system

Abstract

We provide the system. [Solution] An information management device provides a means for indexing information using natural language processing technology, A means of receiving natural language questions from users, analyzing them, and extracting relevant keywords, A means of searching for relevant information from a database using extracted keywords, A means for automatically generating answers to user questions based on search results, A means of presenting the generated response to the user's terminal, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a 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】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Within a company, due to the vast and scattered information, it takes time to search for information individually, and there is a problem that necessary documents and past cases cannot be efficiently discovered. In addition, since knowledge is personified, new employees and transferees tend to rely on specific persons in charge, resulting in a problem of reduced work speed. It is necessary to solve these problems, quickly and efficiently obtain information, and promote knowledge sharing. 【Means for Solving the Problems】 【0005】 This invention solves the above problems by providing an information management device using natural language processing technology. Specifically, it makes information within a company searchable efficiently by means of indexing information. Furthermore, it accurately understands the user's intent by receiving and analyzing natural language questions from users and extracting relevant keywords. It enables users to quickly retrieve relevant information using a search means that uses the extracted keywords and automatically generates answers based on the search results, allowing users to obtain necessary information immediately. In addition, it improves the efficiency of information utilization by supporting users to easily access information using a means that presents the generated answers. This prevents knowledge from becoming dependent on individuals and makes it possible to increase business productivity. 【0006】 An "information management device" is a device that efficiently collects, processes, and retrieves information within a company, and provides information according to the user's needs. 【0007】 "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate natural language that humans use in everyday life. 【0008】 "Indexing" is the process of organizing information and data according to specific criteria and making them easy to search. 【0009】 Keyword extraction is the process of extracting important words and categories from text data to clarify the essential points of the information. 【0010】 A "database" is a system or software for efficiently storing and managing large amounts of data. 【0011】 "Automatic generation" refers to the process by which a computer generates data or content based on an algorithm, without human intervention. 【0012】 A "user terminal" is a computer or device used by a user, and is a device that accesses information systems through an interface. 【0013】 A "business category" is a set of criteria or groups used to classify activities and information related to specific business operations within a company. 【0014】 "Re-ranking" is the process of rearranging lists, such as search results, according to certain criteria. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the language used in the following description will be explained. 【0018】 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. 【0019】 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. 【0020】 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. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0029】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 This invention relates to an information management device for achieving effective information management within a company, and is based on the indexing, retrieval, and presentation of information using natural language processing technology. Embodiments of this invention will be described in detail below. 【0037】 The information management system has the function of collecting and managing data such as documents, project histories, FAQs, and operational manuals that exist within a company. This data is collected periodically by a server, and after necessary text cleaning is performed, it is indexed using natural language processing technology. This indexing makes the data quickly searchable. 【0038】 When a user requests specific internal company information and asks a question, the terminal receives the question. The question is entered in natural language and sent to the server. The server analyzes the question using natural language processing technology and extracts keywords related to the question. This keyword extraction is a crucial process for understanding the intent and context of the question. 【0039】 Based on the extracted keywords, the server searches for relevant information from its stored index. This search includes past project history and related documents. Based on the search results, the server automatically generates answers to the questions. The purpose of this answer generation is to provide users with easy-to-understand and directly relevant information. 【0040】 The generated answers are presented to the user via the device. The answers may include links to relevant documents and detailed information, allowing the user to efficiently carry out their tasks by referring to them. 【0041】 As a concrete example, consider a case where a user asks, "I want to know about the past marketing strategies for new product B." The terminal receives the question and sends it to the server. The server uses natural language processing to analyze the question and extracts keywords such as "new product B," "past," and "marketing strategy." Based on this, it searches the database and extracts relevant past project histories and reports. The server organizes the results and generates answers about specific past campaign examples and success stories, which the terminal then presents to the user. 【0042】 This invention enables the rapid and efficient acquisition and utilization of information within a company, allowing new employees and transferees to easily access internal knowledge. This prevents knowledge from becoming tied to specific individuals and improves work productivity. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The server periodically collects data from multiple sources within the company's information repository, including documents, project histories, FAQs, and operational manuals. This includes automated retrieval from file servers and databases. 【0046】 Step 2: 【0047】 The server performs text preprocessing on the collected data. This preprocessing cleans the data, removes noise, and standardizes character encoding. 【0048】 Step 3: 【0049】 The server applies natural language processing techniques to the pre-processed data, indexing the information through tokenization and morphological analysis. At this stage, the data is organized to facilitate searching. 【0050】 Step 4: 【0051】 When users need information, they input questions into their devices using natural language. 【0052】 Step 5: 【0053】 The terminal sends the question received from the user to the server. The question is provided in natural language format. 【0054】 Step 6: 【0055】 After receiving a user's question, the server analyzes it using natural language processing. Specifically, it extracts contextually relevant keywords to understand the intent of the question. 【0056】 Step 7: 【0057】 The server searches an already indexed database based on the extracted keywords and the context of the question. This is a process to quickly identify relevant information. 【0058】 Step 8: 【0059】 The server organizes the search results and selects the most relevant information. It applies a re-ranking algorithm to prioritize and output the most important information. 【0060】 Step 9: 【0061】 The server generates specific answers to user questions based on the selected information, including links to relevant documents. 【0062】 Step 10: 【0063】 The device presents the generated response to the user. Based on the information provided, the user can access the necessary details. 【0064】 This step allows users to quickly obtain the necessary information and carry out their work efficiently. 【0065】 (Example 1) 【0066】 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." 【0067】 Within companies, a vast amount of information is generated daily from diverse sources, making it crucial to quickly acquire necessary information and utilize it in business operations. However, systems that efficiently manage this information and provide immediate and appropriate answers when users naturally ask questions are not yet sufficiently developed. This challenge is particularly urgent in order to prevent information from becoming siloed and improve business productivity by making it easier for new employees and transferees to access internal knowledge. 【0068】 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. 【0069】 In this invention, the server includes means for acquiring information and formatting text data, means for indexing the information using natural language processing technology, and means for generating responses in natural language using a generative AI model. This makes it possible to quickly and accurately acquire the necessary information and present responses when a user gives instructions in natural language. 【0070】 "Methods for acquiring information and formatting text data" refers to the process of collecting information from various data sources dispersed within a company, removing unnecessary elements, and converting it into a format that is easy to analyze. 【0071】 "A method for indexing information using natural language processing technology" refers to a method of efficiently structuring collected and formatted text information into searchable data by analyzing it with a natural language processing algorithm. 【0072】 "A method for generating answers in natural language using a generative AI model" refers to a process that uses generative AI technology to automatically generate grammatically correct and human-readable text when providing answers to questions. 【0073】 "Means for identifying the business classification related to the instructions based on the analysis of the received instructions" refers to a technology that analyzes natural language instructions from users and identifies the business classification corresponding to the content of the instructions. 【0074】 "Methods for re-evaluating search results and selecting and presenting the most relevant information" refers to a method of re-evaluating multiple search results obtained within the system from the perspective of importance and relevance, and prioritizing the provision of the most suitable information to the user. 【0075】 For the present invention to be implemented, information flow and data analysis between servers, terminals, and users are important. 【0076】 The server periodically collects documents and business data distributed within the company using internet technology. This process utilizes dedicated crawling software and performs text cleaning as needed using natural language processing libraries such as OpenNLP and NLTK. The collected data is indexed using Lucene and Elasticsearch (registered trademark) and stored as data with enhanced searchability. 【0077】 When a user requests specific information, they input their question in natural language through the user interface on their device. The device receives this question and sends it to the server. For example, a user might input, "Please tell me about past marketing cases for the new product B." 【0078】 When the server analyzes the received questions, it uses libraries such as spaCy and Transformers to extract highly relevant keywords from the questions. Based on these keywords, it searches for relevant information in past project history and document databases. 【0079】 Search results are re-evaluated on the server, and the most relevant information is selected. Then, a generative AI model, such as GPT-3®, is used to automatically generate answers in newly generated, natural language. These answers include relevant documents and links. 【0080】 The generated answers are presented to the user via the terminal. This system allows users to quickly obtain the information necessary for their work, leading to improved work productivity and preventing knowledge from becoming tied to specific individuals. 【0081】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0082】 Step 1: 【0083】 The server uses crawling technology to collect diverse documents and project histories within the company. Simultaneously, metadata for each document is collected and stored in a database. The input information comes from different formats and sources, but the output is text data in a unified format. 【0084】 Step 2: 【0085】 The server performs text cleaning on the collected raw data. This process uses the NLTK library to remove unnecessary symbols and line breaks and format the text. The output is cleaned data suitable for analysis. 【0086】 Step 3: 【0087】 The server applies natural language processing to the cleaned data and performs indexing. It uses Lucene to tokenize the data, assigns weights to each token, and generates an index. The input is cleaned text data, and the output is indexed, searchable data. 【0088】 Step 4: 【0089】 The user enters a question about specific information through the terminal's interface. This question is written in natural language and sent to the server as user instruction. The input is a question in natural language. 【0090】 Step 5: 【0091】 The server receives questions submitted by users and analyzes them using spaCy. It extracts relevant keywords from the questions and uses them to search for related information in an index. The input is a natural language question, and the output is a list of documents that match the relevant keywords. 【0092】 Step 6: 【0093】 The server re-evaluates the searched information and selects the most relevant content. Using a generative AI model, it automatically generates a response based on the selected information. This process utilizes generative models such as GPT-3. The input is a list of documents, and the output is a natural-sounding response. 【0094】 Step 7: 【0095】 The terminal receives the generated response from the server and presents it to the user. The user uses this information for their work and accesses relevant links and documents as needed. The input is the generated response, and the output is the response presentation screen that the user can view. 【0096】 (Application Example 1) 【0097】 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." 【0098】 In modern urban life, it is difficult for citizens to efficiently obtain information about urban public infrastructure. Furthermore, there is a lack of effective information provision systems for quickly grasping complex procedures and event information. This makes daily life inconvenient for citizens and hinders the smooth use of public services. 【0099】 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. 