to determine the user's emotional state. For example, if a user enters "Send me the proposal document for the new product urgently!", the expression "urgently" will indicate an emotion of urgency.

[0207] Next, the server uses a natural language processing engine to analyze the request and extract important keywords. For example, keywords such as "new product," "proposal document," and "send" might be extracted.

[0208] Next, the server generates a database search query based on the extracted keywords. Using this search query, the server searches for documents within the database. For example, the database may contain documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx," and these documents will be searched. The server ranks the search results in order of relevance, displaying the most relevant documents to the user at the top.

[0209] Furthermore, the server considers the results of the emotion engine's analysis to suggest an appropriate combination of solutions. For example, if the emotion of anxiety is recognized, it can prioritize presenting materials and simplified proposals for a quick response.

[0210] The server compiles this information and generates a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[0211] The generated conversational response messages are sent to the terminal. The terminal displays this information to the user, who then selects the necessary documents from the displayed list and clicks the download button.

[0212] Specific example

[0213] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first uses an emotion engine to analyze the expression "urgent" to determine the user's urgency. Then, using a natural language processing engine, it extracts "new product" and "proposal document" as keywords and searches the database for "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". In addition, it suggests a "simplified version of the proposal document" for urgent use.

[0214] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select the necessary materials and download them quickly.

[0215] In this way, the system of the present invention recognizes the user's emotions and provides personalized information and solution suggestions based on those emotions, thereby improving the user experience.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Please send me the proposal document for the new product urgently!"

[0219] Step 2:

[0220] The terminal sends a user information request to the server. The request content is transferred as text data.

[0221] Step 3:

[0222] The server passes the received information request to the emotion engine, which then begins analyzing the emotional state. For example, it might detect the emotion of impatience from the expression "hurry up."

[0223] Step 4:

[0224] The server passes the request text to a natural language processing engine for analysis. Specifically, the natural language processing engine analyzes the request text and extracts important keywords such as "new product," "proposal document," and "send."

[0225] Step 5:

[0226] The server generates a search query based on the extracted keywords and searches the database. This search finds documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx".

[0227] Step 6:

[0228] The server ranks the search results in order of relevance. For example, newer materials and materials containing many relevant keywords will be ranked higher.

[0229] Step 7:

[0230] The server combines the search results with analysis results from the emotion engine to propose the most suitable solution. If the emotion of anxiety is detected, it prioritizes suggesting materials that allow for quick response or simplified versions of materials.

[0231] Step 8:

[0232] The server generates a conversational response message based on the proposal. For example, it might generate a message like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal Document_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[0233] Step 9:

[0234] The server generates a conversational response message and sends it to the terminal.

[0235] Step 10:

[0236] The terminal displays conversational response messages received from the server to the user.

[0237] Step 11:

[0238] The user selects the necessary documents from the displayed list and clicks the download button.

[0239] Step 12:

[0240] The terminal downloads the selected document from the server and provides it to the user.

[0241] In this way, the system recognizes the user's emotions and quickly provides customized materials and solutions based on them.

[0242] (Example 2)

[0243] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0244] Conventional information request processing systems provide information uniformly without considering the user's emotional state, making it difficult to respond quickly and appropriately to user needs. In particular, when a user is in a hurry or experiencing specific emotions, a response tailored to those emotions is required. Furthermore, finding necessary information from a large amount of data can be time-consuming, potentially detracting from the user experience.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0246] In this invention, the server includes means for receiving information requests from users, means for analyzing the user's emotional state using sentiment analysis based on the received information request, means for analyzing the information request using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for ranking the searched materials in order of relevance, means for suggesting combinations of relevant solutions considering the results of the sentiment analysis, means for generating the suggested content as a conversational response and sending it to the user, and means for displaying the conversational response to the user. This enables personalized responses based on emotional state and rapid provision of materials.

[0247] An "information request" is a request that a user submits to obtain specific documents or information.

[0248] "Sentiment analysis" is a technology that identifies and analyzes the emotional state of a user from their input text.

[0249] "Natural language processing" is a technology that allows computers to analyze, understand, and generate natural human language.

[0250] Keyword extraction is the process of identifying and extracting important words and phrases from a document.

[0251] "Document search" is the process of finding documents within a database based on specified keywords.

[0252] "Ranking by relevance" is the process of rearranging search results in order of how well they match the user's requirements.

[0253] A "combination of solutions" is a set of suggestions or answers that address a user's problems or needs.

[0254] A "conversational response" is a response in the form of automatically generated documents or messages, similar to a conversation between humans.

[0255] "Sending to user" refers to the process of transferring responses and materials generated by the server to the user's terminal.

[0256] "Displaying to the user" refers to the process of visually displaying information received on the user's device.

[0257] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. This system includes a function that provides personalized responses based on the user's emotional state by incorporating an emotion engine that recognizes the user's emotions.

[0258] Hardware and software configuration

[0259] server

[0260] The server is responsible for receiving requests, sentiment analysis, natural language processing, document search, ranking, solution proposal, response generation, and message sending. A sentiment engine is used for sentiment analysis, and a natural language processing engine is used for natural language processing. Documents are stored in the database, and the documents are retrieved using a search query.

[0261] Terminal

[0262] The terminal is responsible for receiving information requests from the user and sending them to the server. The terminal also displays the response message received from the server and provides an interface for the user to select and download documents.

[0263] Data processing and data calculation

[0264] Receiving information requests and sentiment analysis

[0265] When the user enters an information request on the terminal and clicks the send button, the terminal sends this request to the server as an HTTP request. The server passes the received request to the sentiment engine to analyze emotional expressions, punctuation, emphasized keywords, etc.

[0266] Natural language processing and keyword extraction

[0267] Next, the server passes the request sentence to the natural language processing engine. The natural language processing engine analyzes the request sentence and extracts important keywords. As a result, keywords such as "new product" and "proposal document" are extracted.

[0268] Search query generation and document search

[0269] The server combines the extracted keywords to generate a search query. For example, a query "SELECT FROM documents WHERE title LIKE '%new product%' AND title LIKE '%proposal document%'" is generated and a search is performed on the document database.

[0270] Ranking and solution proposals

[0271] Once search results are obtained, the server ranks these materials by relevance. Based on the ranked materials, the server considers the sentiment engine's analysis results and proposes appropriate solutions. For example, for urgent requests, a simplified version of the proposal is prioritized.

[0272] Conversational response generation and sending

[0273] The server generates a conversational response message based on the ranking and solution suggestion results. The generated response message is encoded in JSON format and sent to the terminal as an HTTP response.

[0274] Displaying response messages and downloading documents

[0275] The terminal decodes the response message received from the server and displays it to the user. The user selects the necessary documents and clicks the download button to retrieve them.

[0276] Specific example

[0277] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first performs sentiment analysis. It recognizes the emotion of urgency from the expression "urgently," and then uses a natural language processing engine to extract "new product" and "proposal document" as keywords. The server generates a search query and searches the database for "new product proposal document_2023.pdf" and "proposal_new_product_overview.docx." In addition, it suggests a "simplified version of the proposal document" for urgent use.

[0278] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select and quickly download the necessary materials.

[0279] Examples of prompt sentences

[0280] Please send the proposal materials for the new product urgently.

[0281] I'm looking for the latest proposal for the new product. Can you help me?

[0282] A simplified version of the proposal materials is urgently needed.

[0283] The flow of the specific process in Example 2 will be described using FIG. 13.

[0284] Step 1:

[0285] Receiving the user's information request

[0286] Specific operations

[0287] The user enters "Please send the proposal materials for the new product urgently!" on the terminal and clicks the send button.

[0288] Input and output

[0289] Input: User's information request ("Please send the proposal materials for the new product urgently!")

[0290] Output: The terminal sends the request to the server in the form of an HTTP request.

[0291] Step 2:

[0292] Analyzing the user's emotional state

[0293] Specific operations

[0294] The server passes the received request sentence to the emotion engine. The emotion engine analyzes the keyword "urgently" and recognizes the emotion of anxiety.

[0295] Input and Output

[0296] Input: User's information request ("Send me the proposal materials for the new product quickly!")

[0297] Output: Sentiment analysis result (Anxiety sentiment is recognized)

[0298] Step 3:

[0299] Analysis of the request sentence and keyword extraction

[0300] Specific operations

[0301] The server passes the request sentence to the natural language processing engine to extract important keywords. For example, "new product" and "proposal materials" are extracted.

[0302] Input and Output

[0303] Input: User's information request ("Send me the proposal materials for the new product quickly!")

[0304] Output: List of extracted keywords ("new product", "proposal materials")

[0305] Step 4:

[0306] Generation of database search query

[0307] Specific operations

[0308] The server combines the extracted keywords to generate an SQL query. For example, a query like "SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'" is generated.

[0309] Input and Output

[0310] Input: List of extracted keywords ("new product", "proposal materials")

[0311] Output: Generated SQL query ("SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'")

[0312] Step 5:

[0313] Search and ranking of materials

[0314] Specific operations

[0315] [[ID=I7]] The server executes the generated SQL query to search the material database and ranks the search results in order of relevance. For example, make sure that "new product proposal materials_2023.pdf" and "proposal for new product_summary.docx" come out on top.

[0316] Input and output ?

[0317] Input: Generated SQL query

[0318] Output: List of ranked search results ("new product proposal materials_2023.pdf", "proposal for new product_summary.docx")[[]END]

[0319] Step 6:

[0320] Proposal of solutions based on sentiment

[0321] Specific operations

[0322] The server takes into account the results of sentiment analysis and additionally proposes a simplified version of the proposal materials if it is urgent.

[0323] Input and output

[0324] Input: Sentiment analysis results, list of ranked search results

[0325] Output: List of proposed solutions ("New Product Proposal Document_2023.pdf", "Proposal Document_New Product_Overview.docx", "Simplified Proposal Document")

[0326] Step 7:

[0327] Generating conversational response messages

[0328] Specific actions

[0329] The server generates conversational response messages based on a list of proposed solutions.

[0330] Input and output

[0331] Input: List of proposed solutions

[0332] Output: Generated conversational response message (Example: "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. For urgent requests, we also recommend the 'Simplified Proposal Document'.")

[0333] Step 8:

[0334] Sending and displaying response messages

[0335] Specific actions

[0336] The server sends a generated response message to the terminal. The terminal receives the response message and displays it to the user. The user selects the necessary documents and clicks the download button.

[0337] Input and output

[0338] Input: Generated conversational response messages

[0339] Output: Response message displayed on the terminal and user download action

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0342] Traditional information request systems provided uniform responses without considering the user's emotional state, resulting in challenges with user satisfaction and efficiency. Furthermore, particularly in the food delivery sector, there was a lack of mechanisms to quickly suggest optimal menus tailored to the user's condition and emotions. Consequently, it was difficult to adequately meet user needs and improve satisfaction.

[0343] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, and means for recognizing the user's emotional state using an emotion engine. This makes it possible to provide personalized materials or menus that take the user's emotions into consideration.

[0344] An "information request" refers to a request made by a user to obtain specific information or materials.

[0345] "Natural language processing" refers to the technology of analyzing language data entered by a user and understanding its meaning and intent.

[0346] "Keywords" refer to important words or phrases included in a user's information request.

[0347] An "emotion engine" refers to a technology that analyzes a user's text data to understand their emotional state and recognize those emotions.

[0348] "Emotional state" refers to the psychological state that can be inferred from the user's statements and written content.

[0349] "Documents" refers to documents and data files related to the information requested by the user.

[0350] In the context of food delivery, "menu" refers specifically to the range of meals available to the user.

[0351] "Searching" refers to finding relevant documents or menus from databases or information sources based on extracted keywords.

[0352] A "solution" refers to a specific solution or proposal provided in response to a user's information request.

[0353] "Conversational responses" refer to response messages returned to the user that take the form of a dialogue, and are messages exchanged between the user and the system.

[0354] A "terminal" refers to a device used by a user to input information requests or receive response messages.

[0355] The system realizing this invention recognizes the user's emotional state in the context of food delivery and provides personalized menu suggestions based on that emotion. The specific implementation method is described below.

[0356] System Configuration

[0357] This system consists of a server containing multiple modules for receiving and analyzing information requests from users and providing appropriate solutions, and terminals used by the users.

[0358] Hardware and software configuration

[0359] server:

[0360] A web server for receiving and analyzing information requests.

[0361] Emotion engine (e.g., IBM Watson® Natural Language Understanding).

[0362] A natural language processing engine (e.g., Google Cloud Natural Language API).

[0363] Database management systems (e.g., MongoDB).

[0364] Dialogue systems (e.g., Dialogflow).

[0365] Terminal:

[0366] Smartphones and tablets used by users.

[0367] The application will be implemented as a mobile app that runs on iOS or Android®.

[0368] Processing flow

[0369] 1. Receiving information requests from users

[0370] The user types "I'm tired today, I want food right away" into a smartphone application and sends it.

[0371] 2. Recognition of emotional states

[0372] The server uses an emotion engine to recognize the emotion of "tiredness" and analyze the user's urgent requests.

[0373] 3. Keyword extraction using natural language processing

[0374] Using a natural language processing engine, we extract the important keywords "tired," "immediately," and "food."

[0375] 4. Database Search

[0376] Based on the extracted keywords and emotional state, the system searches the database for relevant menu items. For example, it searches using the conditions "available for immediate delivery" and "easy to eat."

[0377] 5. Personalized solution proposals

[0378] Based on the search results, the system generates conversational response messages that take into account the user's emotional state. For example, it might generate messages like, "The following items are available for immediate delivery: pizza, sandwiches. We also recommend our salads, perfect for replenishing energy when you're feeling tired."

[0379] 6. Sending and displaying responses to the user

[0380] The generated response message is sent to the user's terminal, which then displays this message.

[0381] Specific example

[0382] Specific examples are given below.

[0383] The user types and submits "I'm tired today, I want food right away." The server then uses an emotion engine to recognize the emotional state of "tired" and a natural language processing engine to extract the keywords "tired," "immediately," and "food." Next, it searches its database for menus that are "immediately available for delivery" and "easy to eat," generating personalized suggestions such as "pizza," "sandwich," and "salad." Finally, these suggestions are sent to the user's device and displayed, allowing the user to quickly select appropriate food.

[0384] Example of a prompt

[0385] User input: "I'm tired today, I want food right away."

[0386] Emotion recognition result: "Fatigue"

[0387] Key keywords: "tired", "immediately", "food"

[0388] Search criteria: "Menus available for immediate delivery"

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user enters and submits an information request. Once this information request is entered, the device sends this request to the server. For example, if the user enters "I'm tired today, I want food right away," the device transfers that text data to the server.

[0392] Step 2:

[0393] The server receives an information request from the user. The server receives the text data of the information request as input and prepares it as data to be analyzed for processing in the next step.