【0100】 In this invention, the server includes means for indexing information using natural language processing technology, means for analyzing user inquiries and extracting relevant keywords, means for searching for relevant information from a data storage device, and means for analyzing data and selecting and presenting optimal information to provide information about urban infrastructure. This enables citizens to efficiently obtain information about urban public infrastructure, thereby improving their daily convenience. 【0101】 An "information management unit" is a key component of a system equipped with functions for efficiently collecting, organizing, storing, and retrieving information. 【0102】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0103】 "Indexing" is the process of organizing and systematizing information based on specific criteria in order to improve the efficiency of information retrieval. 【0104】 "Users" refer to individuals or organizations that actually use the system or service. 【0105】 A "question" is a question or request that a user enters into the system to obtain information. 【0106】 "Analysis" is the process of breaking down data into its constituent elements, examining them, and understanding their meaning and relationships. 【0107】 "Related keywords" are important words or phrases extracted to identify the meaning and intent of a question. 【0108】 A "data storage device" is a device or system for storing digital data and retrieving it as needed. 【0109】 "Urban infrastructure" is a general term for the foundations that support urban life, such as transportation, communication, water supply, and educational facilities. 【0110】 "Components of information provision" refer to the functions and processes for organizing and communicating the information necessary for users. 【0111】 "Public infrastructure information" refers to information regarding the operation status and usage guidelines of public services and facilities in cities and regions. 【0112】 The system for realizing this invention includes an information management unit using natural language processing technology and operates on a device such as a smartphone as an application that provides citizens with information on the city's public infrastructure. The server first receives a natural language query from the user, analyzes the content using natural language processing technology, and extracts relevant keywords. For example, natural language processing libraries such as spaCy and NLTK are used for this purpose. Based on the extracted keywords, the server searches for relevant information from a cloud database (e.g., Firebase or AWS® DynamoDB), which is a data storage device. 【0113】 The searched information is organized in a re-display order to provide more accurate information. The server uses a recommendation algorithm specialized for urban infrastructure information to select the most relevant information and provide it to the user's terminal. As a result, users can instantly access the necessary administrative information and infrastructure status. 【0114】 For example, if a user enters a question such as "Tell me about local events available this weekend," the generative AI model processes this question and retrieves relevant information using the prompt "Tell me about local events this weekend." This allows the user to quickly find details such as the location and date of the relevant event. 【0115】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0116】 Step 1: 【0117】 The terminal receives a natural language question from the user. This input data is sent directly to the server. This question contains a specific information need. 【0118】 Step 2: 【0119】 The server analyzes the received question using natural language processing techniques. This process extracts keywords using natural language processing libraries such as spaCy and NLTK to understand the intent of the question. The input is the text of the received question, and the output is a list of related keywords. 【0120】 Step 3: 【0121】 The server uses the extracted keywords to search for relevant information in cloud databases (e.g., Firebase, AWS DynamoDB). The search input is a list of keywords, and relevant results are output from the database. 【0122】 Step 4: 【0123】 The server organizes the searched information in a re-display order based on relevance. A recommendation algorithm is used for this organization. Data that matches more accurately is prioritized, generating an optimized list of information. 【0124】 Step 5: 【0125】 The server selects the most appropriate content from the organized information, constructs it in natural language, and provides it to the terminal. Here, a generative AI model is used to generate prompt sentences and present the information. At this stage, the input is organized information, and the output is the constructed response text. 【0126】 Step 6: 【0127】 The terminal displays the response text received from the server to the user. Based on this, the user can satisfy their information needs and obtain information about the city's public infrastructure. 【0128】 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. 【0129】 This invention provides a system for corporate information management that combines natural language processing technology and an emotion engine to deliver more user-friendly information. The embodiments of this invention are described below in detail. 【0130】 This information management system is server-driven, collecting information from various data sources within the company and indexing it using natural language processing technology. This indexing makes the information easily searchable. Users, during their work, can input questions in natural language when they need specific information, and query the system through their terminals. 【0131】 When the server receives a question from a user, it first analyzes the question using natural language processing techniques. This analysis extracts keywords and relevant contextual information, which are then used for searching. In this step, the sentiment engine recognizes the emotions from the user's question and extracts emotional data such as potential stress and urgency. 【0132】 Based on this data, the server searches the database for relevant information. It can adjust the order and amount of information presented, particularly by referencing sentiment data. For example, if a user is dissatisfied, the server can adjust the information to include more detailed and helpful explanations. 【0133】 The server then integrates the search results and automatically generates an answer to the question. This answer may include links to relevant documents and supplementary content. The generated answer is presented to the user via the terminal in an easy-to-understand format. 【0134】 As a concrete example, suppose a user asks a question such as, "I need the meeting notes from yesterday's Project X meeting, and I'm in a hurry." The server receives this question and uses natural language processing to extract keywords such as "Project X," "meeting notes," and "urgent," while also using an emotion engine to recognize the user's level of urgency. Using this information, the server quickly searches for the relevant meeting notes and constructs an automatically generated response that prioritizes displaying the necessary parts. The terminal then promptly presents the response to the user, taking into consideration the urgency of the matter. 【0135】 This system allows users to receive information optimized according to their emotional state at the time, significantly improving the efficiency of information acquisition in their work. Furthermore, by enhancing their ability to respond, especially in situations involving emotions, it becomes possible to create a more comfortable information environment. 【0136】 The following describes the processing flow. 【0137】 Step 1: 【0138】 The server periodically collects information from data sources within the company. This data includes documents, emails, project history, and FAQs. The collected data is indexed using natural language processing techniques to enable rapid searching. 【0139】 Step 2: 【0140】 Users input questions in natural language into their devices when they need information. The more specific the question, the more accurate the answer will be. 【0141】 Step 3: 【0142】 The terminal receives the question entered by the user and sends it to the server. The question is passed to the server as unstructured text data. 【0143】 Step 4: 【0144】 The server analyzes the received question using natural language processing techniques. This analysis extracts keywords and context from the question, forming the basis for information retrieval. 【0145】 Step 5: 【0146】 The server uses an emotion engine to recognize the emotions contained in the user's questions. This allows it to detect emotional data such as stress, urgency, and excitement, and to consider these factors when presenting information. 【0147】 Step 6: 【0148】 The server searches an indexed database using the extracted keywords and sentiment data. This process applies algorithms to quickly identify relevant materials. 【0149】 Step 7: 【0150】 The server aggregates search results and re-ranks them, taking into account the user's emotional state. Here, information is prioritized according to emotional data. 【0151】 Step 8: 【0152】 The server automatically generates appropriate answers to questions based on the selected information. These generated answers include links to relevant additional information and documentation. 【0153】 Step 9: 【0154】 The device presents the generated answers to the user. This presentation is designed to be clear and efficient so that the user can immediately utilize the information. 【0155】 Specifically, through this process, users can receive emotionally sensitive responses, reducing stress during information acquisition. Furthermore, information prioritization is optimized according to the user's situation, and necessary information is provided quickly. 【0156】 (Example 2) 【0157】 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". 【0158】 There is a need to effectively manage information resources scattered throughout the company and provide an environment where users can quickly and accurately obtain information. Furthermore, it is necessary to improve work efficiency by presenting information in a way that is tailored to the user's emotions and the urgency of their questions. 【0159】 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. 【0160】 In this invention, the server includes means for organizing information using natural language processing technology, means for analyzing user questions and extracting relevant features, and means for retrieving relevant information from data storage using emotional information and generating a tailored response. This enables the provision of optimal information in response to user questions, and in particular, enables the accurate presentation of information according to emotions and urgency. 【0161】 An "information processing device" is a device that effectively manages information within a company and quickly provides users with the information they need. 【0162】 "Natural language processing technology" is a technology that enables computers to understand human language, and involves analyzing text data to understand its meaning and structure. 【0163】 "Feature extraction" means extracting useful information and data points for analysis from the input data. 【0164】 "Emotional information" refers to data used to determine emotions and urgency from user input, and is taken into consideration when presenting information. 【0165】 "Data storage area" refers to the location or system where information is stored, and is used during retrieval. 【0166】 "Searching for related information" means finding useful data or documents from data storage in response to a query. 【0167】 A "tailored response" is a response customized according to the user's emotions and the content of their question, with the aim of providing the most relevant information. 【0168】 This invention is a system for streamlining information processing within a company and providing optimized information in response to user inquiries. This system primarily consists of servers, terminals, and users, and aims to effectively manage and provide information. 【0169】 First, the server collects information from various data sources within the company. These data sources include document management systems, email, and databases. The server then analyzes and indexes this information using natural language processing technologies (e.g., spaCy or NLTK). This organizes the information into an efficiently searchable format. 【0170】 Next, the user inputs a question in natural language and sends it to the server via their device. This input is done using a web browser or desktop application. The server analyzes the user's question and extracts key keywords and relevant contextual information. Furthermore, it uses an emotion engine to identify emotional information from the user's question, extracting emotional data such as urgency and dissatisfaction. 【0171】 Based on the extracted information, the server searches the database for relevant information. During this process, the search results are adjusted based on sentiment data. For example, if high urgency is recognized, the search is optimized to prioritize the presentation of necessary information. Then, using the search results, the server automatically generates answers to the user's questions using a generative AI model (e.g., a GPT model). These answers may include links to relevant additional information and documents. 【0172】 Finally, the device provides the generated response to the user, presenting it in a format that takes the user's situation into particular. This allows the user to quickly and effectively obtain the necessary information. 【0173】 As a concrete example, consider a scenario where a user asks a question such as, "I urgently need the meeting notes from yesterday's Project X." In this case, the server analyzes the question and extracts keywords such as "Project X," "meeting notes," and "urgent," along with an indication of urgency. Based on this information, it quickly searches for relevant meeting notes and displays them preferentially through the user's device. 【0174】 An example of a prompt message is, "Please provide the latest information on Project X. I'm in a hurry." This example demonstrates how to provide optimal information tailored to the user's needs. 【0175】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0176】 Step 1: 【0177】 The server collects information from data sources within the company (e.g., document management systems, email, databases). At this stage, it receives raw data obtained from the data sources as input. Subsequently, the collected data is analyzed using natural language processing techniques (e.g., spaCy, NLTK), organized, and indexed. Specifically, it extracts topics and keywords from the text and generates an index to facilitate information retrieval. As a result, the indexed data is output. 【0178】 Step 2: 【0179】 Users input necessary information for their work and send natural language questions to the server using their terminal. At this time, the user's specific questions and information requests (e.g., "Please provide the meeting notes for Project X.") are treated as input. 