[0394] Step 3:

[0395] The server uses an emotion engine to recognize the user's emotional state from the received information request. At this time, the emotion engine analyzes emotional expressions such as "tired" from the text data and outputs "fatigue" as the emotional state.

[0396] Step 4:

[0397] The server uses a natural language processing engine to extract key keywords from the information request. This process extracts the keywords "tired," "immediately," and "food." The input is the request text data, and the output is a list of extracted keywords.

[0398] Step 5:

[0399] Based on the keywords extracted by the server and the recognized emotional state, a database management system is used to search for relevant materials or menus. In this process, the keyword list and emotional state are used as input, and a list of menus matching the search criteria is output.

[0400] Step 6:

[0401] The server ranks the menu items in order of relevance based on search results and sentiment. At this stage, relevance analysis is performed, and a ranked menu list is output.

[0402] Step 7:

[0403] The server generates personalized solutions for the user as conversational response messages based on a ranked menu list. The input is the ranked menu list, and the output is the generated conversational response messages.

[0404] Step 8:

[0405] The server sends a generated conversational response message to the terminal. Specifically, the response message is sent to the user's smartphone in the form of an email or notification.

[0406] Step 9:

[0407] The terminal displays the response message it received to the user. The user then checks the appropriate menu from the displayed message and makes a selection or order.

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0409] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0411] [Second Embodiment]

[0412] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0413] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0414] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0416] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0418] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0419] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0420] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0423] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0424] The embodiments for carrying out the present invention will be described in detail below.

[0425] This invention relates to a system for efficiently obtaining the materials requested by a user. The main components of the system include a terminal where the user enters their request, and a server that analyzes the request and searches for the materials.

[0426] Feature Overview

[0427] The system receives information requests from users and analyzes them using natural language processing. Based on the analyzed keywords, it searches for relevant materials and suggests combinations of solutions based on the search results. The generated suggestions are sent to the user as a conversational response. This conversational response is displayed to the user on their device, allowing them to review the necessary materials and download them as needed.

[0428] Program processing flow

[0429] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I want to find a proposal document for a new product," the keywords extracted will be "new product," "proposal document," and "search."

[0430] Next, the server searches the database for relevant documents based on these keywords. For example, the database may contain files such as "New Product Proposal Document_2023.pdf" and "Proposal_New Product_Overview.docx". The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0431] Furthermore, based on the search results, the server suggests a combination of solutions suitable for the user's request. For example, in the case of a new product proposal, it might suggest a set of resources such as a "product data sheet," "competitor analysis report," and "case studies." This suggestion is formatted as a conversational response.

[0432] The server generates a conversational response, which is then sent to the terminal. The terminal displays this response to the user, allowing the user to view a list of materials and proposals on the screen. When the user selects a specific material, the terminal downloads it and provides it to the user.

[0433] Specific example

[0434] Suppose a user is looking for a proposal document for a new product. When the user types "Find a proposal document for a new product" into their device and sends it, the device sends this request to the server. The server parses the request and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Along with these search results, the server suggests a combination of solutions that includes a "product data sheet," "competitor analysis report," and "case studies."

[0435] The server generates a conversational response summarizing this information and sends it to the terminal. The terminal displays this information to the user, who can then select and download the necessary documents from the displayed list.

[0436] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Find me the proposal document for the new product."

[0440] Step 2:

[0441] The terminal sends a user information request to the server. The request content is transferred as text data.

[0442] Step 3:

[0443] The server passes the received information request to the natural language processing engine, which then begins the analysis.

[0444] Step 4:

[0445] The server uses a natural language processing engine to analyze the request and extract important keywords. For example, it might extract keywords such as "new product," "proposal document," and "search."

[0446] Step 5:

[0447] The server generates a database search query based on the keywords it extracts. This generated search query targets the data within the database.

[0448] Step 6:

[0449] The database is searched using a search query generated by the server. As a result of the search, relevant documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are obtained.

[0450] Step 7:

[0451] The server analyzes the search results and ranks them in order of relevance. This ensures that the most relevant information for the user is displayed at the top.

[0452] Step 8:

[0453] The server suggests combinations of relevant solutions based on the search results. For example, it might suggest sets of resources such as "product data sheets," "competitor analysis reports," and "case study collections."

[0454] Step 9:

[0455] The server generates the proposal as a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. Also, the recommended solution set is 'Product Datasheet', 'Competitive Analysis Report', and 'Case Studies'."

[0456] Step 10:

[0457] The server generates a conversational response message and sends it to the terminal.

[0458] Step 11:

[0459] The terminal displays conversational response messages received from the server to the user.

[0460] Step 12:

[0461] The user selects the necessary documents from the displayed list and clicks the download button.

[0462] Step 13:

[0463] The terminal downloads the selected document from the server and provides it to the user.

[0464] In this way, the system of the present invention efficiently processes information requests from users and quickly provides appropriate materials and solutions.

[0465] (Example 1)

[0466] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0467] Conventional information retrieval systems often make it difficult for users to quickly and accurately obtain specific documents or information resources they require. Furthermore, search results can be overwhelming and contain a lot of irrelevant information, meaning users may spend a considerable amount of time finding the information they need. Moreover, most systems simply provide documents without offering appropriate solutions. This leads to users having to go through a lot of trial and error before achieving satisfaction, hindering efficient information gathering. Therefore, the present invention aims to provide a system that efficiently retrieves the documents users require and quickly proposes appropriate solutions.

[0468] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0469] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for information resources based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, and means for downloading information resources selected by the user. This enables the user to efficiently obtain the desired materials and be quickly proposed appropriate solutions based on them.

[0470] An "information request" is a request or question that a user enters regarding specific information or materials they are looking for.

[0471] "Natural language processing" refers to the technologies and methods that enable computers to understand, interpret, and generate human language.

[0472] "Keywords" are the main words or phrases extracted from an information request that are used for searching.

[0473] An "information resource" is a collection of information, such as documents, files, and reports, stored in a database or other storage location.

[0474] A "combination of solutions" is a suggestion of a series of highly relevant information resources or methods for addressing a specific problem.

[0475] A "conversational response" is a type of response message that provides information to the user in a natural, dialogue-like format.

[0476] "Downloading means" refers to functions and methods for transferring information resources from a server to a terminal, enabling users to access and save them.

[0477] "Ranking" is the process of ordering search results based on their relevance and importance.

[0478] "List format" refers to a format in which search results or suggestions are organized and displayed in bullet points or lists.

[0479] A "terminal" is an electronic device used by a user to input information requests and to receive and display search results and solution responses.

[0480] The embodiments for carrying out the present invention will be described in detail below. This invention relates to a system that efficiently obtains the materials requested by a user and proposes appropriate solutions. The main components include a terminal where the user enters a request and a server that analyzes the request and searches for materials. The specific implementation method of this system will be described below.

[0481] System Configuration

[0482] The system is comprised of the following hardware and software.

[0483] hardware

[0484] 1. Terminal: An electronic device used by a user, such as a personal computer or smartphone.

[0485] 2. Server: A computer device that processes requests, searches databases, and generates responses.

[0486] software

[0487] 1. Natural Language Processing Engine: Google's Natural Language Processing API.

[0488] 2. Database management system: A database system such as MySQL.

[0489] 3. Communication protocol: HTTP and JSON format used for communication between the terminal and the server.

[0490] System operation

[0491] User operation

[0492] The user enters their information request in natural language into the input field on their device. For example, they might enter a request such as, "Find me the proposal document for the new product." The user then clicks the submit button to send this request to the server.

[0493] Server receives and parses requests

[0494] The server receives information requests sent from the terminal. These requests are received as text data in JSON format. The received requests are passed to a natural language processing engine (Google's Natural Language Processing API) and parsed. As a result of the parsing, important keywords are extracted. For example, the keywords "new product" and "proposal document" are extracted.

[0495] Searching for materials

[0496] The server searches the MySQL database based on the extracted keywords. It executes SQL queries to find relevant documents. For example, documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are found.

[0497] Solution proposal

[0498] Based on the search results, the server generates a combination of solutions that respond to the user's request. For example, a set of resources such as a "product data sheet," "competitor analysis report," and "case study collection" might be suggested. This suggestion is then formatted as a conversational response.

[0499] Response generation and transmission

[0500] The server generates a conversational response and sends it to the terminal in JSON format. This response includes a list of retrieved materials and a set of proposed solutions.

[0501] Displaying the response from the terminal

[0502] The terminal analyzes the response received from the server and displays it in a user-friendly format. Users can view document lists and proposals on the screen.

[0503] Download materials

[0504] When a user selects a specific document, the device sends a download request for that document to the server. The server then sends the selected document to the device, allowing the user to download and view it.

[0505] Specific example

[0506] Suppose a user is looking for a proposal document for a new product. The user types "Find a proposal document for a new product" into their device and clicks the send button. The device sends this request to the server in JSON format. The server receives the request, parses it using a natural language processing engine, and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Furthermore, the server suggests a solution combination that includes a "product data sheet," "competitor analysis report," and "case studies." The server generates a conversational response summarizing this information and sends it to the device. The device displays this information to the user, who can select and download the necessary documents from the displayed list.

[0507] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The user enters an information request into the terminal. They enter natural language text about the documents or information they are seeking into the input field and click the submit button. The input is in text format, such as a prompt like "Find new product proposal documents." The output is the user's information request text.

[0511] Step 2:

[0512] The terminal sends an information request to the server. The terminal receives the user's request and sends it to the server as an HTTP POST request. This request is formatted in JSON format. The input is the text of the information request entered by the user, and the output is the JSON data sent to the server.

[0513] Step 3:

[0514] The server receives and parses the information request. The server receives the information request sent from the terminal and passes it to a natural language processing engine (e.g., a natural language processing API). The input is JSON data received from the terminal, and the output is the result of the analysis by the natural language processing engine.

[0515] Step 4:

[0516] The server extracts relevant keywords. The natural language processing engine then extracts important keywords from the results of its analysis. The input is the analysis results of the natural language processing engine, and the output is a set of extracted keywords. Specifically, keywords such as "new product" and "proposal document" are obtained.

[0517] Step 5:

[0518] The server searches the database to retrieve documents. The server searches the MySQL database using SQL queries based on the extracted keywords. The input is a set of extracted keywords, and the output is a list of related documents. For example, search results for "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" can be obtained.

[0519] Step 6:

[0520] The server generates a suitable combination of solutions. Based on the search results, the server proposes a combination of solutions that meets the user's request. The input is a list of documents retrieved from the database, and the output is a set of solutions. For example, combinations such as "product data sheets," "competitor analysis reports," and "case studies" are generated.

[0521] Step 7:

[0522] The server generates a response and sends it to the terminal. Based on the generated solution set and search results, the server creates a conversational response. The response is formatted in JSON format for easy understanding by the user and sent to the terminal. The input is the solution set and search results, and the output is the JSON data sent to the terminal.

[0523] Step 8:

[0524] The terminal displays the response to the user. The terminal parses the JSON data received from the server and displays the information in a user-friendly format. The input is the JSON data received from the server, and the output is a set of documents and solutions displayed to the user.

[0525] Step 9:

[0526] The user selects and downloads the necessary documents. The user clicks to select the desired documents from the displayed list. The terminal sends a download request to the server based on the selection. The server sends the selected documents to the terminal in response to the request, and the user can download and view the documents. The input is the documents selected by the user, and the output is the downloaded document file.

[0527] (Application Example 1)

[0528] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0529] The goal is to address the challenge of customers lacking an efficient way to acquire products and information in physical stores by providing a system that improves in-store engagement using smart glasses. Traditional methods often resulted in customers spending a considerable amount of time searching for products and requiring assistance from store staff.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0531] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, means for the user to input a request using smart glasses in a physical store, and means for presenting products and information based on the request. This makes it possible for customers to efficiently obtain the products and information they are looking for.

[0532] "Means for receiving information requests from users" refers to devices or software that receive voice or text requests entered by users using smart glasses.

[0533] "Means for analyzing and extracting keywords using natural language processing" refers to devices or software that utilize natural language processing techniques to analyze received requests and identify important keywords or phrases.

[0534] "Means for searching for materials based on extracted keywords" refers to devices or software that use extracted keywords to search for related materials and information from a database.

[0535] "Means of suggesting combinations of related solutions" refers to devices or software that suggest the optimal set of materials and information to respond to a user's request based on search results.

[0536] "A means of generating and sending proposed content as a conversational response to the user" refers to a device or software that compiles proposed materials and information into a natural conversational format and sends it to the user.

[0537] "Means of displaying conversational responses to the user" refers to devices or software that display conversational responses to the user through smart glasses or other terminals.

[0538] "A means for users to input requests using smart glasses in a physical store" refers to devices and software that allow customers to input requests via voice or text through smart glasses within an actual store.

[0539] "Means of presenting products and information based on requests" refers to devices or software that display relevant products and information based on the user's request.

[0540] To implement this invention, a terminal including smart glasses, a server that analyzes requests and searches for data, and an application installed on the smart glasses are required. Specifically, the embodiments for implementing the invention are described below.

[0541] System Configuration

[0542] The main components are as follows:

[0543] 1. Smart glasses: A device for users to input requests and display information.

[0544] 2. Server: The central processing unit that parses requests, searches for materials, and provides relevant information.

[0545] 3. Natural Language Processing Engine: Software that analyzes user requests and extracts keywords (e.g., Google Cloud Natural Language API).

[0546] 4. Document search server: Software that searches for documents in a database based on extracted keywords (e.g., Elasticsearch).

[0547] Program processing flow

[0548] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I'm looking for a new smartphone," the keywords "new" and "smartphone" will be extracted.

[0549] Next, the server searches for relevant products and information from the data retrieval server based on these keywords. The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0550] Furthermore, the server suggests the best product combinations for the user's request based on the search results. For example, if the user is looking for a "new smartphone," the server might suggest a set including "the latest smartphone model," "special offers," and "accessories." This suggestion is formatted as a conversational response.

[0551] The conversational responses generated by the server are sent to the smart glasses. The smart glasses display these responses to the user, allowing the user to view product lists and suggestions on the screen. When the user selects a specific document or product, that information is displayed in detail.

[0552] Specific example

[0553] For example, a user might voice-input "I'm looking for a new smartphone" through smart glasses. This request is sent to the server, where a natural language processing engine extracts the keywords "new" and "smartphone." The server then uses a data search server to search for related product information and retrieves a list of smartphones, such as "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB." This list is presented to the user in a conversational format, for example, "Here are some recommended products: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0554] Example of a prompt

[0555] When a user enters "I'm looking for a new smartphone" into their smart glasses, the system uses an NLP process to extract the keywords "new" and "smartphone," and then retrieves related products from a search server based on those keywords. Related products include "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB," among others. The results are as follows:

[0556] Our recommended products are as follows:

[0557] iPhone 13 - 128GB

[0558] Samsung Galaxy S21 - 256GB

[0559] In this way, the present invention realizes a system that allows users to efficiently obtain the products and materials they desire and to quickly propose appropriate solutions based on them.

[0560] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0561] Step 1:

[0562] Receiving a request

[0563] The user enters a request into the smart glasses via voice or text. For example, the request might say, "I'm looking for a new smartphone." This request is then sent from the smart glasses to the server.

[0564] Input: User's voice or text request

[0565] Output: Request data sent to the server

[0566] Step 2:

[0567] Request parsing

[0568] The server passes the received request to the natural language processing engine. The natural language processing engine analyzes the request and extracts important keywords. For example, the keywords "new" and "smartphone" might be extracted.

[0569] Input: Received request data

[0570] Output: Extracted keywords (e.g., "new", "smartphone")

[0571] Step 3:

[0572] Searching for materials

[0573] The server uses the extracted keywords to send a query to the data retrieval server. The data retrieval server searches the database and retrieves relevant products and information. For example, it might retrieve a list of smartphones such as "iPhone 13 - 128GB" or "Samsung Galaxy S21 - 256GB".

[0574] Input: Extracted keywords

[0575] Output: Search results (e.g., "iPhone 13 - 128GB", "Samsung Galaxy S21 - 256GB")

[0576] Step 4:

[0577] Response generation

[0578] The server generates conversational responses to provide to the user based on the search results. For example, it might generate text such as, "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0579] Input: Search Results

[0580] Output: Conversational response (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0581] Step 5:

[0582] Sending and displaying responses

[0583] The server sends the generated conversational response to the smart glasses. The smart glasses display the received response to the user. The user can review the information displayed on the smart glasses' screen and select the products or information they need.

[0584] Input: Conversational response

[0585] Output: Response displayed on smart glasses (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0586] By executing these processing steps sequentially, users can efficiently obtain the products and information they need using smart glasses in physical stores.

[0587] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0588] The embodiments for carrying out the present invention will be described in detail below.

[0589] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. The system includes a function that provides personalized responses based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0590] Feature Overview

[0591] The system receives information requests from users and analyzes them using natural language processing and an emotion engine. Based on the analysis results, it searches for materials, ranks the search results in order of relevance, and suggests a combination of solutions that correspond to the user's emotions. The generated suggestions are sent to the user as a conversational response and displayed on their device.

[0592] Program processing flow

[0593] When the server receives an information request from a user, it first uses an emotion engine to analyze the user's emotional state. It analyzes emotional expressions, characters, and punctuation in the request text to determine the user's emotional state. For example, if a user enters "Send me the proposal document for the new product urgently!", the expression "urgently" will indicate an emotion of urgency.

[0594] Next, the server uses a natural language processing engine to analyze the request and extract important keywords. For example, keywords such as "new product," "proposal document," and "send" might be extracted.

[0595] Next, the server generates a database search query based on the extracted keywords. Using this search query, the server searches for documents within the database. For example, the database may contain documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx," and these documents will be searched. The server ranks the search results in order of relevance, displaying the most relevant documents to the user at the top.

[0596] Furthermore, the server considers the results of the emotion engine's analysis to suggest an appropriate combination of solutions. For example, if the emotion of anxiety is recognized, it can prioritize presenting materials and simplified proposals for a quick response.

[0597] The server compiles this information and generates a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[0598] The generated conversational response messages are sent to the terminal. The terminal displays this information to the user, who then selects the necessary documents from the displayed list and clicks the download button.

[0599] Specific example

[0600] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first uses an emotion engine to analyze the expression "urgent" to determine the user's urgency. Then, using a natural language processing engine, it extracts "new product" and "proposal document" as keywords and searches the database for "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". In addition, it suggests a "simplified version of the proposal document" for urgent use.

[0601] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select the necessary materials and download them quickly.

[0602] In this way, the system of the present invention recognizes the user's emotions and provides personalized information and solution suggestions based on those emotions, thereby improving the user experience.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Please send me the proposal document for the new product urgently!"

[0606] Step 2:

[0607] The terminal sends a user information request to the server. The request content is transferred as text data.

[0608] Step 3:

[0609] The server passes the received information request to the emotion engine, which then begins analyzing the emotional state. For example, it might detect the emotion of impatience from the expression "hurry up."

[0610] Step 4:

[0611] The server passes the request text to a natural language processing engine for analysis. Specifically, the natural language processing engine analyzes the request text and extracts important keywords such as "new product," "proposal document," and "send."

[0612] Step 5:

[0613] The server generates a search query based on the extracted keywords and searches the database. This search finds documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx".

[0614] Step 6:

[0615] The server ranks the search results in order of relevance. For example, newer materials and materials containing many relevant keywords will be ranked higher.

[0616] Step 7:

[0617] The server combines the search results with analysis results from the emotion engine to propose the most suitable solution. If the emotion of anxiety is detected, it prioritizes suggesting materials that allow for quick response or simplified versions of materials.

[0618] Step 8:

[0619] The server generates a conversational response message based on the proposal. For example, it might generate a message like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal Document_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[0620] Step 9:

[0621] The server generates a conversational response message and sends it to the terminal.

[0622] Step 10:

[0623] The terminal displays conversational response messages received from the server to the user.

[0624] Step 11:

[0625] The user selects the necessary documents from the displayed list and clicks the download button.

[0626] Step 12:

[0627] The terminal downloads the selected document from the server and provides it to the user.

[0628] In this way, the system recognizes the user's emotions and quickly provides customized materials and solutions based on them.

[0629] (Example 2)

[0630] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0631] Conventional information request processing systems provide information uniformly without considering the user's emotional state, making it difficult to respond quickly and appropriately to user needs. In particular, when a user is in a hurry or experiencing specific emotions, a response tailored to those emotions is required. Furthermore, finding necessary information from a large amount of data can be time-consuming, potentially detracting from the user experience.

[0632] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0633] In this invention, the server includes means for receiving information requests from users, means for analyzing the user's emotional state using sentiment analysis based on the received information request, means for analyzing the information request using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for ranking the searched materials in order of relevance, means for suggesting combinations of relevant solutions considering the results of the sentiment analysis, means for generating the suggested content as a conversational response and sending it to the user, and means for displaying the conversational response to the user. This enables personalized responses based on emotional state and rapid provision of materials.

[0634] An "information request" is a request that a user submits to obtain specific documents or information.

[0635] "Sentiment analysis" is a technology that identifies and analyzes the emotional state of a user from their input text.

[0636] "Natural language processing" is a technology that allows computers to analyze, understand, and generate natural human language.

[0637] Keyword extraction is the process of identifying and extracting important words and phrases from a document.

[0638] "Document search" is the process of finding documents within a database based on specified keywords.

[0639] "Ranking by relevance" is the process of rearranging search results in order of how well they match the user's requirements.

[0640] A "combination of solutions" is a set of suggestions or answers that address a user's problems or needs.

[0641] A "conversational response" is a response in the form of automatically generated documents or messages, similar to a conversation between humans.

[0642] "Sending to user" refers to the process of transferring responses and materials generated by the server to the user's terminal.

[0643] "Displaying to the user" refers to the process of visually displaying information received on the user's device.

[0644] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. This system includes a function that provides personalized responses based on the user's emotional state by incorporating an emotion engine that recognizes the user's emotions.

[0645] Hardware and software configuration

[0646] server

[0647] The server is responsible for receiving requests, sentiment analysis, natural language processing, data retrieval, ranking, solution suggestions, response generation, and message sending. A sentiment engine is used for sentiment analysis, and a natural language processing engine is used for natural language processing. Data is stored in the database, and data is retrieved using search queries.

[0648] terminal

[0649] The terminal is responsible for receiving information requests from users and sending them to the server. The terminal also displays response messages received from the server and provides an interface for users to select and download materials.

[0650] Data processing and data calculation

[0651] Receiving information requests and sentiment analysis

[0652] When a user enters an information request into their device and clicks the send button, the device sends this request to the server as an HTTP request. The server passes the received request to the sentiment engine, which analyzes emotional expressions, punctuation, emphasized keywords, and other relevant information.

[0653] Natural language processing and keyword extraction

[0654] Next, the server passes the request sentence to the natural language processing engine. The natural language processing engine analyzes the request sentence and extracts important keywords. As a result, keywords such as "new product" and "proposal materials" are extracted.

[0655] Search query generation and document search

[0656] The server combines the extracted keywords to generate a search query. For example, a query such as "SELECT FROM documents WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'" is generated, and a search is performed on the document database.

[0657] Ranking and solution proposal

[0658] When search results are obtained, the server ranks these documents in order of relevance. For the ranked documents, the server considers the analysis results of the sentiment engine and proposes appropriate solutions. For example, for urgent requests, priority is given to simplified proposal documents.

[0659] Generation and transmission of response in conversation form

[0660] The server generates a response message in conversation form based on the results of ranking and solution proposal. The generated response message is encoded in JSON format and sent to the terminal as an HTTP response.

[0661] Display of response message and document download

[0662] The terminal decodes the response message received from the server and displays it to the user. The user selects the required document and clicks the download button to obtain the document.

[0663] Specific example

[0664] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first performs sentiment analysis. It recognizes the emotion of urgency from the expression "urgently," and then uses a natural language processing engine to extract "new product" and "proposal document" as keywords. The server generates a search query and searches the database for "new product proposal document_2023.pdf" and "proposal_new_product_overview.docx." In addition, it suggests a "simplified version of the proposal document" for urgent use.

[0665] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select and quickly download the necessary materials.

[0666] Example of a prompt

[0667] Please send the proposal document for the new product as soon as possible.

[0668] I'm looking for the latest proposals for a new product. Can you help me?

[0669] I urgently need a simplified version of the proposal document.

[0670] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0671] Step 1:

[0672] Receiving a user information request

[0673] Specific actions

[0674] The user types "Send me the new product proposal quickly!" into their device and clicks the send button.

[0675] Input and output

[0676] Input: User information request ("Please send me the new product proposal materials urgently!")

[0677] Output: The terminal sends the request to the server in HTTP request format.

[0678] Step 2:

[0679] Analyze the user's emotional state.

[0680] Specific actions

[0681] The server passes the received request to the emotion engine. The emotion engine analyzes the keyword "hurry" and recognizes the emotion of urgency.

[0682] Input and output

[0683] Input: User information request ("Please send me the new product proposal materials urgently!")

[0684] Output: Emotion analysis results (emotion of anxiety is recognized)

[0685] Step 3:

[0686] Request text analysis and keyword extraction

[0687] Specific actions

[0688] The server passes the request to a natural language processing engine, which extracts important keywords. For example, "new product" and "proposal document" might be extracted.

[0689] Input and output

[0690] Input: User information request ("Please send me the new product proposal materials urgently!")

[0691] Output: List of extracted keywords ("New Product", "Proposal Document")

[0692] Step 4:

[0693] Generation of database search query

[0694] Specific operations

[0695] The server combines the extracted keywords to generate an SQL query. For example, a query like "SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'" is generated.

[0696] Input and output

[0697] Input: List of extracted keywords ("new product", "proposal materials")

[0698] Output: Generated SQL query ("SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'")

[0699] Step 5:

[0700] Search and ranking of materials

[0701] Specific operations

[0702] The server executes the generated SQL query to search the materials database. The search results are ranked in order of relevance. For example, make sure that "new product proposal materials_2023.pdf" and "proposal document_summary of new product.docx" come to the top.

[0703] Input and output

[0704] Input: Generated SQL query

[0705] Output: List of ranked search results ("new product proposal materials_2023.pdf", "proposal document_summary of new product.docx")

[0706] Step 6:

[0707] Proposing solutions based on emotions

[0708] Specific actions

[0709] The server will consider the results of the sentiment analysis and, in urgent cases, will submit an additional, simplified version of the proposal document.

[0710] Input and output

[0711] Input: Sentiment analysis results, list of ranked search results

[0712] Output: List of proposed solutions ("New Product Proposal Document_2023.pdf", "Proposal Document_New Product_Overview.docx", "Simplified Proposal Document")

[0713] Step 7:

[0714] Generating conversational response messages

[0715] Specific actions

[0716] The server generates conversational response messages based on a list of proposed solutions.

[0717] Input and output

[0718] Input: List of proposed solutions

[0719] Output: Generated conversational response message (Example: "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. For urgent requests, we also recommend the 'Simplified Proposal Document'.")

[0720] Step 8:

[0721] Sending and displaying response messages

[0722] Specific actions

[0723] The server sends a generated response message to the terminal. The terminal receives the response message and displays it to the user. The user selects the necessary documents and clicks the download button.

[0724] Input and output

[0725] Input: Generated conversational response messages

[0726] Output: Response message displayed on the terminal and user download action

[0727] (Application Example 2)

[0728] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0729] Traditional information request systems provided uniform responses without considering the user's emotional state, resulting in challenges with user satisfaction and efficiency. Furthermore, particularly in the food delivery sector, there was a lack of mechanisms to quickly suggest optimal menus tailored to the user's condition and emotions. Consequently, it was difficult to adequately meet user needs and improve satisfaction.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, and means for recognizing the user's emotional state using an emotion engine. This makes it possible to provide personalized materials or menus that take the user's emotions into consideration.

[0731] An "information request" refers to a request made by a user to obtain specific information or materials.

[0732] "Natural language processing" refers to the technology of analyzing language data entered by a user and understanding its meaning and intent.

[0733] "Keywords" refer to important words or phrases included in a user's information request.

[0734] An "emotion engine" refers to a technology that analyzes a user's text data to understand their emotional state and recognize those emotions.

[0735] "Emotional state" refers to the psychological state that can be inferred from the user's statements and written content.

[0736] "Documents" refers to documents and data files related to the information requested by the user.

[0737] In the context of food delivery, "menu" refers specifically to the range of meals available to the user.

[0738] "Searching" refers to finding relevant documents or menus from databases or information sources based on extracted keywords.

[0739] A "solution" refers to a specific solution or proposal provided in response to a user's information request.

[0740] "Conversational responses" refer to response messages returned to the user that take the form of a dialogue, and are messages exchanged between the user and the system.

[0741] A "terminal" refers to a device used by a user to input information requests or receive response messages.

[0742] The system realizing this invention recognizes the user's emotional state in the context of food delivery and provides personalized menu suggestions based on that emotion. The specific implementation method is described below.

[0743] System Configuration

[0744] This system consists of a server containing multiple modules for receiving and analyzing information requests from users and providing appropriate solutions, and terminals used by the users.

[0745] Hardware and software configuration

[0746] server:

[0747] A web server for receiving and analyzing information requests.

[0748] Emotion engines (e.g., IBM Watson Natural Language Understanding).

[0749] A natural language processing engine (e.g., Google Cloud Natural Language API).

[0750] Database management systems (e.g., MongoDB).

[0751] Dialogue systems (e.g., Dialogflow).

[0752] Terminal:

[0753] Smartphones and tablets used by users.

[0754] The application will be implemented as a mobile app that runs on iOS or Android.