【0180】 Step 3: 【0181】 The server analyzes the received question. This analysis uses natural language processing techniques to extract key keywords and relevant contextual information from the input text. Specifically, this step involves tokenizing the text and performing morphological and dependency analysis. Then, an emotion engine is used to identify the user's emotional information (e.g., urgency, dissatisfaction) from the question. As a result of the analysis, keywords and emotional information are output. 【0182】 Step 4: 【0183】 Based on the analysis results from step 3, the server searches for relevant information from the indexed database. Specifically, it performs keyword matching and relevance scoring, and prioritizes information while considering sentiment. This search operation outputs highly relevant and well-organized information. 【0184】 Step 5: 【0185】 The server uses the search results to automatically generate answers to the user's questions using a generative AI model (e.g., a GPT-based model). This step integrates the search results with generative AI technology to generate contextually natural-sounding text. The final answer may include links to relevant documents and supplementary information, which are then output. 【0186】 Step 6: 【0187】 The device provides the generated response to the user. Specifically, it displays the response on the user interface, visually highlighting information that is particularly urgent or requires further detail. Here, the necessary information is presented clearly so that it is immediately available to the user. As a result of this step, the user can quickly obtain the information that meets their needs. 【0188】 (Application Example 2) 【0189】 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". 【0190】 In today's world, the importance of providing information that takes users' emotions into consideration is increasing, but conventional information processing systems have the challenge of not being able to provide optimal information that reflects the emotional state of users. Furthermore, when users try to acquire information while feeling anxious or stressed, conventional information presentation may not alleviate that anxiety, potentially resulting in decreased user satisfaction. Therefore, there is a need for means to quickly and effectively acquire the information that users need. 【0191】 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. 【0192】 In this invention, the server includes means for organizing information using natural language understanding technology, means for receiving and analyzing natural language inquiries from users to extract relevant linguistic elements, and means for analyzing the user's emotional state and optimizing the content and order of responses based on the results. This makes it possible to provide information optimized according to the user's emotional state. 【0193】 An "information processing device" is a system consisting of hardware and software for efficiently managing and processing information. 【0194】 "Natural language understanding technology" is a technology that mechanically analyzes human language and extracts structured information. 【0195】 A "user" is a person or organization that attempts to obtain information using the system. 【0196】 A "natural language query" is a question or request made to a system using the language that humans use in everyday life. 【0197】 "Analysis" is the act of breaking down and examining given data or information based on a specific purpose, and deriving its characteristics and relationships. 【0198】 "Linguistic elements" are elements such as words and phrases that have meaning and structure within natural language. 【0199】 "Exploration" is the process of identifying and retrieving information according to a specific purpose. 【0200】 An "information set" is a collection of data and information stored in various formats. 【0201】 A "response" is the answer or information that a system returns in response to an inquiry or request. 【0202】 A "user terminal" is a device or interface used by a user to receive output from an information processing device and perform operations on it. 【0203】 "Emotional state" refers to the psychological state that a user experiences in a particular situation, and includes feelings such as anxiety, joy, and urgency. 【0204】 "Optimization" refers to adjusting processes and results to their most efficient and effective state according to specific objectives and conditions. 【0205】 This invention relates to a system centered on an information processing device that enables the provision of information while considering the user's emotional state. The system includes a process of analyzing natural language inquiries from users and extracting relevant linguistic elements, utilizing sophisticated natural language understanding technology. 【0206】 The core server of the system performs natural language processing using spaCy and analyzes the user's emotional state using TextBlob. Based on the key keywords and emotional data obtained in this step, it efficiently searches for relevant information from data sets such as Firebase Realtime Database. Based on the searched information, the server uses Adobe's generative AI model to generate the most relevant response for the user. 【0207】 The user terminal is responsible for presenting the generated responses in both visual and textual formats. The interface on this terminal is optimized for rapid and appropriate information transmission, enabling the presentation of information that reflects the user's emotional state. 【0208】 For example, if a user makes a natural language inquiry such as, "I would like to know specific security advice for when I leave my home," the server will analyze this inquiry, extract relevant security information, and ultimately display advice on the user's device that will allow them to act with peace of mind. 【0209】 An example of a prompt used in a generative AI model would be, "Please provide steps and advice for optimizing home security while you are away." 【0210】 This system allows users to receive timely and accurate information optimized based on their emotions at the time. 【0211】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0212】 Step 1: 【0213】 The user enters a query in natural language via their device. The input sentence might be something like, "I'd like some specific security advice for when I'm away from home." This query is sent to the server as text data. 【0214】 Step 2: 【0215】 The server analyzes the received text data using the spaCy library. During this analysis, the server extracts key keywords and contextual information from the text. For example, keywords such as "home," "away," "security," and "advice" are obtained. This information is then used as input for the next process. 【0216】 Step 3: 【0217】 The server performs sentiment analysis on the parsed text data using TextBlob. In this step, the user's emotional state, such as the degree of anxiety or reassurance, is output as numerical data. This makes it possible to present information tailored to the user's psychological state. 【0218】 Step 4: 【0219】 The server uses the extracted keywords and sentiment data to search for relevant information in the Firebase Realtime Database. For example, it might search for information on crime prevention measures or the latest security guidelines. This information is then compiled as the server's output. 【0220】 Step 5: 【0221】 Based on the information explored, the server uses a generative AI model to generate the most suitable response for the user. The response generated at this stage is specific advice, such as "Placing security cameras would be effective." This response is then obtained as the next output. 【0222】 Step 6: 【0223】 The generated response is sent to the terminal and presented to the user. The terminal displays the response in a visually easy-to-understand format. The user can view this and obtain the information they need. 【0224】 This series of processes allows users to efficiently retrieve the most relevant information based on their emotional state and other relevant data. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 [Second Embodiment] 【0229】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0230】 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. 【0231】 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). 【0232】 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. 【0233】 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. 【0234】 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). 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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. 【0239】 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. 【0240】 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". 【0241】 This invention relates to an information management device for achieving effective information management within a company, and is based on the indexing, retrieval, and presentation of information using natural language processing technology. Embodiments of this invention will be described in detail below. 【0242】 The information management system has the function of collecting and managing data such as documents, project histories, FAQs, and operational manuals that exist within a company. This data is collected periodically by a server, and after necessary text cleaning is performed, it is indexed using natural language processing technology. This indexing makes the data quickly searchable. 【0243】 When a user requests specific internal company information and asks a question, the terminal receives the question. The question is entered in natural language and sent to the server. The server analyzes the question using natural language processing technology and extracts keywords related to the question. This keyword extraction is a crucial process for understanding the intent and context of the question. 【0244】 Based on the extracted keywords, the server searches for relevant information from its stored index. This search includes past project history and related documents. Based on the search results, the server automatically generates answers to the questions. The purpose of this answer generation is to provide users with easy-to-understand and directly relevant information. 【0245】 The generated answers are presented to the user via the device. The answers may include links to relevant documents and detailed information, allowing the user to efficiently carry out their tasks by referring to them. 【0246】 As a concrete example, consider a case where a user asks, "I want to know about the past marketing strategies for new product B." The terminal receives the question and sends it to the server. The server uses natural language processing to analyze the question and extracts keywords such as "new product B," "past," and "marketing strategy." Based on this, it searches the database and extracts relevant past project histories and reports. The server organizes the results and generates answers about specific past campaign examples and success stories, which the terminal then presents to the user. 【0247】 This invention enables the rapid and efficient acquisition and utilization of information within a company, allowing new employees and transferees to easily access internal knowledge. This prevents knowledge from becoming tied to specific individuals and improves work productivity. 【0248】 The following describes the processing flow. 【0249】 Step 1: 【0250】 The server periodically collects data from multiple sources within the company's information repository, including documents, project histories, FAQs, and operational manuals. This includes automated retrieval from file servers and databases. 【0251】 Step 2: 【0252】 The server performs text preprocessing on the collected data. This preprocessing cleans the data, removes noise, and standardizes character encoding. 【0253】 Step 3: 【0254】 The server applies natural language processing techniques to the pre-processed data, indexing the information through tokenization and morphological analysis. At this stage, the data is organized to facilitate searching. 【0255】 Step 4: 【0256】 When users need information, they input questions into their devices using natural language. 【0257】 Step 5: 【0258】 The terminal sends the question received from the user to the server. The question is provided in natural language format. 【0259】 Step 6: 【0260】 After receiving a user's question, the server analyzes it using natural language processing. Specifically, it extracts contextually relevant keywords to understand the intent of the question. 【0261】 Step 7: 【0262】 The server searches an already indexed database based on the extracted keywords and the context of the question. This is a process to quickly identify relevant information. 【0263】 Step 8: 【0264】 The server organizes the search results and selects the most relevant information. It applies a re-ranking algorithm to prioritize and output the most important information. 【0265】 Step 9: 【0266】 The server generates specific answers to user questions based on the selected information, including links to relevant documents. 【0267】 Step 10: 【0268】 The device presents the generated response to the user. Based on the information provided, the user can access the necessary details. 【0269】 This step allows users to quickly obtain the necessary information and carry out their work efficiently. 【0270】 (Example 1) 【0271】 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." 【0272】 Within companies, a vast amount of information is generated daily from diverse sources, making it crucial to quickly acquire necessary information and utilize it in business operations. However, systems that efficiently manage this information and provide immediate and appropriate answers when users naturally ask questions are not yet sufficiently developed. This challenge is particularly urgent in order to prevent information from becoming siloed and improve business productivity by making it easier for new employees and transferees to access internal knowledge. 【0273】 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. 【0274】 In this invention, the server includes means for acquiring information and formatting text data, means for indexing the information using natural language processing technology, and means for generating responses in natural language using a generative AI model. This makes it possible to quickly and accurately acquire the necessary information and present responses when a user gives instructions in natural language. 【0275】 "Methods for acquiring information and formatting text data" refers to the process of collecting information from various data sources dispersed within a company, removing unnecessary elements, and converting it into a format that is easy to analyze. 【0276】 "A method for indexing information using natural language processing technology" refers to a method of efficiently structuring collected and formatted text information into searchable data by analyzing it with a natural language processing algorithm. 【0277】 "A method for generating answers in natural language using a generative AI model" refers to the process of automatically generating grammatically correct and human-readable text using generative AI technology when providing answers to questions. 【0278】 "Means for identifying the business classification related to the instructions based on the analysis of the received instructions" refers to a technology that analyzes natural language instructions from users and identifies the business classification corresponding to the content of the instructions. 