[0755] Processing flow

[0756] 1. Receiving information requests from users

[0757] The user types "I'm tired today, I want food right away" into a smartphone application and sends it.

[0758] 2. Recognition of emotional states

[0759] The server uses an emotion engine to recognize the emotion of "tiredness" and analyze the user's urgent requests.

[0760] 3. Keyword extraction using natural language processing

[0761] Using a natural language processing engine, we extract the important keywords "tired," "immediately," and "food."

[0762] 4. Database Search

[0763] Based on the extracted keywords and emotional state, the system searches the database for relevant menu items. For example, it searches using the conditions "available for immediate delivery" and "easy to eat."

[0764] 5. Personalized solution proposals

[0765] Based on the search results, the system generates conversational response messages that take into account the user's emotional state. For example, it might generate messages like, "The following items are available for immediate delivery: pizza, sandwiches. We also recommend our salads, perfect for replenishing energy when you're feeling tired."

[0766] 6. Sending and displaying responses to the user

[0767] The generated response message is sent to the user's terminal, which then displays this message.

[0768] Specific example

[0769] Specific examples are given below.

[0770] The user types and submits "I'm tired today, I want food right away." The server then uses an emotion engine to recognize the emotional state of "tired" and a natural language processing engine to extract the keywords "tired," "immediately," and "food." Next, it searches its database for menus that are "immediately available for delivery" and "easy to eat," generating personalized suggestions such as "pizza," "sandwich," and "salad." Finally, these suggestions are sent to the user's device and displayed, allowing the user to quickly select appropriate food.

[0771] Example of a prompt

[0772] User input: "I'm tired today, I want food right away."

[0773] Emotion recognition result: "Fatigue"

[0774] Key keywords: "tired", "immediately", "food"

[0775] Search criteria: "Menus available for immediate delivery"

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The user enters and submits an information request. Once this information request is entered, the device sends this request to the server. For example, if the user enters "I'm tired today, I want food right away," the device transfers that text data to the server.

[0779] Step 2:

[0780] The server receives an information request from the user. The server receives the text data of the information request as input and prepares it as data to be analyzed for processing in the next step.

[0781] Step 3:

[0782] The server uses an emotion engine to recognize the user's emotional state from the received information request. At this time, the emotion engine analyzes emotional expressions such as "tired" from the text data and outputs "fatigue" as the emotional state.

[0783] Step 4:

[0784] The server uses a natural language processing engine to extract key keywords from the information request. This process extracts the keywords "tired," "immediately," and "food." The input is the request text data, and the output is a list of extracted keywords.

[0785] Step 5:

[0786] Based on the keywords extracted by the server and the recognized emotional state, a database management system is used to search for relevant materials or menus. In this process, the keyword list and emotional state are used as input, and a list of menus matching the search criteria is output.

[0787] Step 6:

[0788] The server ranks the menu items in order of relevance based on search results and sentiment. At this stage, relevance analysis is performed, and a ranked menu list is output.

[0789] Step 7:

[0790] The server generates personalized solutions for the user as conversational response messages based on a ranked menu list. The input is the ranked menu list, and the output is the generated conversational response messages.

[0791] Step 8:

[0792] The server sends a generated conversational response message to the terminal. Specifically, the response message is sent to the user's smartphone in the form of an email or notification.

[0793] Step 9:

[0794] The terminal displays the response message it received to the user. The user then checks the appropriate menu from the displayed message and makes a selection or order.

[0795] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0798] [Third Embodiment]

[0799] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0800] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0801] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0802] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0803] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0804] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0805] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0806] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0807] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0808] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0809] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0810] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0811] The embodiments for carrying out the present invention will be described in detail below.

[0812] This invention relates to a system for efficiently obtaining the materials requested by a user. The main components of the system include a terminal where the user enters their request, and a server that analyzes the request and searches for the materials.

[0813] Feature Overview

[0814] The system receives information requests from users and analyzes them using natural language processing. Based on the analyzed keywords, it searches for relevant materials and suggests combinations of solutions based on the search results. The generated suggestions are sent to the user as a conversational response. This conversational response is displayed to the user on their device, allowing them to review the necessary materials and download them as needed.

[0815] Program processing flow

[0816] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I want to find a proposal document for a new product," the keywords extracted will be "new product," "proposal document," and "search."

[0817] Next, the server searches the database for relevant documents based on these keywords. For example, the database may contain files such as "New Product Proposal Document_2023.pdf" and "Proposal_New Product_Overview.docx". The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0818] Furthermore, based on the search results, the server suggests a combination of solutions suitable for the user's request. For example, in the case of a new product proposal, it might suggest a set of resources such as a "product data sheet," "competitor analysis report," and "case studies." This suggestion is formatted as a conversational response.

[0819] The server generates a conversational response, which is then sent to the terminal. The terminal displays this response to the user, allowing the user to view a list of materials and proposals on the screen. When the user selects a specific material, the terminal downloads it and provides it to the user.

[0820] Specific example

[0821] Suppose a user is looking for a proposal document for a new product. When the user types "Find a proposal document for a new product" into their device and sends it, the device sends this request to the server. The server parses the request and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Along with these search results, the server suggests a combination of solutions that includes a "product data sheet," "competitor analysis report," and "case studies."

[0822] The server generates a conversational response summarizing this information and sends it to the terminal. The terminal displays this information to the user, who can then select and download the necessary documents from the displayed list.

[0823] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0824] The following describes the processing flow.

[0825] Step 1:

[0826] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Find me the proposal document for the new product."

[0827] Step 2:

[0828] The terminal sends a user information request to the server. The request content is transferred as text data.

[0829] Step 3:

[0830] The server passes the received information request to the natural language processing engine, which then begins the analysis.

[0831] Step 4:

[0832] The server uses a natural language processing engine to analyze the request and extract important keywords. For example, it might extract keywords such as "new product," "proposal document," and "search."

[0833] Step 5:

[0834] The server generates a database search query based on the keywords it extracts. This generated search query targets the data within the database.

[0835] Step 6:

[0836] The database is searched using a search query generated by the server. As a result of the search, relevant documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are obtained.

[0837] Step 7:

[0838] The server analyzes the search results and ranks them in order of relevance. This ensures that the most relevant information for the user is displayed at the top.

[0839] Step 8:

[0840] The server suggests combinations of relevant solutions based on the search results. For example, it might suggest sets of resources such as "product data sheets," "competitor analysis reports," and "case study collections."

[0841] Step 9:

[0842] The server generates the proposal as a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. Also, the recommended solution set is 'Product Datasheet', 'Competitive Analysis Report', and 'Case Studies'."

[0843] Step 10:

[0844] The server generates a conversational response message and sends it to the terminal.

[0845] Step 11:

[0846] The terminal displays conversational response messages received from the server to the user.

[0847] Step 12:

[0848] The user selects the necessary documents from the displayed list and clicks the download button.

[0849] Step 13:

[0850] The terminal downloads the selected document from the server and provides it to the user.

[0851] In this way, the system of the present invention efficiently processes information requests from users and quickly provides appropriate materials and solutions.

[0852] (Example 1)

[0853] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0854] Conventional information retrieval systems often make it difficult for users to quickly and accurately obtain specific documents or information resources they require. Furthermore, search results can be overwhelming and contain a lot of irrelevant information, meaning users may spend a considerable amount of time finding the information they need. Moreover, most systems simply provide documents without offering appropriate solutions. This leads to users having to go through a lot of trial and error before achieving satisfaction, hindering efficient information gathering. Therefore, the present invention aims to provide a system that efficiently retrieves the documents users require and quickly proposes appropriate solutions.

[0855] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0856] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for information resources based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, and means for downloading information resources selected by the user. This enables the user to efficiently obtain the desired materials and be quickly proposed appropriate solutions based on them.

[0857] An "information request" is a request or question that a user enters regarding specific information or materials they are looking for.

[0858] "Natural language processing" refers to the technologies and methods that enable computers to understand, interpret, and generate human language.

[0859] "Keywords" are the main words or phrases extracted from an information request that are used for searching.

[0860] An "information resource" is a collection of information, such as documents, files, and reports, stored in a database or other storage location.

[0861] A "combination of solutions" is a suggestion of a series of highly relevant information resources or methods for addressing a specific problem.

[0862] A "conversational response" is a type of response message that provides information to the user in a natural, dialogue-like format.

[0863] "Downloading means" refers to functions and methods for transferring information resources from a server to a terminal, enabling users to access and save them.

[0864] "Ranking" is the process of ordering search results based on their relevance and importance.

[0865] "List format" refers to a format in which search results or suggestions are organized and displayed in bullet points or lists.

[0866] A "terminal" is an electronic device used by a user to input information requests and to receive and display search results and solution responses.

[0867] The embodiments for carrying out the present invention will be described in detail below. This invention relates to a system that efficiently obtains the materials requested by a user and proposes appropriate solutions. The main components include a terminal where the user enters a request and a server that analyzes the request and searches for materials. The specific implementation method of this system will be described below.

[0868] System Configuration

[0869] The system is comprised of the following hardware and software.

[0870] hardware

[0871] 1. Terminal: An electronic device used by a user, such as a personal computer or smartphone.

[0872] 2. Server: A computer device that processes requests, searches databases, and generates responses.

[0873] software

[0874] 1. Natural Language Processing Engine: Google's Natural Language Processing API.

[0875] 2. Database management system: A database system such as MySQL.

[0876] 3. Communication protocol: HTTP and JSON format used for communication between the terminal and the server.

[0877] System operation

[0878] User operation

[0879] The user enters their information request in natural language into the input field on their device. For example, they might enter a request such as, "Find me the proposal document for the new product." The user then clicks the submit button to send this request to the server.

[0880] Server receives and parses requests

[0881] The server receives information requests sent from the terminal. These requests are received as text data in JSON format. The received requests are passed to a natural language processing engine (Google's Natural Language Processing API) and parsed. As a result of the parsing, important keywords are extracted. For example, the keywords "new product" and "proposal document" are extracted.

[0882] Searching for materials

[0883] The server searches the MySQL database based on the extracted keywords. It executes SQL queries to find relevant documents. For example, documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are found.

[0884] Solution proposal

[0885] Based on the search results, the server generates a combination of solutions that respond to the user's request. For example, a set of resources such as a "product data sheet," "competitor analysis report," and "case study collection" might be suggested. This suggestion is then formatted as a conversational response.

[0886] Response generation and transmission

[0887] The server generates a conversational response and sends it to the terminal in JSON format. This response includes a list of retrieved materials and a set of proposed solutions.

[0888] Displaying the response from the terminal

[0889] The terminal analyzes the response received from the server and displays it in a user-friendly format. Users can view document lists and proposals on the screen.

[0890] Download materials

[0891] When a user selects a specific document, the device sends a download request for that document to the server. The server then sends the selected document to the device, allowing the user to download and view it.

[0892] Specific example

[0893] Suppose a user is looking for a proposal document for a new product. The user types "Find a proposal document for a new product" into their device and clicks the send button. The device sends this request to the server in JSON format. The server receives the request, parses it using a natural language processing engine, and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Furthermore, the server suggests a solution combination that includes a "product data sheet," "competitor analysis report," and "case studies." The server generates a conversational response summarizing this information and sends it to the device. The device displays this information to the user, who can select and download the necessary documents from the displayed list.

[0894] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[0895] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0896] Step 1:

[0897] The user enters an information request into the terminal. They enter natural language text about the documents or information they are seeking into the input field and click the submit button. The input is in text format, such as a prompt like "Find new product proposal documents." The output is the user's information request text.

[0898] Step 2:

[0899] The terminal sends an information request to the server. The terminal receives the user's request and sends it to the server as an HTTP POST request. This request is formatted in JSON format. The input is the text of the information request entered by the user, and the output is the JSON data sent to the server.

[0900] Step 3:

[0901] The server receives and parses the information request. The server receives the information request sent from the terminal and passes it to a natural language processing engine (e.g., a natural language processing API). The input is JSON data received from the terminal, and the output is the result of the analysis by the natural language processing engine.

[0902] Step 4:

[0903] The server extracts relevant keywords. The natural language processing engine then extracts important keywords from the results of its analysis. The input is the analysis results of the natural language processing engine, and the output is a set of extracted keywords. Specifically, keywords such as "new product" and "proposal document" are obtained.

[0904] Step 5:

[0905] The server searches the database to retrieve documents. The server searches the MySQL database using SQL queries based on the extracted keywords. The input is a set of extracted keywords, and the output is a list of related documents. For example, search results for "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" can be obtained.

[0906] Step 6:

[0907] The server generates a suitable combination of solutions. Based on the search results, the server proposes a combination of solutions that meets the user's request. The input is a list of documents retrieved from the database, and the output is a set of solutions. For example, combinations such as "product data sheets," "competitor analysis reports," and "case studies" are generated.

[0908] Step 7:

[0909] The server generates a response and sends it to the terminal. Based on the generated solution set and search results, the server creates a conversational response. The response is formatted in JSON format for easy understanding by the user and sent to the terminal. The input is the solution set and search results, and the output is the JSON data sent to the terminal.

[0910] Step 8:

[0911] The terminal displays the response to the user. The terminal parses the JSON data received from the server and displays the information in a user-friendly format. The input is the JSON data received from the server, and the output is a set of documents and solutions displayed to the user.

[0912] Step 9:

[0913] The user selects and downloads the necessary documents. The user clicks to select the desired documents from the displayed list. The terminal sends a download request to the server based on the selection. The server sends the selected documents to the terminal in response to the request, and the user can download and view the documents. The input is the documents selected by the user, and the output is the downloaded document file.

[0914] (Application Example 1)

[0915] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0916] The goal is to address the challenge of customers lacking an efficient way to acquire products and information in physical stores by providing a system that improves in-store engagement using smart glasses. Traditional methods often resulted in customers spending a considerable amount of time searching for products and requiring assistance from store staff.

[0917] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0918] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, means for the user to input a request using smart glasses in a physical store, and means for presenting products and information based on the request. This makes it possible for customers to efficiently obtain the products and information they are looking for.

[0919] "Means for receiving information requests from users" refers to devices or software that receive voice or text requests entered by users using smart glasses.

[0920] "Means for analyzing and extracting keywords using natural language processing" refers to devices or software that utilize natural language processing techniques to analyze received requests and identify important keywords or phrases.

[0921] "Means for searching for materials based on extracted keywords" refers to devices or software that use extracted keywords to search for related materials and information from a database.

[0922] "Means of suggesting combinations of related solutions" refers to devices or software that suggest the optimal set of materials and information to respond to a user's request based on search results.

[0923] "A means of generating and sending proposed content as a conversational response to the user" refers to a device or software that compiles proposed materials and information into a natural conversational format and sends it to the user.

[0924] "Means of displaying conversational responses to the user" refers to devices or software that display conversational responses to the user through smart glasses or other terminals.

[0925] "A means for users to input requests using smart glasses in a physical store" refers to devices and software that allow customers to input requests via voice or text through smart glasses within an actual store.

[0926] "Means of presenting products and information based on requests" refers to devices or software that display relevant products and information based on the user's request.

[0927] To implement this invention, a terminal including smart glasses, a server that analyzes requests and searches for data, and an application installed on the smart glasses are required. Specifically, the embodiments for implementing the invention are described below.

[0928] System Configuration

[0929] The main components are as follows:

[0930] 1. Smart glasses: A device for users to input requests and display information.

[0931] 2. Server: The central processing unit that parses requests, searches for materials, and provides relevant information.

[0932] 3. Natural Language Processing Engine: Software that analyzes user requests and extracts keywords (e.g., Google Cloud Natural Language API).

[0933] 4. Document search server: Software that searches for documents in a database based on extracted keywords (e.g., Elasticsearch).

[0934] Program processing flow

[0935] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I'm looking for a new smartphone," the keywords "new" and "smartphone" will be extracted.

[0936] Next, the server searches for relevant products and information from the data retrieval server based on these keywords. The search results are ranked in order of relevance, and a list is generated to provide to the user.

[0937] Furthermore, the server suggests the best product combinations for the user's request based on the search results. For example, if the user is looking for a "new smartphone," the server might suggest a set including "the latest smartphone model," "special offers," and "accessories." This suggestion is formatted as a conversational response.

[0938] The conversational responses generated by the server are sent to the smart glasses. The smart glasses display these responses to the user, allowing the user to view product lists and suggestions on the screen. When the user selects a specific document or product, that information is displayed in detail.

[0939] Specific example

[0940] For example, a user might voice-input "I'm looking for a new smartphone" through smart glasses. This request is sent to the server, where a natural language processing engine extracts the keywords "new" and "smartphone." The server then uses a data search server to search for related product information and retrieves a list of smartphones, such as "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB." This list is presented to the user in a conversational format, for example, "Here are some recommended products: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0941] Example of a prompt

[0942] When a user enters "I'm looking for a new smartphone" into their smart glasses, the system uses an NLP process to extract the keywords "new" and "smartphone," and then retrieves related products from a search server based on those keywords. Related products include "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB," among others. The results are as follows:

[0943] Our recommended products are as follows:

[0944] iPhone 13 - 128GB

[0945] Samsung Galaxy S21 - 256GB

[0946] In this way, the present invention realizes a system that allows users to efficiently obtain the products and materials they desire and to quickly propose appropriate solutions based on them.

[0947] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0948] Step 1:

[0949] Receiving a request

[0950] The user enters a request into the smart glasses via voice or text. For example, the request might say, "I'm looking for a new smartphone." This request is then sent from the smart glasses to the server.

[0951] Input: User's voice or text request

[0952] Output: Request data sent to the server

[0953] Step 2:

[0954] Request parsing

[0955] The server passes the received request to the natural language processing engine. The natural language processing engine analyzes the request and extracts important keywords. For example, the keywords "new" and "smartphone" might be extracted.

[0956] Input: Received request data

[0957] Output: Extracted keywords (e.g., "new", "smartphone")

[0958] Step 3:

[0959] Searching for materials

[0960] The server uses the extracted keywords to send a query to the data retrieval server. The data retrieval server searches the database and retrieves relevant products and information. For example, it might retrieve a list of smartphones such as "iPhone 13 - 128GB" or "Samsung Galaxy S21 - 256GB".

[0961] Input: Extracted keywords

[0962] Output: Search results (e.g., "iPhone 13 - 128GB", "Samsung Galaxy S21 - 256GB")

[0963] Step 4:

[0964] Response generation

[0965] The server generates conversational responses to provide to the user based on the search results. For example, it might generate text such as, "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[0966] Input: Search Results

[0967] Output: Conversational response (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0968] Step 5:

[0969] Sending and displaying responses

[0970] The server sends the generated conversational response to the smart glasses. The smart glasses display the received response to the user. The user can review the information displayed on the smart glasses' screen and select the products or information they need.

[0971] Input: Conversational response

[0972] Output: Response displayed on smart glasses (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[0973] By executing these processing steps sequentially, users can efficiently obtain the products and information they need using smart glasses in physical stores.

[0974] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0975] The embodiments for carrying out the present invention will be described in detail below.

[0976] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. The system includes a function that provides personalized responses based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0977] Feature Overview

[0978] The system receives information requests from users and analyzes them using natural language processing and an emotion engine. Based on the analysis results, it searches for materials, ranks the search results in order of relevance, and suggests a combination of solutions that correspond to the user's emotions. The generated suggestions are sent to the user as a conversational response and displayed on their device.

[0979] Program processing flow

[0980] When the server receives an information request from a user, it first uses an emotion engine to analyze the user's emotional state. It analyzes emotional expressions, characters, and punctuation in the request text to determine the user's emotional state. For example, if a user enters "Send me the proposal document for the new product urgently!", the expression "urgently" will indicate an emotion of urgency.

[0981] Next, the server uses a natural language processing engine to analyze the request and extract important keywords. For example, keywords such as "new product," "proposal document," and "send" might be extracted.

[0982] Next, the server generates a database search query based on the extracted keywords. Using this search query, the server searches for documents within the database. For example, the database may contain documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx," and these documents will be searched. The server ranks the search results in order of relevance, displaying the most relevant documents to the user at the top.

[0983] Furthermore, the server considers the results of the emotion engine's analysis to suggest an appropriate combination of solutions. For example, if the emotion of anxiety is recognized, it can prioritize presenting materials and simplified proposals for a quick response.

[0984] The server compiles this information and generates a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[0985] The generated conversational response messages are sent to the terminal. The terminal displays this information to the user, who then selects the necessary documents from the displayed list and clicks the download button.

[0986] Specific example

[0987] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first uses an emotion engine to analyze the expression "urgent" to determine the user's urgency. Then, using a natural language processing engine, it extracts "new product" and "proposal document" as keywords and searches the database for "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". In addition, it suggests a "simplified version of the proposal document" for urgent use.

[0988] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select the necessary materials and download them quickly.

[0989] In this way, the system of the present invention recognizes the user's emotions and provides personalized information and solution suggestions based on those emotions, thereby improving the user experience.

[0990] The following describes the processing flow.

[0991] Step 1:

[0992] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Please send me the proposal document for the new product urgently!"

[0993] Step 2:

[0994] The terminal sends a user information request to the server. The request content is transferred as text data.

[0995] Step 3:

[0996] The server passes the received information request to the emotion engine, which then begins analyzing the emotional state. For example, it might detect the emotion of impatience from the expression "hurry up."

[0997] Step 4:

[0998] The server passes the request text to a natural language processing engine for analysis. Specifically, the natural language processing engine analyzes the request text and extracts important keywords such as "new product," "proposal document," and "send."

[0999] Step 5:

[1000] The server generates a search query based on the extracted keywords and searches the database. This search finds documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx".

[1001] Step 6:

[1002] The server ranks the search results in order of relevance. For example, newer materials and materials containing many relevant keywords will be ranked higher.

[1003] Step 7:

[1004] The server combines the search results with analysis results from the emotion engine to propose the most suitable solution. If the emotion of anxiety is detected, it prioritizes suggesting materials that allow for quick response or simplified versions of materials.

[1005] Step 8:

[1006] The server generates a conversational response message based on the proposal. For example, it might generate a message like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal Document_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[1007] Step 9:

[1008] The server generates a conversational response message and sends it to the terminal.

[1009] Step 10:

[1010] The terminal displays conversational response messages received from the server to the user.

[1011] Step 11:

[1012] The user selects the necessary documents from the displayed list and clicks the download button.

[1013] Step 12:

[1014] The terminal downloads the selected document from the server and provides it to the user.

[1015] In this way, the system recognizes the user's emotions and quickly provides customized materials and solutions based on them.

[1016] (Example 2)

[1017] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1018] Conventional information request processing systems provide information uniformly without considering the user's emotional state, making it difficult to respond quickly and appropriately to user needs. In particular, when a user is in a hurry or experiencing specific emotions, a response tailored to those emotions is required. Furthermore, finding necessary information from a large amount of data can be time-consuming, potentially detracting from the user experience.

[1019] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1020] In this invention, the server includes means for receiving information requests from users, means for analyzing the user's emotional state using sentiment analysis based on the received information request, means for analyzing the information request using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for ranking the searched materials in order of relevance, means for suggesting combinations of relevant solutions considering the results of the sentiment analysis, means for generating the suggested content as a conversational response and sending it to the user, and means for displaying the conversational response to the user. This enables personalized responses based on emotional state and rapid provision of materials.

[1021] An "information request" is a request that a user submits to obtain specific documents or information.

[1022] "Sentiment analysis" is a technology that identifies and analyzes the emotional state of a user from their input text.

[1023] "Natural language processing" is a technology that allows computers to analyze, understand, and generate natural human language.

[1024] Keyword extraction is the process of identifying and extracting important words and phrases from a document.

[1025] "Document search" is the process of finding documents within a database based on specified keywords.

[1026] "Ranking by relevance" is the process of rearranging search results in order of how well they match the user's requirements.

[1027] A "combination of solutions" is a set of suggestions or answers that address a user's problems or needs.

[1028] A "conversational response" is a response in the form of automatically generated documents or messages, similar to a conversation between humans.

[1029] "Sending to user" refers to the process of transferring responses and materials generated by the server to the user's terminal.

[1030] "Displaying to the user" refers to the process of visually displaying information received on the user's device.

[1031] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. This system includes a function that provides personalized responses based on the user's emotional state by incorporating an emotion engine that recognizes the user's emotions.

[1032] Hardware and software configuration

[1033] server

[1034] The server is responsible for receiving requests, sentiment analysis, natural language processing, data retrieval, ranking, solution suggestions, response generation, and message sending. A sentiment engine is used for sentiment analysis, and a natural language processing engine is used for natural language processing. Data is stored in the database, and data is retrieved using search queries.

[1035] terminal

[1036] The terminal is responsible for receiving information requests from users and sending them to the server. The terminal also displays response messages received from the server and provides an interface for users to select and download materials.

[1037] Data processing and data calculation

[1038] Receiving information requests and sentiment analysis

[1039] When a user enters an information request into their device and clicks the send button, the device sends this request to the server as an HTTP request. The server passes the received request to the sentiment engine, which analyzes emotional expressions, punctuation, emphasized keywords, and other relevant information.

[1040] Natural language processing and keyword extraction

[1041] Next, the server passes the request sentence to the natural language processing engine. The natural language processing engine analyzes the request sentence and extracts important keywords. As a result, keywords such as "new product" and "proposal materials" are extracted.

[1042] Search query generation and document search

[1043] The server combines the extracted keywords to generate a search query. For example, it generates a query such as "SELECT FROM documents WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'", and performs a search on the document database.

[1044] Ranking and solution proposal

[1045] When the search results are obtained, the server ranks these documents in order of relevance. For the ranked documents, the server considers the analysis results of the sentiment engine and proposes appropriate solutions. For example, for urgent requests, it prioritizes simplified proposal documents.

[1046] Generation and transmission of a response in a conversation format

[1047] The server generates a response message in a conversation format based on the results of ranking and solution proposal. The generated response message is encoded in JSON format and sent to the terminal as an HTTP response.

[1048] Display of the response message and download of the document

[1049] The terminal decodes the response message received from the server and displays it to the user. The user selects the necessary document and clicks the download button to obtain the document.

[1050] Specific example

[1051] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first performs sentiment analysis. It recognizes the emotion of urgency from the expression "urgently," and then uses a natural language processing engine to extract "new product" and "proposal document" as keywords. The server generates a search query and searches the database for "new product proposal document_2023.pdf" and "proposal_new_product_overview.docx." In addition, it suggests a "simplified version of the proposal document" for urgent use.

[1052] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select and quickly download the necessary materials.

[1053] Example of a prompt

[1054] Please send the proposal document for the new product as soon as possible.

[1055] I'm looking for the latest proposals for a new product. Can you help me?

[1056] I urgently need a simplified version of the proposal document.

[1057] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1058] Step 1:

[1059] Receiving a user information request

[1060] Specific actions

[1061] The user types "Send me the new product proposal quickly!" into their device and clicks the send button.

[1062] Input and output

[1063] Input: User information request ("Please send me the new product proposal materials urgently!")

[1064] Output: The terminal sends the request to the server in HTTP request format.

[1065] Step 2:

[1066] Analyze the user's emotional state.

[1067] Specific actions

[1068] The server passes the received request to the emotion engine. The emotion engine analyzes the keyword "hurry" and recognizes the emotion of urgency.

[1069] Input and output

[1070] Input: User information request ("Please send me the new product proposal materials urgently!")

[1071] Output: Emotion analysis results (emotion of anxiety is recognized)

[1072] Step 3:

[1073] Request text analysis and keyword extraction

[1074] Specific actions

[1075] The server passes the request to a natural language processing engine, which extracts important keywords. For example, "new product" and "proposal document" might be extracted.

[1076] Input and output

[1077] Input: User information request ("Please send me the new product proposal materials urgently!")

[1078] Output: List of extracted keywords ("New Product", "Proposal Document")

[1079] Step 4:

[1080] Generation of database search query

[1081] Specific operations

[1082] The server combines the extracted keywords to generate an SQL query. For example, a query like "SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'" is generated.

[1083] Input and output

[1084] Input: List of extracted keywords ("new product", "proposal materials")

[1085] Output: Generated SQL query ("SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'")

[1086] Step 5:

[1087] Search and ranking of materials

[1088] Specific operations

[1089] The server executes the generated SQL query to search the materials database. The search results are ranked in order of relevance. For example, make sure that "new product proposal materials_2023.pdf" and "proposal document_summary of new product.docx" come to the top.

[1090] Input and output

[1091] Input: Generated SQL query

[1092] Output: List of ranked search results ("new product proposal materials_2023.pdf", "proposal document_summary of new product.docx")

[1093] Step 6:

[1094] Proposing solutions based on emotions

[1095] Specific actions

[1096] The server will consider the results of the sentiment analysis and, in urgent cases, will submit an additional, simplified version of the proposal document.

[1097] Input and output

[1098] Input: Sentiment analysis results, list of ranked search results

[1099] Output: List of proposed solutions ("New Product Proposal Document_2023.pdf", "Proposal Document_New Product_Overview.docx", "Simplified Proposal Document")

[1100] Step 7:

[1101] Generating conversational response messages

[1102] Specific actions

[1103] The server generates conversational response messages based on a list of proposed solutions.

[1104] Input and output

[1105] Input: List of proposed solutions

[1106] Output: Generated conversational response message (Example: "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. For urgent requests, we also recommend the 'Simplified Proposal Document'.")

[1107] Step 8:

[1108] Sending and displaying response messages

[1109] Specific actions

[1110] The server sends a generated response message to the terminal. The terminal receives the response message and displays it to the user. The user selects the necessary documents and clicks the download button.