【0279】 "A means of re-evaluating search results and selecting and presenting the most relevant information" refers to a method of re-evaluating multiple search results obtained within the system from the perspective of importance and relevance, and prioritizing the provision of the most suitable information to the user. 【0280】 For the present invention to be implemented, information flow and data analysis between servers, terminals, and users are important. 【0281】 The server regularly collects the documents and business data distributed within the enterprise by using Internet technology. In this process, dedicated crawling software is utilized, and text cleaning is performed using natural language processing libraries such as OpenNLP and NLTK as needed. The collected data is indexed using Lucene or Elasticsearch and stored as data with enhanced searchability. 【0282】 When a user seeks specific information, they input a question in natural language through the user interface on the terminal. The terminal receives this question and sends it to the server. As an example at this time, it is conceivable that the user inputs "Tell me about the past marketing cases of new product B". 【0283】 When analyzing the received question, the server utilizes libraries such as spaCy and Transformers to extract highly relevant keywords from the question. Based on these keywords, relevant information is retrieved from the past project history and document database. 【0284】 The search results are re-evaluated by the server, and the most relevant information is selected. Then, using a generative AI model, such as GPT-3, an answer is automatically generated in a newly generated natural sentence. This answer includes relevant documents and links. 【0285】 The generated answer is presented to the user through the terminal. This mechanism enables the user to quickly obtain the information necessary for their work, leading to improved work productivity and prevention of knowledge personalization. 【0286】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0287】 Step 1: 【0288】 The server uses crawling technology to collect diverse documents and project histories within the company. Simultaneously, metadata for each document is collected and stored in a database. The input information comes from different formats and sources, but the output is text data in a unified format. 【0289】 Step 2: 【0290】 The server performs text cleaning on the collected raw data. This process uses the NLTK library to remove unnecessary symbols and line breaks and format the text. The output is cleaned data suitable for analysis. 【0291】 Step 3: 【0292】 The server applies natural language processing to the cleaned data and performs indexing. It uses Lucene to tokenize the data, assigns weights to each token, and generates an index. The input is cleaned text data, and the output is indexed, searchable data. 【0293】 Step 4: 【0294】 The user enters a question about specific information through the terminal's interface. This question is written in natural language and sent to the server as user instruction. The input is a question in natural language. 【0295】 Step 5: 【0296】 The server receives questions submitted by users and analyzes them using spaCy. It extracts relevant keywords from the questions and uses them to search for related information in an index. The input is a natural language question, and the output is a list of documents that match the relevant keywords. 【0297】 Step 6: 【0298】 The server re-evaluates the searched information and selects the most relevant content. Using a generative AI model, it automatically generates a response based on the selected information. This process utilizes generative models such as GPT-3. The input is a list of documents, and the output is a natural-sounding response. 【0299】 Step 7: 【0300】 The terminal receives the generated response from the server and presents it to the user. The user uses this information for their work and accesses relevant links and documents as needed. The input is the generated response, and the output is the response presentation screen that the user can view. 【0301】 (Application Example 1) 【0302】 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." 【0303】 In modern urban life, it is difficult for citizens to efficiently obtain information about urban public infrastructure. Furthermore, there is a lack of effective information provision systems for quickly grasping complex procedures and event information. This makes daily life inconvenient for citizens and hinders the smooth use of public services. 【0304】 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. 【0305】 In this invention, the server includes means for indexing information using natural language processing technology, means for analyzing user inquiries and extracting relevant keywords, means for searching for relevant information from a data storage device, and means for analyzing data and selecting and presenting optimal information to provide information about urban infrastructure. This enables citizens to efficiently obtain information about urban public infrastructure, thereby improving their daily convenience. 【0306】 The "information management unit" is the main component of a system equipped with functions for efficiently collecting, organizing, storing, and retrieving information. 【0307】 "Natural language processing technology" is a technology that enables a computer to understand and process the language that people use in their daily lives. 【0308】 "Indexing" is a process of organizing and systematizing information based on specific criteria in order to improve the search efficiency of information. 【0309】 "User" refers to an individual or organization that actually uses a system or service. 【0310】 "Query" is a question or request that a user inputs into a system in order to obtain information. 【0311】 "Analysis" is a process of decomposing data into its components and examining them to understand their meaning and relationships. 【0312】 "Relevant keywords" are important words or phrases extracted to identify the meaning and intention of a query. 【0313】 "Data storage device" is a device or system for storing digital data and retrieving it as needed. 【0314】 "Urban infrastructure" is a general term for transportation, communication, water supply, educational facilities, etc., which are the foundation for supporting urban life. 【0315】 "Components of information provision" are functions or processes for organizing and transmitting the information necessary for users. 【0316】 "Public infrastructure information" is information regarding the operation status and usage guidance of public services and facilities in a city or region. 【0317】 The system for realizing this invention includes an information management unit using natural language processing technology and operates on a device such as a smartphone as an application that provides citizens with information on the city's public infrastructure. The server first receives a natural language query from the user, analyzes the content using natural language processing technology, and extracts relevant keywords. For example, natural language processing libraries such as spaCy and NLTK are used for this purpose. Based on the extracted keywords, the server searches for relevant information from a cloud database (e.g., Firebase or AWS DynamoDB), which is a data storage device. 【0318】 The searched information is organized in a re-display order to provide more accurate information. The server uses a recommendation algorithm specialized for urban infrastructure information to select the most relevant information and provide it to the user's terminal. As a result, users can instantly access the necessary administrative information and infrastructure status. 【0319】 For example, if a user enters a question such as "Tell me about local events available this weekend," the generative AI model processes this question and retrieves relevant information using the prompt "Tell me about local events this weekend." This allows the user to quickly find details such as the location and date of the relevant event. 【0320】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0321】 Step 1: 【0322】 The terminal receives a natural language question from the user. This input data is sent directly to the server. This question contains a specific information need. 【0323】 Step 2: 【0324】 The server analyzes the received question using natural language processing techniques. This process extracts keywords using natural language processing libraries such as spaCy and NLTK to understand the intent of the question. The input is the text of the received question, and the output is a list of related keywords. 【0325】 Step 3: 【0326】 The server uses the extracted keywords to search for relevant information in cloud databases (e.g., Firebase, AWS DynamoDB). The search input is a list of keywords, and relevant results are output from the database. 【0327】 Step 4: 【0328】 The server organizes the searched information in a re-display order based on relevance. A recommendation algorithm is used for this organization. Data that matches more accurately is prioritized, generating an optimized list of information. 【0329】 Step 5: 【0330】 The server selects the most appropriate content from the organized information, constructs it in natural language, and provides it to the terminal. Here, a generative AI model is used to generate prompt sentences and present the information. At this stage, the input is organized information, and the output is the constructed response text. 【0331】 Step 6: 【0332】 The terminal displays the response text received from the server to the user. Based on this, the user can satisfy their information needs and obtain information about the city's public infrastructure. 【0333】 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. 【0334】 This invention provides a system for corporate information management that combines natural language processing technology and an emotion engine to deliver more user-friendly information. The embodiments of this invention are described below in detail. 【0335】 This information management system is server-driven, collecting information from various data sources within the company and indexing it using natural language processing technology. This indexing makes the information easily searchable. Users, during their work, can input questions in natural language when they need specific information, and query the system through their terminals. 【0336】 When the server receives a question from a user, it first analyzes the question using natural language processing techniques. This analysis extracts keywords and relevant contextual information, which are then used for searching. In this step, the sentiment engine recognizes the emotions from the user's question and extracts emotional data such as potential stress and urgency. 【0337】 Based on this data, the server searches the database for relevant information. It can adjust the order and amount of information presented, particularly by referencing sentiment data. For example, if a user is dissatisfied, the server can adjust the information to include more detailed and helpful explanations. 【0338】 The server then integrates the search results and automatically generates an answer to the question. This answer may include links to relevant documents and supplementary content. The generated answer is presented to the user via the terminal in an easy-to-understand format. 【0339】 As a concrete example, suppose a user asks a question such as, "I need the meeting notes from yesterday's Project X meeting, and I'm in a hurry." The server receives this question and uses natural language processing to extract keywords such as "Project X," "meeting notes," and "urgent," while also using an emotion engine to recognize the user's level of urgency. The server uses this information to quickly search for the relevant meeting notes and construct an automatically generated response that prioritizes displaying the necessary parts. The terminal then promptly presents the response to the user, taking into consideration the urgency of the matter. 【0340】 This system allows users to receive information optimized according to their emotional state at the time, significantly improving the efficiency of information acquisition in their work. Furthermore, by enhancing their ability to respond, especially in situations involving emotions, it becomes possible to create a more comfortable information environment. 【0341】 The following describes the processing flow. 【0342】 Step 1: 【0343】 The server periodically collects information from data sources within the company. This data includes documents, emails, project history, and FAQs. The collected data is indexed using natural language processing techniques to enable rapid searching. 【0344】 Step 2: 【0345】 Users input questions in natural language into their devices when they need information. The more specific the question, the more accurate the answer will be. 【0346】 Step 3: 【0347】 The terminal receives the question entered by the user and sends it to the server. The question is passed to the server as unstructured text data. 【0348】 Step 4: 【0349】 The server analyzes the received question using natural language processing techniques. This analysis extracts keywords and context from the question, forming the basis for information retrieval. 【0350】 Step 5: 【0351】 The server uses an emotion engine to recognize the emotions contained in the user's questions. This allows it to detect emotional data such as stress, urgency, and excitement, and to consider these factors when presenting information. 【0352】 Step 6: 【0353】 The server searches an indexed database using the extracted keywords and sentiment data. This process applies algorithms to quickly identify relevant materials. 【0354】 Step 7: 【0355】 The server aggregates search results and re-ranks them, taking into account the user's emotional state. Here, information is prioritized according to emotional data. 【0356】 Step 8: 【0357】 The server automatically generates appropriate answers to questions based on the selected information. These generated answers also include links to relevant additional information and documentation. 【0358】 Step 9: 【0359】 The device presents the generated answers to the user. This presentation is designed to be clear and efficient so that the user can immediately utilize the information. 【0360】 Specifically, through this process, users can receive emotionally sensitive responses, reducing stress during information acquisition. Furthermore, information prioritization is optimized according to the user's situation, and necessary information is provided quickly. 【0361】 (Example 2) 【0362】 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". 【0363】 There is a need to effectively manage information resources scattered throughout the company and provide an environment where users can quickly and accurately obtain information. Furthermore, it is necessary to improve work efficiency by presenting information in a way that is tailored to the user's emotions and the urgency of their questions. 【0364】 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. 