[1111] Input and output

[1112] Input: Generated conversational response messages

[1113] Output: Response message displayed on the terminal and user download action

[1114] (Application Example 2)

[1115] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1116] Traditional information request systems provided uniform responses without considering the user's emotional state, resulting in challenges with user satisfaction and efficiency. Furthermore, particularly in the food delivery sector, there was a lack of mechanisms to quickly suggest optimal menus tailored to the user's condition and emotions. Consequently, it was difficult to adequately meet user needs and improve satisfaction.

[1117] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, and means for recognizing the user's emotional state using an emotion engine. This makes it possible to provide personalized materials or menus that take the user's emotions into consideration.

[1118] An "information request" refers to a request made by a user to obtain specific information or materials.

[1119] "Natural language processing" refers to the technology of analyzing language data entered by a user and understanding its meaning and intent.

[1120] "Keywords" refer to important words or phrases included in a user's information request.

[1121] An "emotion engine" refers to a technology that analyzes a user's text data to understand their emotional state and recognize those emotions.

[1122] "Emotional state" refers to the psychological state that can be inferred from the user's statements and written content.

[1123] "Documents" refers to documents and data files related to the information requested by the user.

[1124] In the context of food delivery, "menu" refers specifically to the range of meals available to the user.

[1125] "Searching" refers to finding relevant documents or menus from databases or information sources based on extracted keywords.

[1126] A "solution" refers to a specific solution or proposal provided in response to a user's information request.

[1127] "Conversational responses" refer to response messages returned to the user that take the form of a dialogue, and are messages exchanged between the user and the system.

[1128] A "terminal" refers to a device used by a user to input information requests or receive response messages.

[1129] The system realizing this invention recognizes the user's emotional state in the context of food delivery and provides personalized menu suggestions based on that emotion. The specific implementation method is described below.

[1130] System Configuration

[1131] This system consists of a server containing multiple modules for receiving and analyzing information requests from users and providing appropriate solutions, and terminals used by the users.

[1132] Hardware and software configuration

[1133] server:

[1134] A web server for receiving and analyzing information requests.

[1135] Emotion engines (e.g., IBM Watson Natural Language Understanding).

[1136] A natural language processing engine (e.g., Google Cloud Natural Language API).

[1137] Database management systems (e.g., MongoDB).

[1138] Dialogue systems (e.g., Dialogflow).

[1139] Terminal:

[1140] Smartphones and tablets used by users.

[1141] The application will be implemented as a mobile app that runs on iOS or Android.

[1142] Processing flow

[1143] 1. Receiving information requests from users

[1144] The user types "I'm tired today, I want food right away" into a smartphone application and sends it.

[1145] 2. Recognition of emotional states

[1146] The server uses an emotion engine to recognize the emotion of "tiredness" and analyze the user's urgent requests.

[1147] 3. Keyword extraction using natural language processing

[1148] Using a natural language processing engine, we extract the important keywords "tired," "immediately," and "food."

[1149] 4. Database Search

[1150] Based on the extracted keywords and emotional state, the system searches the database for relevant menu items. For example, it searches using the conditions "available for immediate delivery" and "easy to eat."

[1151] 5. Personalized solution proposals

[1152] Based on the search results, the system generates conversational response messages that take into account the user's emotional state. For example, it might generate messages like, "The following items are available for immediate delivery: pizza, sandwiches. We also recommend our salads, perfect for replenishing energy when you're feeling tired."

[1153] 6. Sending and displaying responses to the user

[1154] The generated response message is sent to the user's terminal, which then displays this message.

[1155] Specific example

[1156] Specific examples are given below.

[1157] The user types and submits "I'm tired today, I want food right away." The server then uses an emotion engine to recognize the emotional state of "tired" and a natural language processing engine to extract the keywords "tired," "immediately," and "food." Next, it searches its database for menus that are "immediately available for delivery" and "easy to eat," generating personalized suggestions such as "pizza," "sandwich," and "salad." Finally, these suggestions are sent to the user's device and displayed, allowing the user to quickly select appropriate food.

[1158] Example of a prompt

[1159] User input: "I'm tired today, I want food right away."

[1160] Emotion recognition result: "Fatigue"

[1161] Key keywords: "tired", "immediately", "food"

[1162] Search criteria: "Menus available for immediate delivery"

[1163] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1164] Step 1:

[1165] The user enters and submits an information request. Once this information request is entered, the device sends this request to the server. For example, if the user enters "I'm tired today, I want food right away," the device transfers that text data to the server.

[1166] Step 2:

[1167] The server receives an information request from the user. The server receives the text data of the information request as input and prepares it as data to be analyzed for processing in the next step.

[1168] Step 3:

[1169] The server uses an emotion engine to recognize the user's emotional state from the received information request. At this time, the emotion engine analyzes emotional expressions such as "tired" from the text data and outputs "fatigue" as the emotional state.

[1170] Step 4:

[1171] The server uses a natural language processing engine to extract key keywords from the information request. This process extracts the keywords "tired," "immediately," and "food." The input is the request text data, and the output is a list of extracted keywords.

[1172] Step 5:

[1173] Based on the keywords extracted by the server and the recognized emotional state, a database management system is used to search for relevant materials or menus. In this process, the keyword list and emotional state are used as input, and a list of menus matching the search criteria is output.

[1174] Step 6:

[1175] The server ranks the menu items in order of relevance based on search results and sentiment. At this stage, relevance analysis is performed, and a ranked menu list is output.

[1176] Step 7:

[1177] The server generates personalized solutions for the user as conversational response messages based on a ranked menu list. The input is the ranked menu list, and the output is the generated conversational response messages.

[1178] Step 8:

[1179] The server sends a generated conversational response message to the terminal. Specifically, the response message is sent to the user's smartphone in the form of an email or notification.

[1180] Step 9:

[1181] The terminal displays the response message it received to the user. The user then checks the appropriate menu from the displayed message and makes a selection or order.

[1182] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1183] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1184] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1185] [Fourth Embodiment]

[1186] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1187] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1188] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1190] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1192] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1193] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1194] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1195] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1196] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1197] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1198] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1199] The embodiments for carrying out the present invention will be described in detail below.

[1200] This invention relates to a system for efficiently obtaining the materials requested by a user. The main components of the system include a terminal where the user enters their request, and a server that analyzes the request and searches for the materials.

[1201] Feature Overview

[1202] The system receives information requests from users and analyzes them using natural language processing. Based on the analyzed keywords, it searches for relevant materials and suggests combinations of solutions based on the search results. The generated suggestions are sent to the user as a conversational response. This conversational response is displayed to the user on their device, allowing them to review the necessary materials and download them as needed.

[1203] Program processing flow

[1204] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I want to find a proposal document for a new product," the keywords extracted will be "new product," "proposal document," and "search."

[1205] Next, the server searches the database for relevant documents based on these keywords. For example, the database may contain files such as "New Product Proposal Document_2023.pdf" and "Proposal_New Product_Overview.docx". The search results are ranked in order of relevance, and a list is generated to provide to the user.

[1206] Furthermore, based on the search results, the server suggests a combination of solutions suitable for the user's request. For example, in the case of a new product proposal, it might suggest a set of resources such as a "product data sheet," "competitor analysis report," and "case studies." This suggestion is formatted as a conversational response.

[1207] The server generates a conversational response, which is then sent to the terminal. The terminal displays this response to the user, allowing the user to view a list of materials and proposals on the screen. When the user selects a specific material, the terminal downloads it and provides it to the user.

[1208] Specific example

[1209] Suppose a user is looking for a proposal document for a new product. When the user types "Find a proposal document for a new product" into their device and sends it, the device sends this request to the server. The server parses the request and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Along with these search results, the server suggests a combination of solutions that includes a "product data sheet," "competitor analysis report," and "case studies."

[1210] The server generates a conversational response summarizing this information and sends it to the terminal. The terminal displays this information to the user, who can then select and download the necessary documents from the displayed list.

[1211] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[1212] The following describes the processing flow.

[1213] Step 1:

[1214] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Find me the proposal document for the new product."

[1215] Step 2:

[1216] The terminal sends a user information request to the server. The request content is transferred as text data.

[1217] Step 3:

[1218] The server passes the received information request to the natural language processing engine, which then begins the analysis.

[1219] Step 4:

[1220] The server uses a natural language processing engine to analyze the request and extract important keywords. For example, it might extract keywords such as "new product," "proposal document," and "search."

[1221] Step 5:

[1222] The server generates a database search query based on the keywords it extracts. This generated search query targets the data within the database.

[1223] Step 6:

[1224] The database is searched using a search query generated by the server. As a result of the search, relevant documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are obtained.

[1225] Step 7:

[1226] The server analyzes the search results and ranks them in order of relevance. This ensures that the most relevant information for the user is displayed at the top.

[1227] Step 8:

[1228] The server suggests combinations of relevant solutions based on the search results. For example, it might suggest sets of resources such as "product data sheets," "competitor analysis reports," and "case study collections."

[1229] Step 9:

[1230] The server generates the proposal as a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. Also, the recommended solution set is 'Product Datasheet', 'Competitive Analysis Report', and 'Case Studies'."

[1231] Step 10:

[1232] The server generates a conversational response message and sends it to the terminal.

[1233] Step 11:

[1234] The terminal displays conversational response messages received from the server to the user.

[1235] Step 12:

[1236] The user selects the necessary documents from the displayed list and clicks the download button.

[1237] Step 13:

[1238] The terminal downloads the selected document from the server and provides it to the user.

[1239] In this way, the system of the present invention efficiently processes information requests from users and quickly provides appropriate materials and solutions.

[1240] (Example 1)

[1241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1242] Conventional information retrieval systems often make it difficult for users to quickly and accurately obtain specific documents or information resources they require. Furthermore, search results can be overwhelming and contain a lot of irrelevant information, meaning users may spend a considerable amount of time finding the information they need. Moreover, most systems simply provide documents without offering appropriate solutions. This leads to users having to go through a lot of trial and error before achieving satisfaction, hindering efficient information gathering. Therefore, the present invention aims to provide a system that efficiently retrieves the documents users require and quickly proposes appropriate solutions.

[1243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1244] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for information resources based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, and means for downloading information resources selected by the user. This enables the user to efficiently obtain the desired materials and be quickly proposed appropriate solutions based on them.

[1245] An "information request" is a request or question that a user enters regarding specific information or materials they are looking for.

[1246] "Natural language processing" refers to the technologies and methods that enable computers to understand, interpret, and generate human language.

[1247] "Keywords" are the main words or phrases extracted from an information request that are used for searching.

[1248] An "information resource" is a collection of information, such as documents, files, and reports, stored in a database or other storage location.

[1249] A "combination of solutions" is a suggestion of a series of highly relevant information resources or methods for addressing a specific problem.

[1250] A "conversational response" is a type of response message that provides information to the user in a natural, dialogue-like format.

[1251] "Downloading means" refers to functions and methods for transferring information resources from a server to a terminal, enabling users to access and save them.

[1252] "Ranking" is the process of ordering search results based on their relevance and importance.

[1253] "List format" refers to a format in which search results or suggestions are organized and displayed in bullet points or lists.

[1254] A "terminal" is an electronic device used by a user to input information requests and to receive and display search results and solution responses.

[1255] The embodiments for carrying out the present invention will be described in detail below. This invention relates to a system that efficiently obtains the materials requested by a user and proposes appropriate solutions. The main components include a terminal where the user enters a request and a server that analyzes the request and searches for materials. The specific implementation method of this system will be described below.

[1256] System Configuration

[1257] The system is comprised of the following hardware and software.

[1258] hardware

[1259] 1. Terminal: An electronic device used by a user, such as a personal computer or smartphone.

[1260] 2. Server: A computer device that processes requests, searches databases, and generates responses.

[1261] software

[1262] 1. Natural Language Processing Engine: Google's Natural Language Processing API.

[1263] 2. Database management system: A database system such as MySQL.

[1264] 3. Communication protocol: HTTP and JSON format used for communication between the terminal and the server.

[1265] System operation

[1266] User operation

[1267] The user enters their information request in natural language into the input field on their device. For example, they might enter a request such as, "Find me the proposal document for the new product." The user then clicks the submit button to send this request to the server.

[1268] Server receives and parses requests

[1269] The server receives information requests sent from the terminal. These requests are received as text data in JSON format. The received requests are passed to a natural language processing engine (Google's Natural Language Processing API) and parsed. As a result of the parsing, important keywords are extracted. For example, the keywords "new product" and "proposal document" are extracted.

[1270] Searching for materials

[1271] The server searches the MySQL database based on the extracted keywords. It executes SQL queries to find relevant documents. For example, documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" are found.

[1272] Solution proposal

[1273] Based on the search results, the server generates a combination of solutions that respond to the user's request. For example, a set of resources such as a "product data sheet," "competitor analysis report," and "case study collection" might be suggested. This suggestion is then formatted as a conversational response.

[1274] Response generation and transmission

[1275] The server generates a conversational response and sends it to the terminal in JSON format. This response includes a list of retrieved materials and a set of proposed solutions.

[1276] Displaying the response from the terminal

[1277] The terminal analyzes the response received from the server and displays it in a user-friendly format. Users can view document lists and proposals on the screen.

[1278] Download materials

[1279] When a user selects a specific document, the device sends a download request for that document to the server. The server then sends the selected document to the device, allowing the user to download and view it.

[1280] Specific example

[1281] Suppose a user is looking for a proposal document for a new product. The user types "Find a proposal document for a new product" into their device and clicks the send button. The device sends this request to the server in JSON format. The server receives the request, parses it using a natural language processing engine, and extracts "new product" and "proposal document" as keywords. The server then searches its database and finds "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". Furthermore, the server suggests a solution combination that includes a "product data sheet," "competitor analysis report," and "case studies." The server generates a conversational response summarizing this information and sends it to the device. The device displays this information to the user, who can select and download the necessary documents from the displayed list.

[1282] In this way, the present invention realizes a system that allows users to efficiently obtain the materials they need and to quickly propose appropriate solutions based on those materials.

[1283] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1284] Step 1:

[1285] The user enters an information request into the terminal. They enter natural language text about the documents or information they are seeking into the input field and click the submit button. The input is in text format, such as a prompt like "Find new product proposal documents." The output is the user's information request text.

[1286] Step 2:

[1287] The terminal sends an information request to the server. The terminal receives the user's request and sends it to the server as an HTTP POST request. This request is formatted in JSON format. The input is the text of the information request entered by the user, and the output is the JSON data sent to the server.

[1288] Step 3:

[1289] The server receives and parses the information request. The server receives the information request sent from the terminal and passes it to a natural language processing engine (e.g., a natural language processing API). The input is JSON data received from the terminal, and the output is the result of the analysis by the natural language processing engine.

[1290] Step 4:

[1291] The server extracts relevant keywords. The natural language processing engine then extracts important keywords from the results of its analysis. The input is the analysis results of the natural language processing engine, and the output is a set of extracted keywords. Specifically, keywords such as "new product" and "proposal document" are obtained.

[1292] Step 5:

[1293] The server searches the database to retrieve documents. The server searches the MySQL database using SQL queries based on the extracted keywords. The input is a set of extracted keywords, and the output is a list of related documents. For example, search results for "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx" can be obtained.

[1294] Step 6:

[1295] The server generates a suitable combination of solutions. Based on the search results, the server proposes a combination of solutions that meets the user's request. The input is a list of documents retrieved from the database, and the output is a set of solutions. For example, combinations such as "product data sheets," "competitor analysis reports," and "case studies" are generated.

[1296] Step 7:

[1297] The server generates a response and sends it to the terminal. Based on the generated solution set and search results, the server creates a conversational response. The response is formatted in JSON format for easy understanding by the user and sent to the terminal. The input is the solution set and search results, and the output is the JSON data sent to the terminal.

[1298] Step 8:

[1299] The terminal displays the response to the user. The terminal parses the JSON data received from the server and displays the information in a user-friendly format. The input is the JSON data received from the server, and the output is a set of documents and solutions displayed to the user.

[1300] Step 9:

[1301] The user selects and downloads the necessary documents. The user clicks to select the desired documents from the displayed list. The terminal sends a download request to the server based on the selection. The server sends the selected documents to the terminal in response to the request, and the user can download and view the documents. The input is the documents selected by the user, and the output is the downloaded document file.

[1302] (Application Example 1)

[1303] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1304] The goal is to address the challenge of customers lacking an efficient way to acquire products and information in physical stores by providing a system that improves in-store engagement using smart glasses. Traditional methods often resulted in customers spending a considerable amount of time searching for products and requiring assistance from store staff.

[1305] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1306] In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for proposing combinations of relevant solutions based on the search results, means for generating the proposed content as a conversational response and sending it to the user, means for displaying the conversational response to the user, means for the user to input a request using smart glasses in a physical store, and means for presenting products and information based on the request. This makes it possible for customers to efficiently obtain the products and information they are looking for.

[1307] "Means for receiving information requests from users" refers to devices or software that receive voice or text requests entered by users using smart glasses.

[1308] "Means for analyzing and extracting keywords using natural language processing" refers to devices or software that utilize natural language processing techniques to analyze received requests and identify important keywords or phrases.

[1309] "Means for searching for materials based on extracted keywords" refers to devices or software that use extracted keywords to search for related materials and information from a database.

[1310] "Means of suggesting combinations of related solutions" refers to devices or software that suggest the optimal set of materials and information to respond to a user's request based on search results.

[1311] "A means of generating and sending proposed content as a conversational response to the user" refers to a device or software that compiles proposed materials and information into a natural conversational format and sends it to the user.

[1312] "Means of displaying conversational responses to the user" refers to devices or software that display conversational responses to the user through smart glasses or other terminals.

[1313] "A means for users to input requests using smart glasses in a physical store" refers to devices and software that allow customers to input requests via voice or text through smart glasses within an actual store.

[1314] "Means of presenting products and information based on requests" refers to devices or software that display relevant products and information based on the user's request.

[1315] To implement this invention, a terminal including smart glasses, a server that analyzes requests and searches for data, and an application installed on the smart glasses are required. Specifically, the embodiments for implementing the invention are described below.

[1316] System Configuration

[1317] The main components are as follows:

[1318] 1. Smart glasses: A device for users to input requests and display information.

[1319] 2. Server: The central processing unit that parses requests, searches for materials, and provides relevant information.

[1320] 3. Natural Language Processing Engine: Software that analyzes user requests and extracts keywords (e.g., Google Cloud Natural Language API).

[1321] 4. Document search server: Software that searches for documents in a database based on extracted keywords (e.g., Elasticsearch).

[1322] Program processing flow

[1323] When the server receives an information request from a user, it uses a natural language processing engine to analyze the request and extract keywords. This natural language processing allows the server to accurately understand the user's intent. For example, if a user enters "I'm looking for a new smartphone," the keywords "new" and "smartphone" will be extracted.

[1324] Next, the server searches for relevant products and information from the data retrieval server based on these keywords. The search results are ranked in order of relevance, and a list is generated to provide to the user.

[1325] Furthermore, the server suggests the best product combinations for the user's request based on the search results. For example, if the user is looking for a "new smartphone," the server might suggest a set including "the latest smartphone model," "special offers," and "accessories." This suggestion is formatted as a conversational response.

[1326] The conversational responses generated by the server are sent to the smart glasses. The smart glasses display these responses to the user, allowing the user to view product lists and suggestions on the screen. When the user selects a specific document or product, that information is displayed in detail.

[1327] Specific example

[1328] For example, a user might voice-input "I'm looking for a new smartphone" through smart glasses. This request is sent to the server, where a natural language processing engine extracts the keywords "new" and "smartphone." The server then uses a data search server to search for related product information and retrieves a list of smartphones, such as "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB." This list is presented to the user in a conversational format, for example, "Here are some recommended products: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[1329] Example of a prompt

[1330] When a user enters "I'm looking for a new smartphone" into their smart glasses, the system uses an NLP process to extract the keywords "new" and "smartphone," and then retrieves related products from a search server based on those keywords. Related products include "iPhone 13 - 128GB" and "Samsung Galaxy S21 - 256GB," among others. The results are as follows:

[1331] Our recommended products are as follows:

[1332] iPhone 13 - 128GB

[1333] Samsung Galaxy S21 - 256GB

[1334] In this way, the present invention realizes a system that allows users to efficiently obtain the products and materials they desire and to quickly propose appropriate solutions based on them.

[1335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1336] Step 1:

[1337] Receiving a request

[1338] The user enters a request into the smart glasses via voice or text. For example, the request might say, "I'm looking for a new smartphone." This request is then sent from the smart glasses to the server.

[1339] Input: User's voice or text request

[1340] Output: Request data sent to the server

[1341] Step 2:

[1342] Request parsing

[1343] The server passes the received request to the natural language processing engine. The natural language processing engine analyzes the request and extracts important keywords. For example, the keywords "new" and "smartphone" might be extracted.

[1344] Input: Received request data

[1345] Output: Extracted keywords (e.g., "new", "smartphone")

[1346] Step 3:

[1347] Searching for materials

[1348] The server uses the extracted keywords to send a query to the data retrieval server. The data retrieval server searches the database and retrieves relevant products and information. For example, it might retrieve a list of smartphones such as "iPhone 13 - 128GB" or "Samsung Galaxy S21 - 256GB".

[1349] Input: Extracted keywords

[1350] Output: Search results (e.g., "iPhone 13 - 128GB", "Samsung Galaxy S21 - 256GB")

[1351] Step 4:

[1352] Response generation

[1353] The server generates conversational responses to provide to the user based on the search results. For example, it might generate text such as, "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB."

[1354] Input: Search Results

[1355] Output: Conversational response (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[1356] Step 5:

[1357] Sending and displaying responses

[1358] The server sends the generated conversational response to the smart glasses. The smart glasses display the received response to the user. The user can review the information displayed on the smart glasses' screen and select the products or information they need.

[1359] Input: Conversational response

[1360] Output: Response displayed on smart glasses (Example: "Our recommended products are: iPhone 13 - 128GB, Samsung Galaxy S21 - 256GB")

[1361] By executing these processing steps sequentially, users can efficiently obtain the products and information they need using smart glasses in physical stores.

[1362] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1363] The embodiments for carrying out the present invention will be described in detail below.

[1364] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. The system includes a function that provides personalized responses based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1365] Feature Overview

[1366] The system receives information requests from users and analyzes them using natural language processing and an emotion engine. Based on the analysis results, it searches for materials, ranks the search results in order of relevance, and suggests a combination of solutions that correspond to the user's emotions. The generated suggestions are sent to the user as a conversational response and displayed on their device.

[1367] Program processing flow

[1368] When the server receives an information request from a user, it first uses an emotion engine to analyze the user's emotional state. It analyzes emotional expressions, characters, and punctuation in the request text to determine the user's emotional state. For example, if a user enters "Send me the proposal document for the new product urgently!", the expression "urgently" will indicate an emotion of urgency.

[1369] Next, the server uses a natural language processing engine to analyze the request and extract important keywords. For example, keywords such as "new product," "proposal document," and "send" might be extracted.

[1370] Next, the server generates a database search query based on the extracted keywords. Using this search query, the server searches for documents within the database. For example, the database may contain documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx," and these documents will be searched. The server ranks the search results in order of relevance, displaying the most relevant documents to the user at the top.

[1371] Furthermore, the server considers the results of the emotion engine's analysis to suggest an appropriate combination of solutions. For example, if the emotion of anxiety is recognized, it can prioritize presenting materials and simplified proposals for a quick response.

[1372] The server compiles this information and generates a conversational response message. For example, it might generate a response like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[1373] The generated conversational response messages are sent to the terminal. The terminal displays this information to the user, who then selects the necessary documents from the displayed list and clicks the download button.

[1374] Specific example

[1375] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first uses an emotion engine to analyze the expression "urgent" to determine the user's urgency. Then, using a natural language processing engine, it extracts "new product" and "proposal document" as keywords and searches the database for "new product proposal document_2023.pdf" and "proposal_new product_overview.docx". In addition, it suggests a "simplified version of the proposal document" for urgent use.

[1376] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select the necessary materials and download them quickly.

[1377] In this way, the system of the present invention recognizes the user's emotions and provides personalized information and solution suggestions based on those emotions, thereby improving the user experience.

[1378] The following describes the processing flow.

[1379] Step 1:

[1380] The user enters an information request into the terminal and presses the send button. For example, the user might type, "Please send me the proposal document for the new product urgently!"

[1381] Step 2:

[1382] The terminal sends a user information request to the server. The request content is transferred as text data.

[1383] Step 3:

[1384] The server passes the received information request to the emotion engine, which then begins analyzing the emotional state. For example, it might detect the emotion of impatience from the expression "hurry up."

[1385] Step 4:

[1386] The server passes the request text to a natural language processing engine for analysis. Specifically, the natural language processing engine analyzes the request text and extracts important keywords such as "new product," "proposal document," and "send."

[1387] Step 5:

[1388] The server generates a search query based on the extracted keywords and searches the database. This search finds documents such as "New Product Proposal Document_2023.pdf" and "Proposal Document_New Product_Overview.docx".

[1389] Step 6:

[1390] The server ranks the search results in order of relevance. For example, newer materials and materials containing many relevant keywords will be ranked higher.

[1391] Step 7:

[1392] The server combines the search results with analysis results from the emotion engine to propose the most suitable solution. If the emotion of anxiety is detected, it prioritizes suggesting materials that allow for quick response or simplified versions of materials.

[1393] Step 8:

[1394] The server generates a conversational response message based on the proposal. For example, it might generate a message like, "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal Document_New Product_Overview.docx'. We also recommend the 'Simplified Proposal Document' for urgent requests."

[1395] Step 9:

[1396] The server generates a conversational response message and sends it to the terminal.

[1397] Step 10:

[1398] The terminal displays conversational response messages received from the server to the user.

[1399] Step 11:

[1400] The user selects the necessary documents from the displayed list and clicks the download button.

[1401] Step 12:

[1402] The terminal downloads the selected document from the server and provides it to the user.

[1403] In this way, the system recognizes the user's emotions and quickly provides customized materials and solutions based on them.

[1404] (Example 2)

[1405] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1406] Conventional information request processing systems provide information uniformly without considering the user's emotional state, making it difficult to respond quickly and appropriately to user needs. In particular, when a user is in a hurry or experiencing specific emotions, a response tailored to those emotions is required. Furthermore, finding necessary information from a large amount of data can be time-consuming, potentially detracting from the user experience.

[1407] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1408] In this invention, the server includes means for receiving information requests from users, means for analyzing the user's emotional state using sentiment analysis based on the received information request, means for analyzing the information request using natural language processing and extracting keywords, means for searching for materials based on the extracted keywords, means for ranking the searched materials in order of relevance, means for suggesting combinations of relevant solutions considering the results of the sentiment analysis, means for generating the suggested content as a conversational response and sending it to the user, and means for displaying the conversational response to the user. This enables personalized responses based on emotional state and rapid provision of materials.

[1409] An "information request" is a request that a user submits to obtain specific documents or information.

[1410] "Sentiment analysis" is a technology that identifies and analyzes the emotional state of a user from their input text.

[1411] "Natural language processing" is a technology that allows computers to analyze, understand, and generate natural human language.

[1412] Keyword extraction is the process of identifying and extracting important words and phrases from a document.

[1413] "Document search" is the process of finding documents within a database based on specified keywords.

[1414] "Ranking by relevance" is the process of rearranging search results in order of how well they match the user's requirements.

[1415] A "combination of solutions" is a set of suggestions or answers that address a user's problems or needs.

[1416] A "conversational response" is a response in the form of automatically generated documents or messages, similar to a conversation between humans.

[1417] "Sending to user" refers to the process of transferring responses and materials generated by the server to the user's terminal.

[1418] "Displaying to the user" refers to the process of visually displaying information received on the user's device.

[1419] This invention relates to a system that efficiently retrieves the information requested by the user and provides appropriate solutions. This system includes a function that provides personalized responses based on the user's emotional state by incorporating an emotion engine that recognizes the user's emotions.

[1420] Hardware and software configuration

[1421] server

[1422] The server is responsible for receiving requests, sentiment analysis, natural language processing, data retrieval, ranking, solution suggestions, response generation, and message sending. A sentiment engine is used for sentiment analysis, and a natural language processing engine is used for natural language processing. Data is stored in the database, and data is retrieved using search queries.

[1423] terminal

[1424] The terminal is responsible for receiving information requests from users and sending them to the server. The terminal also displays response messages received from the server and provides an interface for users to select and download materials.

[1425] Data processing and data calculation

[1426] Receiving information requests and sentiment analysis

[1427] When a user enters an information request into their device and clicks the send button, the device sends this request to the server as an HTTP request. The server passes the received request to the sentiment engine, which analyzes emotional expressions, punctuation, emphasized keywords, and other relevant information.

[1428] Natural language processing and keyword extraction

[1429] Next, the server passes the request text to the natural language processing engine. The natural language processing engine analyzes the request text and extracts important keywords. This extracts keywords such as "new product" and "proposal document."

[1430] Search query generation and document retrieval

[1431] The server combines the extracted keywords to generate a search query. For example, it generates a query like "SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'", and conducts a search on the material database.

[1432] Ranking and solution proposal

[1433] Once the search results are obtained, the server ranks these materials in order of relevance. For the ranked materials, the server considers the analysis results of the sentiment engine and proposes appropriate solutions. For example, for urgent requests, it prioritizes simple version proposal materials.

[1434] Generation and transmission of response in conversation form

[1435] The server generates a response message in conversation form based on the results of ranking and solution proposal. The generated response message is encoded in JSON format and sent to the terminal as an HTTP response.