【0365】 In this invention, the server includes means for organizing information using natural language processing technology, means for analyzing user questions and extracting relevant features, and means for retrieving relevant information from data storage using emotional information and generating a tailored response. This enables the provision of optimal information in response to user questions, and in particular, enables the accurate presentation of information according to emotions and urgency. 【0366】 An "information processing device" is a device that effectively manages information within a company and quickly provides users with the information they need. 【0367】 "Natural language processing technology" is a technology that enables computers to understand human language, and involves analyzing text data to understand its meaning and structure. 【0368】 "Feature extraction" means extracting useful information and data points for analysis from the input data. 【0369】 "Emotional information" refers to data used to determine emotions and urgency from user input, and is taken into consideration when presenting information. 【0370】 "Data storage area" refers to the location or system where information is stored, and is used during retrieval. 【0371】 "Searching for related information" means finding useful data or documents from data storage in response to a query. 【0372】 A "tailored response" is a response customized according to the user's emotions and the content of their question, with the aim of providing the most relevant information. 【0373】 This invention is a system for streamlining information processing within a company and providing optimized information in response to user inquiries. This system primarily consists of servers, terminals, and users, and aims to effectively manage and provide information. 【0374】 First, the server collects information from various data sources within the company. These data sources include document management systems, email, and databases. The server then analyzes and indexes this information using natural language processing technologies (e.g., spaCy or NLTK). This organizes the information into an efficiently searchable format. 【0375】 Next, the user inputs a question in natural language and sends it to the server via their device. This input is done using a web browser or desktop application. The server analyzes the user's question and extracts key keywords and relevant contextual information. Furthermore, it uses an emotion engine to identify emotional information from the user's question, extracting emotional data such as urgency and dissatisfaction. 【0376】 Based on the extracted information, the server searches the database for relevant information. During this process, the search results are adjusted based on sentiment data. For example, if high urgency is recognized, the search is optimized to prioritize the presentation of necessary information. Then, using the search results, the server automatically generates answers to the user's questions using a generative AI model (e.g., a GPT model). These answers may include links to relevant additional information and documents. 【0377】 Finally, the device provides the generated response to the user, presenting it in a format that takes the user's situation into particular. This allows the user to quickly and effectively obtain the necessary information. 【0378】 As a concrete example, consider a scenario where a user asks a question such as, "I urgently need the meeting notes from yesterday's Project X." In this case, the server analyzes the question and extracts keywords such as "Project X," "meeting notes," and "urgent," along with an indication of urgency. Based on this information, it quickly searches for relevant meeting notes and displays them preferentially through the user's device. 【0379】 An example of a prompt message is, "Please provide the latest information on Project X. I'm in a hurry." This example demonstrates how to provide optimal information tailored to the user's needs. 【0380】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0381】 Step 1: 【0382】 The server collects information from data sources within the company (e.g., document management systems, email, databases). At this stage, it receives raw data obtained from the data sources as input. Subsequently, the collected data is analyzed using natural language processing techniques (e.g., spaCy, NLTK), organized, and indexed. Specifically, it extracts topics and keywords from the text and generates an index to facilitate information retrieval. As a result, the indexed data is output. 【0383】 Step 2: 【0384】 Users input necessary information for their work and send natural language questions to the server using their terminal. At this time, the user's specific questions and information requests (e.g., "Please provide the meeting notes for Project X.") are treated as input. 【0385】 Step 3: 【0386】 The server analyzes the received question. This analysis uses natural language processing techniques to extract key keywords and relevant contextual information from the input text. Specifically, this step involves tokenizing the text and performing morphological and dependency analysis. Then, an emotion engine is used to identify the user's emotional information (e.g., urgency, dissatisfaction) from the question. As a result of the analysis, keywords and emotional information are output. 【0387】 Step 4: 【0388】 Based on the analysis results from step 3, the server searches for relevant information from the indexed database. Specifically, it performs keyword matching and relevance scoring, and prioritizes information while considering sentiment. This search operation outputs highly relevant and well-organized information. 【0389】 Step 5: 【0390】 The server uses the search results to automatically generate answers to the user's questions using a generative AI model (e.g., a GPT-based model). This step integrates the search results with generative AI technology to generate contextually natural-sounding text. The final answer may include links to relevant documents and supplementary information, which are then output. 【0391】 Step 6: 【0392】 The device provides the generated response to the user. Specifically, it displays the response on the user interface, visually highlighting information that is particularly urgent or requires further detail. Here, the necessary information is presented clearly so that it is immediately available to the user. As a result of this step, the user can quickly obtain the information that meets their needs. 【0393】 (Application Example 2) 【0394】 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." 【0395】 In today's world, the importance of providing information that takes users' emotions into consideration is increasing, but conventional information processing systems have the challenge of not being able to provide optimal information that reflects the emotional state of users. Furthermore, when users try to acquire information while feeling anxious or stressed, conventional information presentation may not alleviate that anxiety, potentially resulting in decreased user satisfaction. Therefore, there is a need for means to quickly and effectively acquire the information that users need. 【0396】 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. 【0397】 In this invention, the server includes means for organizing information using natural language understanding technology, means for receiving and analyzing natural language inquiries from users to extract relevant linguistic elements, and means for analyzing the user's emotional state and optimizing the content and order of responses based on the results. This makes it possible to provide information optimized according to the user's emotional state. 【0398】 An "information processing device" is a system consisting of hardware and software for efficiently managing and processing information. 【0399】 "Natural language understanding technology" is a technology that mechanically analyzes human language and extracts structured information. 【0400】 A "user" is a person or organization that attempts to obtain information using the system. 【0401】 A "natural language query" is a question or request made to a system using the language that humans use in everyday life. 【0402】 "Analysis" is the act of breaking down and examining given data or information based on a specific purpose, and deriving its characteristics and relationships. 【0403】 "Linguistic elements" are elements such as words and phrases that have meaning and structure within natural language. 【0404】 "Exploration" is the process of identifying and retrieving information according to a specific purpose. 【0405】 An "information set" is a collection of data and information stored in various formats. 【0406】 A "response" is the answer or information that a system returns in response to an inquiry or request. 【0407】 A "user terminal" is a device or interface used by a user to receive output from an information processing device and perform operations on it. 【0408】 "Emotional state" refers to the psychological state that a user experiences in a particular situation, and includes feelings such as anxiety, joy, and urgency. 【0409】 "Optimization" refers to adjusting processes and results to their most efficient and effective state according to specific objectives and conditions. 【0410】 This invention relates to a system centered on an information processing device that enables the provision of information while considering the user's emotional state. The system includes a process of analyzing natural language inquiries from users and extracting relevant linguistic elements, utilizing sophisticated natural language understanding technology. 【0411】 The core server of the system performs natural language processing using spaCy and analyzes the user's emotional state using TextBlob. Based on the key keywords and emotional data obtained in this step, it efficiently searches for relevant information from data sets such as Firebase Realtime Database. Based on the searched information, the server uses Adobe's generative AI model to generate the most relevant response for the user. 【0412】 The user terminal is responsible for presenting the generated responses in both visual and textual formats. The interface on this terminal is optimized for rapid and appropriate information transmission, enabling the presentation of information that reflects the user's emotional state. 【0413】 For example, if a user makes a natural language inquiry such as, "I would like to know specific security advice for when I leave my home," the server will analyze this inquiry, extract relevant security information, and ultimately display advice on the user's device that will allow them to act with peace of mind. 【0414】 An example of a prompt used in a generative AI model would be, "Please provide steps and advice for optimizing home security while you are away." 【0415】 This system allows users to receive timely and accurate information optimized based on their emotions at the time. 【0416】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0417】 Step 1: 【0418】 The user enters a query in natural language via their device. The input sentence might be something like, "I'd like some specific security advice for when I'm away from home." This query is sent to the server as text data. 【0419】 Step 2: 【0420】 The server analyzes the received text data using the spaCy library. During this analysis, the server extracts key keywords and contextual information from the text. For example, keywords such as "home," "away," "security," and "advice" are obtained. This information is then used as input for the next process. 【0421】 Step 3: 【0422】 The server performs sentiment analysis on the parsed text data using TextBlob. In this step, the user's emotional state, such as the degree of anxiety or reassurance, is output as numerical data. This makes it possible to present information tailored to the user's psychological state. 【0423】 Step 4: 【0424】 The server uses the extracted keywords and sentiment data to search for relevant information in the Firebase Realtime Database. For example, it might search for information on crime prevention measures or the latest security guidelines. This information is then compiled as the server's output. 【0425】 Step 5: 【0426】 Based on the information explored, the server uses a generative AI model to generate the most suitable response for the user. The response generated at this stage is specific advice, such as "Placing security cameras would be effective." This response is then obtained as the next output. 【0427】 Step 6: 【0428】 The generated response is sent to the terminal and presented to the user. The terminal displays the response in a visually easy-to-understand format. The user can view this and obtain the information they need. 【0429】 This series of processes allows users to efficiently obtain the most relevant information based on their emotional state and other relevant data. 【0430】 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. 【0431】 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. 【0432】 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. 【0433】 [Third Embodiment] 【0434】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0435】 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. 【0436】 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). 【0437】 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. 【0438】 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. 【0439】 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). 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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. 【0444】 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. 【0445】 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". 【0446】 This invention relates to an information management device for achieving effective information management within a company, and is based on the indexing, retrieval, and presentation of information using natural language processing technology. Embodiments of this invention will be described in detail below. 【0447】 The information management system has the function of collecting and managing data such as documents, project histories, FAQs, and operational manuals that exist within a company. This data is collected periodically by a server, and after necessary text cleaning is performed, it is indexed using natural language processing technology. This indexing makes the data quickly searchable. 【0448】 When a user requests specific internal company information and asks a question, the terminal receives the question. The question is entered in natural language and sent to the server. The server analyzes the question using natural language processing technology and extracts keywords related to the question. This keyword extraction is a crucial process for understanding the intent and context of the question. 【0449】 Based on the extracted keywords, the server searches for relevant information from its stored index. This search includes past project history and related documents. Based on the search results, the server automatically generates answers to the questions. The purpose of this answer generation is to provide users with easy-to-understand and directly relevant information. 【0450】 The generated answers are presented to the user via the device. The answers may include links to relevant documents and detailed information, allowing the user to efficiently carry out their tasks by referring to them. 【0451】 As a concrete example, consider a case where a user asks, "I want to know about the past marketing strategies for new product B." The terminal receives the question and sends it to the server. The server uses natural language processing to analyze the question and extracts keywords such as "new product B," "past," and "marketing strategy." Based on this, it searches the database and extracts relevant past project histories and reports. The server organizes the results and generates answers about specific past campaign examples and success stories, which the terminal then presents to the user. 【0452】 This invention enables the rapid and efficient acquisition and utilization of information within a company, allowing new employees and transferees to easily access internal knowledge. This prevents knowledge from becoming tied to specific individuals and improves work productivity. 【0453】 The following describes the processing flow. 【0454】 Step 1: 【0455】 The server periodically collects data from multiple sources within the company's information repository, including documents, project histories, FAQs, and operational manuals. This includes automated retrieval from file servers and databases. 【0456】 Step 2: 【0457】 The server performs text preprocessing on the collected data. This preprocessing cleans the data, removes noise, and standardizes character encoding. 【0458】 Step 3: 【0459】 The server applies natural language processing techniques to the pre-processed data, indexing the information through tokenization and morphological analysis. At this stage, the data is organized to facilitate searching. 【0460】 Step 4: 【0461】 When users need information, they input questions into their devices using natural language. 【0462】 Step 5: 【0463】 The terminal sends the question received from the user to the server. The question is provided in natural language format. 【0464】 Step 6: 【0465】 After receiving a user's question, the server analyzes it using natural language processing. Specifically, it extracts contextually relevant keywords to understand the intent of the question. 【0466】 Step 7: 【0467】 The server searches an already indexed database based on the extracted keywords and the context of the question. This is a process to quickly identify relevant information. 【0468】 Step 8: 【0469】 The server organizes the search results and selects the most relevant information. It applies a re-ranking algorithm to prioritize and output the most important information. 【0470】 Step 9: 【0471】 The server generates specific answers to user questions based on the selected information, including links to relevant documents. 【0472】 Step 10: 【0473】 The device presents the generated response to the user. Based on the information provided, the user can access the necessary details. 【0474】 This step allows users to quickly obtain the necessary information and carry out their work efficiently. 【0475】 (Example 1) 【0476】 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." 【0477】 Within companies, a vast amount of information is generated daily from diverse sources, making it crucial to quickly acquire necessary information and utilize it in business operations. However, systems that efficiently manage this information and provide immediate and appropriate answers when users naturally ask questions are not yet sufficiently developed. This challenge is particularly urgent in order to prevent information from becoming siloed and improve business productivity by making it easier for new employees and transferees to access internal knowledge. 【0478】 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. 【0479】 In this invention, the server includes means for acquiring information and formatting text data, means for indexing the information using natural language processing technology, and means for generating responses in natural language using a generative AI model. This makes it possible to quickly and accurately acquire the necessary information and present responses when a user gives instructions in natural language. 【0480】 "Methods for acquiring information and formatting text data" refers to the process of collecting information from various data sources dispersed within a company, removing unnecessary elements, and converting it into a format that is easy to analyze. 【0481】 "A method for indexing information using natural language processing technology" refers to a method of efficiently structuring collected and formatted text information into searchable data by analyzing it with a natural language processing algorithm. 【0482】 "A method for generating answers in natural language using a generative AI model" refers to the process of automatically generating grammatically correct and human-readable text using generative AI technology when providing answers to questions. 【0483】 "Means for identifying the business classification related to the instructions based on the analysis of the received instructions" refers to a technology that analyzes natural language instructions from users and identifies the business classification corresponding to the content of the instructions. 【0484】 "A means of re-evaluating search results and selecting and presenting the most relevant information" refers to a method of re-evaluating multiple search results obtained within the system from the perspective of importance and relevance, and prioritizing the provision of the most suitable information to the user. 【0485】 For the present invention to be implemented, information flow and data analysis between servers, terminals, and users are important. 【0486】 The server periodically collects documents and business data distributed within the company using internet technology. This process utilizes dedicated crawling software and performs text cleaning as needed using natural language processing libraries such as OpenNLP and NLTK. The collected data is indexed using Lucene or Elasticsearch and stored as data with enhanced searchability. 【0487】 When a user requests specific information, they input their question in natural language through the user interface on their device. The device receives this question and sends it to the server. For example, a user might input, "Please tell me about past marketing cases for the new product B." 【0488】 When the server analyzes the received questions, it uses libraries such as spaCy and Transformers to extract highly relevant keywords from the questions. Based on these keywords, it searches for relevant information in past project history and document databases. 【0489】 The search results are re-evaluated on the server, and the most relevant information is selected. Then, a generative AI model, such as GPT-3, is used to automatically generate answers in newly generated, natural-sounding text. These answers include relevant documents and links. 【0490】 The generated answers are presented to the user via the terminal. This system allows users to quickly obtain the information necessary for their work, leading to improved work productivity and preventing knowledge from becoming tied to specific individuals. 【0491】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0492】 Step 1: 【0493】 The server uses crawling technology to collect diverse documents and project histories within the company. Simultaneously, metadata for each document is collected and stored in a database. The input information comes from different formats and sources, but the output is text data in a unified format. 【0494】 Step 2: 【0495】 The server performs text cleaning on the collected raw data. This process uses the NLTK library to remove unnecessary symbols and line breaks and format the text. The output is cleaned data suitable for analysis. 【0496】 Step 3: 【0497】 The server applies natural language processing to the cleaned data and performs indexing. It uses Lucene to tokenize the data, assigns weights to each token, and generates an index. The input is cleaned text data, and the output is indexed, searchable data. 【0498】 Step 4: 【0499】 The user enters a question about specific information through the terminal's interface. This question is written in natural language and sent to the server as user instruction. The input is a question in natural language. 【0500】 Step 5: 【0501】 The server receives questions submitted by users and analyzes them using spaCy. It extracts relevant keywords from the questions and uses them to search for related information in an index. The input is a natural language question, and the output is a list of documents that match the relevant keywords. 【0502】 Step 6: 【0503】 The server re-evaluates the searched information and selects the most relevant content. Using a generative AI model, it automatically generates a response based on the selected information. This process utilizes generative models such as GPT-3. The input is a list of documents, and the output is a natural-sounding response. 【0504】 Step 7: 【0505】 The terminal receives the generated response from the server and presents it to the user. The user uses this information for their work and accesses relevant links and documents as needed. The input is the generated response, and the output is the response presentation screen that the user can view. 【0506】 (Application Example 1) 【0507】 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." 【0508】 In modern urban life, it is difficult for citizens to efficiently obtain information about urban public infrastructure. Furthermore, there is a lack of effective information provision systems for quickly grasping complex procedures and event information. This makes daily life inconvenient for citizens and hinders the smooth use of public services. 【0509】 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. 【0510】 In this invention, the server includes means for indexing information using natural language processing technology, means for analyzing user inquiries and extracting relevant keywords, means for searching for relevant information from a data storage device, and means for analyzing data and selecting and presenting optimal information to provide information about urban infrastructure. This enables citizens to efficiently obtain information about urban public infrastructure, thereby improving their daily convenience. 【0511】 An "information management unit" is a key component of a system equipped with functions for efficiently collecting, organizing, storing, and retrieving information. 【0512】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0513】 "Indexing" is the process of organizing and systematizing information based on specific criteria in order to improve the efficiency of information retrieval. 【0514】 "Users" refer to individuals or organizations that actually use the system or service. 【0515】 A "question" is a question or request that a user enters into the system to obtain information. 【0516】 "Analysis" is the process of breaking down data into its constituent elements, examining them, and understanding their meaning and relationships. 【0517】 "Related keywords" are important words or phrases extracted to identify the meaning and intent of a question. 【0518】 A "data storage device" is a device or system for storing digital data and retrieving it as needed. 【0519】 "Urban infrastructure" is a general term for the foundations that support urban life, such as transportation, communication, water supply, and educational facilities. 【0520】 "Components of information provision" refer to the functions and processes for organizing and communicating the information necessary for users. 【0521】 "Public infrastructure information" refers to information regarding the operation status and usage guidelines of public services and facilities in cities and regions. 【0522】 The system for realizing this invention includes an information management unit using natural language processing technology and operates on a device such as a smartphone as an application that provides citizens with information on the city's public infrastructure. The server first receives a natural language query from the user, analyzes the content using natural language processing technology, and extracts relevant keywords. For example, natural language processing libraries such as spaCy and NLTK are used for this purpose. Based on the extracted keywords, the server searches for relevant information from a cloud database (e.g., Firebase or AWS DynamoDB), which is a data storage device. 【0523】 The searched information is organized in a re-display order to provide more accurate information. The server uses a recommendation algorithm specialized for urban infrastructure information to select the most relevant information and provide it to the user's terminal. As a result, users can instantly access the necessary administrative information and infrastructure status. 【0524】 For example, if a user enters a question such as "Tell me about local events available this weekend," the generative AI model processes this question and retrieves relevant information using the prompt "Tell me about local events this weekend." This allows the user to quickly find details such as the location and date of the relevant event. 【0525】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0526】 Step 1: 【0527】 The terminal receives a natural language question from the user. This input data is sent directly to the server. This question contains a specific information need. 【0528】 Step 2: 【0529】 The server analyzes the received question using natural language processing techniques. This process extracts keywords using natural language processing libraries such as spaCy and NLTK to understand the intent of the question. The input is the text of the received question, and the output is a list of related keywords. 【0530】 Step 3: 【0531】 The server uses the extracted keywords to search for relevant information in cloud databases (e.g., Firebase, AWS DynamoDB). The search input is a list of keywords, and relevant results are output from the database. 【0532】 Step 4: 【0533】 The server organizes the searched information in a re-display order based on relevance. A recommendation algorithm is used for this organization. Data that matches more accurately is prioritized, generating an optimized list of information. 【0534】 Step 5: 【0535】 The server selects the most appropriate content from the organized information, constructs it in natural language, and provides it to the terminal. Here, a generative AI model is used to generate prompt sentences and present the information. At this stage, the input is organized information, and the output is the constructed response text. 【0536】 Step 6: 【0537】 The terminal displays the response text received from the server to the user. Based on this, the user can satisfy their information needs and obtain information about the city's public infrastructure. 【0538】 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. 【0539】 This invention provides a system for corporate information management that combines natural language processing technology and an emotion engine to deliver more user-friendly information. The embodiments of this invention are described below in detail. 