[1436] Display of response message and download of materials

[1437] The terminal decodes the response message received from the server and displays it to the user. The user selects the required materials and clicks the download button to obtain the materials.

[1438] Specific example

[1439] If a user is urgently searching for a new product proposal document, they might type "Send me the new product proposal document urgently!" into their device and send it. The device sends this request to the server, which first performs sentiment analysis. It recognizes the emotion of urgency from the expression "urgently," and then uses a natural language processing engine to extract "new product" and "proposal document" as keywords. The server generates a search query and searches the database for "new product proposal document_2023.pdf" and "proposal_new_product_overview.docx." In addition, it suggests a "simplified version of the proposal document" for urgent use.

[1440] Based on this information, the server generates a conversational response message and sends it to the terminal. The terminal displays this message to the user, who can then select and quickly download the necessary materials.

[1441] Example of a prompt

[1442] Please send the proposal document for the new product as soon as possible.

[1443] I'm looking for the latest proposals for a new product. Can you help me?

[1444] I urgently need a simplified version of the proposal document.

[1445] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1446] Step 1:

[1447] Receiving a user information request

[1448] Specific actions

[1449] The user types "Send me the new product proposal quickly!" into their device and clicks the send button.

[1450] Input and output

[1451] Input: User information request ("Please send me the new product proposal materials urgently!")

[1452] Output: The terminal sends the request to the server in HTTP request format.

[1453] Step 2:

[1454] Analyze the user's emotional state.

[1455] Specific actions

[1456] The server passes the received request to the emotion engine. The emotion engine analyzes the keyword "hurry" and recognizes the emotion of urgency.

[1457] Input and output

[1458] Input: User information request ("Please send me the new product proposal materials urgently!")

[1459] Output: Emotion analysis results (emotion of anxiety is recognized)

[1460] Step 3:

[1461] Request text analysis and keyword extraction

[1462] Specific actions

[1463] The server passes the request to a natural language processing engine, which extracts important keywords. For example, "new product" and "proposal document" might be extracted.

[1464] Input and output

[1465] Input: User information request ("Please send me the new product proposal materials urgently!")

[1466] Output: List of extracted keywords ("New Product", "Proposal Document")

[1467] Step 4:

[1468] Generation of database search query

[1469] Specific operations

[1470] The server combines the extracted keywords to generate an SQL query. For example, a query like "SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'" is generated.

[1471] Input and output

[1472] Input: List of extracted keywords ("new product", "proposal materials")

[1473] Output: Generated SQL query ("SELECT FROM materials WHERE title LIKE '%new product%' AND title LIKE '%proposal materials%'")

[1474] Step 5:

[1475] Search and ranking of materials

[1476] <着ID= Specific operations

[1477] The server executes the generated SQL query to search the materials database. The search results are ranked in order of relevance. For example, make sure that "new product proposal materials_2023.pdf" and "proposal document_summary of new product.docx" come to the top.

[1478] Input and output

[1479] Input: Generated SQL query

[1480] Output: List of ranked search results ("new product proposal materials_2023.pdf", "proposal document_summary of new product.docx")

[1481] Step 6:

[1482] Proposing solutions based on emotions

[1483] Specific actions

[1484] The server will consider the results of the sentiment analysis and, in urgent cases, will submit an additional, simplified version of the proposal document.

[1485] Input and output

[1486] Input: Sentiment analysis results, list of ranked search results

[1487] Output: List of proposed solutions ("New Product Proposal Document_2023.pdf", "Proposal Document_New Product_Overview.docx", "Simplified Proposal Document")

[1488] Step 7:

[1489] Generating conversational response messages

[1490] Specific actions

[1491] The server generates conversational response messages based on a list of proposed solutions.

[1492] Input and output

[1493] Input: List of proposed solutions

[1494] Output: Generated conversational response message (Example: "The following documents were found: 'New Product Proposal Document_2023.pdf', 'Proposal_New Product_Overview.docx'. For urgent requests, we also recommend the 'Simplified Proposal Document'.")

[1495] Step 8:

[1496] Sending and displaying response messages

[1497] Specific actions

[1498] The server sends a generated response message to the terminal. The terminal receives the response message and displays it to the user. The user selects the necessary documents and clicks the download button.

[1499] Input and output

[1500] Input: Generated conversational response messages

[1501] Output: Response message displayed on the terminal and user download action

[1502] (Application Example 2)

[1503] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1504] Traditional information request systems provided uniform responses without considering the user's emotional state, resulting in challenges with user satisfaction and efficiency. Furthermore, particularly in the food delivery sector, there was a lack of mechanisms to quickly suggest optimal menus tailored to the user's condition and emotions. Consequently, it was difficult to adequately meet user needs and improve satisfaction.

[1505] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information requests from users, means for analyzing the received information requests using natural language processing and extracting keywords, and means for recognizing the user's emotional state using an emotion engine. This makes it possible to provide personalized materials or menus that take the user's emotions into consideration.

[1506] An "information request" refers to a request made by a user to obtain specific information or materials.

[1507] "Natural language processing" refers to the technology of analyzing language data entered by a user and understanding its meaning and intent.

[1508] "Keywords" refer to important words or phrases included in a user's information request.

[1509] An "emotion engine" refers to a technology that analyzes a user's text data to understand their emotional state and recognize those emotions.

[1510] "Emotional state" refers to the psychological state that can be inferred from the user's statements and written content.

[1511] "Documents" refers to documents and data files related to the information requested by the user.

[1512] In the context of food delivery, "menu" refers specifically to the range of meals available to the user.

[1513] "Searching" refers to finding relevant documents or menus from databases or information sources based on extracted keywords.

[1514] A "solution" refers to a specific solution or proposal provided in response to a user's information request.

[1515] "Conversational responses" refer to response messages returned to the user that take the form of a dialogue, and are messages exchanged between the user and the system.

[1516] A "terminal" refers to a device used by a user to input information requests or receive response messages.

[1517] The system realizing this invention recognizes the user's emotional state in the context of food delivery and provides personalized menu suggestions based on that emotion. The specific implementation method is described below.

[1518] System Configuration

[1519] This system consists of a server containing multiple modules for receiving and analyzing information requests from users and providing appropriate solutions, and terminals used by the users.

[1520] Hardware and software configuration

[1521] server:

[1522] A web server for receiving and analyzing information requests.

[1523] Emotion engines (e.g., IBM Watson Natural Language Understanding).

[1524] A natural language processing engine (e.g., Google Cloud Natural Language API).

[1525] Database management systems (e.g., MongoDB).

[1526] Dialogue systems (e.g., Dialogflow).

[1527] Terminal:

[1528] Smartphones and tablets used by users.

[1529] The application will be implemented as a mobile app that runs on iOS or Android.

[1530] Processing flow

[1531] 1. Receiving information requests from users

[1532] The user types "I'm tired today, I want food right away" into a smartphone application and sends it.

[1533] 2. Recognition of emotional states

[1534] The server uses an emotion engine to recognize the emotion of "tiredness" and analyze the user's urgent requests.

[1535] 3. Keyword extraction using natural language processing

[1536] Using a natural language processing engine, we extract the important keywords "tired," "immediately," and "food."

[1537] 4. Database Search

[1538] Based on the extracted keywords and emotional state, the system searches the database for relevant menu items. For example, it searches using the conditions "available for immediate delivery" and "easy to eat."

[1539] 5. Personalized solution proposals

[1540] Based on the search results, the system generates conversational response messages that take into account the user's emotional state. For example, it might generate messages like, "The following items are available for immediate delivery: pizza, sandwiches. We also recommend our salads, perfect for replenishing energy when you're feeling tired."

[1541] 6. Sending and displaying responses to the user

[1542] The generated response message is sent to the user's terminal, which then displays this message.

[1543] Specific example

[1544] Specific examples are given below.

[1545] The user types and submits "I'm tired today, I want food right away." The server then uses an emotion engine to recognize the emotional state of "tired" and a natural language processing engine to extract the keywords "tired," "immediately," and "food." Next, it searches its database for menus that are "immediately available for delivery" and "easy to eat," generating personalized suggestions such as "pizza," "sandwich," and "salad." Finally, these suggestions are sent to the user's device and displayed, allowing the user to quickly select appropriate food.

[1546] Example of a prompt

[1547] User input: "I'm tired today, I want food right away."

[1548] Emotion recognition result: "Fatigue"

[1549] Key keywords: "tired", "immediately", "food"

[1550] Search criteria: "Menus available for immediate delivery"

[1551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1552] Step 1:

[1553] The user enters and submits an information request. Once this information request is entered, the device sends this request to the server. For example, if the user enters "I'm tired today, I want food right away," the device transfers that text data to the server.

[1554] Step 2:

[1555] The server receives an information request from the user. The server receives the text data of the information request as input and prepares it as data to be analyzed for processing in the next step.

[1556] Step 3:

[1557] The server uses an emotion engine to recognize the user's emotional state from the received information request. At this time, the emotion engine analyzes emotional expressions such as "tired" from the text data and outputs "fatigue" as the emotional state.

[1558] Step 4:

[1559] The server uses a natural language processing engine to extract key keywords from the information request. This process extracts the keywords "tired," "immediately," and "food." The input is the request text data, and the output is a list of extracted keywords.

[1560] Step 5:

[1561] Based on the keywords extracted by the server and the recognized emotional state, a database management system is used to search for relevant materials or menus. In this process, the keyword list and emotional state are used as input, and a list of menus matching the search criteria is output.

[1562] Step 6:

[1563] The server ranks the menu items in order of relevance based on search results and sentiment. At this stage, relevance analysis is performed, and a ranked menu list is output.

[1564] Step 7:

[1565] The server generates personalized solutions for the user as conversational response messages based on a ranked menu list. The input is the ranked menu list, and the output is the generated conversational response messages.

[1566] Step 8:

[1567] The server sends a generated conversational response message to the terminal. Specifically, the response message is sent to the user's smartphone in the form of an email or notification.

[1568] Step 9:

[1569] The terminal displays the response message it received to the user. The user then checks the appropriate menu from the displayed message and makes a selection or order.

[1570] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1571] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1572] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1573] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1574] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1575] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1576] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1577] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1578] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1579] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1580] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1581] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1582] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1583] 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.

[1584] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1585] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1586] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1587] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1588] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1589] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1590] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1591] The following is further disclosed regarding the embodiments described above.

[1592] (Claim 1)

[1593] A means of receiving information requests from users,

[1594] A means of analyzing received information requests using natural language processing and extracting keywords,

[1595] A means of searching for materials based on extracted keywords,

[1596] A means of suggesting a combination of relevant solutions based on the search results,

[1597] A means of generating a proposal as a conversational response and sending it to the user,

[1598] A means of displaying conversational responses to the user,

[1599] A system that includes this.

[1600] (Claim 2)

[1601] The system according to claim 1, which uses a natural language processing engine in the analysis of information requests.

[1602] (Claim 3)

[1603] The system according to claim 1, comprising means for ranking search results for materials based on user requests in order of relevance and presenting them in a list format.

[1604] "Example 1"

[1605] (Claim 1)

[1606] A means of receiving information requests from users,

[1607] A means of analyzing received information requests using natural language processing and extracting keywords,

[1608] A means of searching for information resources based on extracted keywords,

[1609] A means of suggesting a combination of relevant solutions based on the search results,

[1610] A means of generating a proposal as a conversational response and sending it to the user,

[1611] A means of displaying conversational responses to the user,

[1612] A means of downloading information resources selected by the user,

[1613] A system that includes this.

[1614] (Claim 2)

[1615] The system according to claim 1, which uses a natural language processing engine in the analysis of information requests.

[1616] (Claim 3)

[1617] The system according to claim 1, comprising means for ranking search results of information resources based on user requests in order of relevance and presenting them in a list format.

[1618] "Application Example 1"

[1619] (Claim 1)

[1620] A means of receiving information requests from users,

[1621] A means of analyzing received information requests using natural language processing and extracting keywords,

[1622] A means of searching for materials based on extracted keywords,

[1623] A means of suggesting a combination of relevant solutions based on the search results,

[1624] A means of generating a proposal as a conversational response and sending it to the user,

[1625] A means of displaying conversational responses to the user,

[1626] In a physical store, a means for users to input requests using smart glasses,

[1627] A means of presenting products and information based on requests,

[1628] A system that includes this.

[1629] (Claim 2)

[1630] The system according to claim 1, which uses a natural language processing engine in the analysis of information requests.

[1631] (Claim 3)

[1632] The system according to claim 1, comprising means for ranking search results for materials based on user requests in order of relevance and presenting them in a list format.

[1633] "Example 2 of combining an emotion engine"

[1634] (Claim 1)

[1635] A means of receiving information requests from users,

[1636] A means of analyzing the user's emotional state using sentiment analysis based on received information requests,

[1637] A method for analyzing information requests using natural language processing and extracting keywords,

[1638] A means of searching for materials based on extracted keywords,

[1639] A method for ranking searched materials in order of relevance,

[1640] A means of proposing a combination of relevant solutions, taking into account the results of sentiment analysis,

[1641] A means of generating a proposal as a conversational response and sending it to the user,

[1642] A means of displaying conversational responses to the user,

[1643] A system that includes this.

[1644] (Claim 2)

[1645] The system according to claim 1, which uses a natural language processing engine in the analysis of information requests.

[1646] (Claim 3)

[1647] The system according to claim 1, comprising means for ranking search results for materials based on user requests in order of relevance and presenting them in a list format.

[1648] "Application example 2 when combining with an emotional engine"

[1649] (Claim 1)

[1650] A means of receiving information requests from users,

[1651] A means of analyzing received information requests using natural language processing and extracting keywords,

[1652] A means of recognizing a user's emotional state using an emotion engine,

[1653] A means of searching for materials or menus based on extracted keywords and emotional states,

[1654] A means of suggesting a combination of relevant solutions based on search results and emotional state,

[1655] A means of generating a proposal as a conversational response and sending it to the user,

[1656] A means of displaying conversational responses to the user,

[1657] A system that includes this.

[1658] (Claim 2)

[1659] The system according to claim 1, which uses a natural language processing engine and an emotion engine in analyzing information requests.

[1660] (Claim 3)

[1661] The system according to claim 1, further comprising means for ranking search results for materials or menus based on user requests and emotional states in order of relevance and presenting them in a list format. [Explanation of symbols]

[1662] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information requests from users, A means of analyzing received information requests using natural language processing and extracting keywords, A means of searching for materials based on extracted keywords, A means of suggesting a combination of relevant solutions based on the search results, A means of generating a proposal as a conversational response and sending it to the user, A means of displaying conversational responses to the user, A system that includes this.

2. The system according to claim 1, which uses a natural language processing engine in the analysis of information requests.

3. The system according to claim 1, comprising means for ranking search results for materials based on user requests in order of relevance and presenting them in a list format.

Images