【0540】 This information management system is server-driven, collecting information from various data sources within the company and indexing it using natural language processing technology. This indexing makes the information easily searchable. Users, during their work, can input questions in natural language when they need specific information, and query the system through their terminals. 【0541】 When the server receives a question from a user, it first analyzes the question using natural language processing techniques. This analysis extracts keywords and relevant contextual information, which are then used for searching. In this step, the sentiment engine recognizes the emotions from the user's question and extracts emotional data such as potential stress and urgency. 【0542】 Based on this data, the server searches the database for relevant information. It can adjust the order and amount of information presented, particularly by referencing sentiment data. For example, if a user is dissatisfied, the server can adjust the information to include more detailed and helpful explanations. 【0543】 The server then integrates the search results and automatically generates an answer to the question. This answer may include links to relevant documents and supplementary content. The generated answer is presented to the user via the terminal in an easy-to-understand format. 【0544】 As a concrete example, suppose a user asks a question such as, "I need the meeting notes from yesterday's Project X meeting, and I'm in a hurry." The server receives this question and uses natural language processing to extract keywords such as "Project X," "meeting notes," and "urgent," while also using an emotion engine to recognize the user's level of urgency. The server uses this information to quickly search for the relevant meeting notes and construct an automatically generated response that prioritizes displaying the necessary parts. The terminal then promptly presents the response to the user, taking into consideration the urgency of the matter. 【0545】 This system allows users to receive information optimized according to their emotional state at the time, significantly improving the efficiency of information acquisition in their work. Furthermore, by enhancing their ability to respond, especially in situations involving emotions, it becomes possible to create a more comfortable information environment. 【0546】 The following describes the processing flow. 【0547】 Step 1: 【0548】 The server periodically collects information from data sources within the company. This data includes documents, emails, project history, and FAQs. The collected data is indexed using natural language processing techniques to enable rapid searching. 【0549】 Step 2: 【0550】 Users input questions in natural language into their devices when they need information. The more specific the question, the more accurate the answer will be. 【0551】 Step 3: 【0552】 The terminal receives the question entered by the user and sends it to the server. The question is passed to the server as unstructured text data. 【0553】 Step 4: 【0554】 The server analyzes the received question using natural language processing techniques. This analysis extracts keywords and context from the question, forming the basis for information retrieval. 【0555】 Step 5: 【0556】 The server uses an emotion engine to recognize the emotions contained in the user's questions. This allows it to detect emotional data such as stress, urgency, and excitement, and to consider these factors when presenting information. 【0557】 Step 6: 【0558】 The server searches an indexed database using the extracted keywords and sentiment data. This process applies algorithms to quickly identify relevant materials. 【0559】 Step 7: 【0560】 The server aggregates search results and re-ranks them, taking into account the user's emotional state. Here, information is prioritized according to emotional data. 【0561】 Step 8: 【0562】 The server automatically generates appropriate answers to questions based on the selected information. These generated answers also include links to relevant additional information and documentation. 【0563】 Step 9: 【0564】 The device presents the generated answers to the user. This presentation is designed to be clear and efficient so that the user can immediately utilize the information. 【0565】 Specifically, through this process, users can receive emotionally sensitive responses, reducing stress during information acquisition. Furthermore, information prioritization is optimized according to the user's situation, and necessary information is provided quickly. 【0566】 (Example 2) 【0567】 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." 【0568】 There is a need to effectively manage information resources scattered throughout the company and provide an environment where users can quickly and accurately obtain information. Furthermore, it is necessary to improve work efficiency by presenting information in a way that is tailored to the user's emotions and the urgency of their questions. 【0569】 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. 【0570】 In this invention, the server includes means for organizing information using natural language processing technology, means for analyzing user questions and extracting relevant features, and means for retrieving relevant information from data storage using emotional information and generating a tailored response. This enables the provision of optimal information in response to user questions, and in particular, enables the accurate presentation of information according to emotions and urgency. 【0571】 An "information processing device" is a device that effectively manages information within a company and quickly provides users with the information they need. 【0572】 "Natural language processing technology" is a technology that enables computers to understand human language, and involves analyzing text data to understand its meaning and structure. 【0573】 "Feature extraction" means extracting useful information and data points for analysis from the input data. 【0574】 "Emotional information" refers to data used to determine emotions and urgency from user input, and is taken into consideration when presenting information. 【0575】 "Data storage area" refers to the location or system where information is stored, and is used during retrieval. 【0576】 "Searching for related information" means finding useful data or documents from data storage in response to a query. 【0577】 A "tailored response" is a response customized according to the user's emotions and the content of their question, with the aim of providing the most relevant information. 【0578】 This invention is a system for streamlining information processing within a company and providing optimized information in response to user inquiries. This system primarily consists of servers, terminals, and users, and aims to effectively manage and provide information. 【0579】 First, the server collects information from various data sources within the company. These data sources include document management systems, email, and databases. The server then analyzes and indexes this information using natural language processing technologies (e.g., spaCy or NLTK). This organizes the information into an efficiently searchable format. 【0580】 Next, the user inputs a question in natural language and sends it to the server via their device. This input is done using a web browser or desktop application. The server analyzes the user's question and extracts key keywords and relevant contextual information. Furthermore, it uses an emotion engine to identify emotional information from the user's question, extracting emotional data such as urgency and dissatisfaction. 【0581】 Based on the extracted information, the server searches the database for relevant information. During this process, the search results are adjusted based on sentiment data. For example, if high urgency is recognized, the search is optimized to prioritize the presentation of necessary information. Then, using the search results, the server automatically generates answers to the user's questions using a generative AI model (e.g., a GPT model). These answers may include links to relevant additional information and documents. 【0582】 Finally, the device provides the generated response to the user, presenting it in a format that takes the user's situation into particular. This allows the user to quickly and effectively obtain the necessary information. 【0583】 As a concrete example, consider a scenario where a user asks a question such as, "I urgently need the meeting notes from yesterday's Project X." In this case, the server analyzes the question and extracts keywords such as "Project X," "meeting notes," and "urgent," along with an indication of urgency. Based on this information, it quickly searches for relevant meeting notes and displays them preferentially through the user's device. 【0584】 An example of a prompt message is, "Please provide the latest information on Project X. I'm in a hurry." This example demonstrates how to provide optimal information tailored to the user's needs. 【0585】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0586】 Step 1: 【0587】 The server collects information from data sources within the company (e.g., document management systems, email, databases). At this stage, it receives raw data obtained from the data sources as input. Subsequently, the collected data is analyzed using natural language processing techniques (e.g., spaCy, NLTK), organized, and indexed. Specifically, it extracts topics and keywords from the text and generates an index to facilitate information retrieval. As a result, the indexed data is output. 【0588】 Step 2: 【0589】 Users input necessary information for their work and send natural language questions to the server using their terminal. At this time, the user's specific questions and information requests (e.g., "Please provide the meeting notes for Project X.") are treated as input. 【0590】 Step 3: 【0591】 The server analyzes the received question. This analysis uses natural language processing techniques to extract key keywords and relevant contextual information from the input text. Specifically, this step involves tokenizing the text and performing morphological and dependency analysis. Then, an emotion engine is used to identify the user's emotional information (e.g., urgency, dissatisfaction) from the question. As a result of the analysis, keywords and emotional information are output. 【0592】 Step 4: 【0593】 Based on the analysis results from step 3, the server searches for relevant information from the indexed database. Specifically, it performs keyword matching and relevance scoring, and prioritizes information while considering sentiment. This search operation outputs highly relevant and well-organized information. 【0594】 Step 5: 【0595】 The server uses the search results to automatically generate answers to the user's questions using a generative AI model (e.g., a GPT-based model). This step integrates the search results with generative AI technology to generate contextually natural-sounding text. The final answer may include links to relevant documents and supplementary information, which are then output. 【0596】 Step 6: 【0597】 The device provides the generated response to the user. Specifically, it displays the response on the user interface, visually highlighting information that is particularly urgent or requires further detail. Here, the necessary information is presented clearly so that it is immediately available to the user. As a result of this step, the user can quickly obtain the information that meets their needs. 【0598】 (Application Example 2) 【0599】 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." 【0600】 In today's world, the importance of providing information that takes users' emotions into consideration is increasing, but conventional information processing systems have the challenge of not being able to provide optimal information that reflects the emotional state of users. Furthermore, when users try to acquire information while feeling anxious or stressed, conventional information presentation may not alleviate that anxiety, potentially resulting in decreased user satisfaction. Therefore, there is a need for means to quickly and effectively acquire the information that users need. 【0601】 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. 【0602】 In this invention, the server includes means for organizing information using natural language understanding technology, means for receiving and analyzing natural language inquiries from users to extract relevant linguistic elements, and means for analyzing the user's emotional state and optimizing the content and order of responses based on the results. This makes it possible to provide information optimized according to the user's emotional state. 【0603】 An "information processing device" is a system consisting of hardware and software for efficiently managing and processing information. 【0604】 "Natural language understanding technology" is a technology that mechanically analyzes human language and extracts structured information. 【0605】 A "user" is a person or organization that attempts to obtain information using the system. 【0606】 A "natural language query" is a question or request made to a system using the language that humans use in everyday life. 【0607】 "Analysis" is the act of breaking down and examining given data or information based on a specific purpose, and deriving its characteristics and relationships. 【0608】 "Linguistic elements" are elements such as words and phrases that have meaning and structure within natural language. 【0609】 "Exploration" is the process of identifying and retrieving information according to a specific purpose. 【0610】 An "information set" is a collection of data and information stored in various formats. 【0611】 A "response" is the answer or information that a system returns in response to an inquiry or request. 【0612】 A "user terminal" is a device or interface used by a user to receive output from an information processing device and perform operations on it. 【0613】 "Emotional state" refers to the psychological state that a user experiences in a particular situation, and includes feelings such as anxiety, joy, and urgency. 【0614】 "Optimization" refers to adjusting processes and results to their most efficient and effective state according to specific objectives and conditions. 【0615】 This invention relates to a system centered on an information processing device that enables the provision of information while considering the user's emotional state. The system includes a process of analyzing natural language inquiries from users and extracting relevant linguistic elements, utilizing sophisticated natural language understanding technology. 【0616】 The core server of the system performs natural language processing using spaCy and analyzes the user's emotional state using TextBlob. Based on the key keywords and emotional data obtained in this step, it efficiently searches for relevant information from data sets such as Firebase Realtime Database. Based on the searched information, the server uses Adobe's generative AI model to generate the most relevant response for the user. 【0617】 The user terminal is responsible for presenting the generated responses in both visual and textual formats. The interface on this terminal is optimized for rapid and appropriate information transmission, enabling the presentation of information that reflects the user's emotional state. 【0618】 For example, if a user makes a natural language inquiry such as, "I would like to know specific security advice for when I leave my home," the server will analyze this inquiry, extract relevant security information, and ultimately display advice on the user's device that will allow them to act with peace of mind. 【0619】 An example of a prompt used in a generative AI model would be, "Please provide steps and advice for optimizing home security while you are away." 【0620】 This system allows users to receive timely and accurate information optimized based on their emotions at the time. 【0621】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0622】 Step 1: 【0623】 The user enters a query in natural language via their device. The input sentence might be something like, "I'd like some specific security advice for when I'm away from home." This query is sent to the server as text data. 【0624】 Step 2: 【0625】 The server analyzes the received text data using the spaCy library. During this analysis, the server extracts key keywords and contextual information from the text. For example, keywords such as "home," "away," "security," and "advice" are obtained. This information is then used as input for the next process. 【0626】 Step 3: 【0627】 The server performs sentiment analysis on the parsed text data using TextBlob. In this step, the user's emotional state, such as the degree of anxiety or reassurance, is output as numerical data. This makes it possible to present information tailored to the user's psychological state. 【0628】 Step 4: 【0629】 The server uses the extracted keywords and sentiment data to search for relevant information in the Firebase Realtime Database. For example, it might search for information on crime prevention measures or the latest security guidelines. This information is then compiled as the server's output. 【0630】 Step 5: 【0631】 Based on the information explored, the server uses a generative AI model to generate the most suitable response for the user. The response generated at this stage is specific advice, such as "Placing security cameras would be effective." This response is then obtained as the next output. 【0632】 Step 6: 【0633】 The generated response is sent to the terminal and presented to the user. The terminal displays the response in a visually easy-to-understand format. The user can view this and obtain the information they need. 【0634】 This series of processes allows users to efficiently obtain the most relevant information based on their emotional state and other relevant data. 【0635】 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. 【0636】 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. 【0637】 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. 【0638】 [Fourth Embodiment] 【0639】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0640】 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. 【0641】 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). 【0642】 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. 【0643】 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. 【0644】 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). 【0645】 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. 【0646】 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. 【0647】 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. 【0648】 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. 【0649】 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. 【0650】 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. 【0651】 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". 【0652】 This invention relates to an information management device for achieving effective information management within a company, and is based on the indexing, retrieval, and presentation of information using natural language processing technology. Embodiments of this invention will be described in detail below. 【0653】 The information management system has the function of collecting and managing data such as documents, project histories, FAQs, and operational manuals that exist within a company. This data is collected periodically by a server, and after necessary text cleaning is performed, it is indexed using natural language processing technology. This indexing makes the data quickly searchable. 【0654】 When a user requests specific internal company information and asks a question, the terminal receives the question. The question is entered in natural language and sent to the server. The server analyzes the question using natural language processing technology and extracts keywords related to the question. This keyword extraction is a crucial process for understanding the intent and context of the question. 【0655】 Based on the extracted keywords, the server searches for relevant information from its stored index. This search includes past project history and related documents. Based on the search results, the server automatically generates answers to the questions. The purpose of this answer generation is to provide users with easy-to-understand and directly relevant information. 【0656】 The generated answers are presented to the user via the device. The answers may include links to relevant documents and detailed information, allowing the user to efficiently carry out their tasks by referring to them. 【0657】 As a concrete example, consider a case where a user asks, "I want to know about the past marketing strategies for new product B." The terminal receives the question and sends it to the server. The server uses natural language processing to analyze the question and extracts keywords such as "new product B," "past," and "marketing strategy." Based on this, it searches the database and extracts relevant past project histories and reports. The server organizes the results and generates answers about specific past campaign examples and success stories, which the terminal then presents to the user. 【0658】 This invention enables the rapid and efficient acquisition and utilization of information within a company, allowing new employees and transferees to easily access internal knowledge. This prevents knowledge from becoming tied to specific individuals and improves work productivity. 【0659】 The following describes the processing flow. 【0660】 Step 1: 【0661】 The server periodically collects data from multiple sources within the company's information repository, including documents, project histories, FAQs, and operational manuals. This includes automated retrieval from file servers and databases. 【0662】 Step 2: 【0663】 The server performs text preprocessing on the collected data. This preprocessing cleans the data, removes noise, and standardizes character encoding. 【0664】 Step 3: 【0665】 The server applies natural language processing techniques to the pre-processed data, indexing the information through tokenization and morphological analysis. At this stage, the data is organized to facilitate searching. 【0666】 Step 4: 【0667】 When users need information, they input questions into their devices using natural language. 【0668】 Step 5: 【0669】 The terminal sends the question received from the user to the server. The question is provided in natural language format. 【0670】 Step 6: 【0671】 After receiving a user's question, the server analyzes it using natural language processing. Specifically, it extracts contextually relevant keywords to understand the intent of the question. 【0672】 Step 7: 【0673】 The server searches an already indexed database based on the extracted keywords and the context of the question. This is a process to quickly identify relevant information. 【0674】 Step 8: 【0675】 The server organizes the search results and selects the most relevant information. It applies a re-ranking algorithm to prioritize and output the most important information. 【0676】 Step 9: 【0677】 The server generates specific answers to user questions based on the selected information, including links to relevant documents. 【0678】 Step 10: 【0679】 The device presents the generated response to the user. Based on the information provided, the user can access the necessary details. 【0680】 This step allows users to quickly obtain the necessary information and carry out their work efficiently. 【0681】 (Example 1) 【0682】 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". 【0683】 Within companies, a vast amount of information is generated daily from diverse sources, making it crucial to quickly acquire necessary information and utilize it in business operations. However, systems that efficiently manage this information and provide immediate and appropriate answers when users naturally ask questions are not yet sufficiently developed. This challenge is particularly urgent in order to prevent information from becoming siloed and improve business productivity by making it easier for new employees and transferees to access internal knowledge. 【0684】 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. 【0685】 In this invention, the server includes means for acquiring information and formatting text data, means for indexing the information using natural language processing technology, and means for generating responses in natural language using a generative AI model. This makes it possible to quickly and accurately acquire the necessary information and present responses when a user gives instructions in natural language. 【0686】 "Methods for acquiring information and formatting text data" refers to the process of collecting information from various data sources dispersed within a company, removing unnecessary elements, and converting it into a format that is easy to analyze. 【0687】 "A method for indexing information using natural language processing technology" refers to a method of efficiently structuring collected and formatted text information into searchable data by analyzing it with a natural language processing algorithm. 【0688】 "A method for generating answers in natural language using a generative AI model" refers to the process of automatically generating grammatically correct and human-readable text using generative AI technology when providing answers to questions. 【0689】 "Means for identifying the business classification related to the instructions based on the analysis of the received instructions" refers to a technology that analyzes natural language instructions from users and identifies the business classification corresponding to the content of the instructions. 【0690】 "A means of re-evaluating search results and selecting and presenting the most relevant information" refers to a method of re-evaluating multiple search results obtained within the system from the perspective of importance and relevance, and prioritizing the provision of the most suitable information to the user. 【0691】 For the present invention to be implemented, information flow and data analysis between servers, terminals, and users are important. 【0692】 The server periodically collects documents and business data distributed within the company using internet technology. This process utilizes dedicated crawling software and performs text cleaning as needed using natural language processing libraries such as OpenNLP and NLTK. The collected data is indexed using Lucene or Elasticsearch and stored as data with enhanced searchability. 【0693】 When a user requests specific information, they input their question in natural language through the user interface on their device. The device receives this question and sends it to the server. For example, a user might input, "Please tell me about past marketing cases for the new product B." 【0694】 When the server analyzes the received questions, it uses libraries such as spaCy and Transformers to extract highly relevant keywords from the questions. Based on these keywords, it searches for relevant information in past project history and document databases. 【0695】 The search results are re-evaluated on the server, and the most relevant information is selected. Then, a generative AI model, such as GPT-3, is used to automatically generate answers in newly generated, natural-sounding text. These answers include relevant documents and links. 【0696】 The generated answers are presented to the user via the terminal. This system allows users to quickly obtain the information necessary for their work, leading to improved work productivity and preventing knowledge from becoming tied to specific individuals. 【0697】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0698】 Step 1: 【0699】 The server uses crawling technology to collect diverse documents and project histories within the company. Simultaneously, metadata for each document is collected and stored in a database. The input information comes from different formats and sources, but the output is text data in a unified format. 【0700】 Step 2: 【0701】 The server performs text cleaning on the collected raw data. This process uses the NLTK library to remove unnecessary symbols and line breaks and format the text. The output is cleaned data suitable for analysis. 【0702】 Step 3: 【0703】 The server applies natural language processing to the cleaned data and performs indexing. It uses Lucene to tokenize the data, assigns weights to each token, and generates an index. The input is cleaned text data, and the output is indexed, searchable data. 【0704】 Step 4: 【0705】 The user enters a question about specific information through the terminal's interface. This question is written in natural language and sent to the server as user instruction. The input is a question in natural language. 【0706】 Step 5: 【0707】 The server receives questions submitted by users and analyzes them using spaCy. It extracts relevant keywords from the questions and uses them to search for related information in an index. The input is a natural language question, and the output is a list of documents that match the relevant keywords. 【0708】 Step 6: 【0709】 The server re-evaluates the searched information and selects the most relevant content. Using a generative AI model, it automatically generates a response based on the selected information. This process utilizes generative models such as GPT-3. The input is a list of documents, and the output is a natural-sounding response. 【0710】 Step 7: 【0711】 The terminal receives the generated response from the server and presents it to the user. The user uses this information for their work and accesses relevant links and documents as needed. The input is the generated response, and the output is the response presentation screen that the user can view. 【0712】 (Application Example 1) 【0713】 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". 【0714】 In modern urban life, it is difficult for citizens to efficiently obtain information about urban public infrastructure. Furthermore, there is a lack of effective information provision systems for quickly grasping complex procedures and event information. This makes daily life inconvenient for citizens and hinders the smooth use of public services. 【0715】 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. 【0716】 In this invention, the server includes means for indexing information using natural language processing technology, means for analyzing user inquiries and extracting relevant keywords, means for searching for relevant information from a data storage device, and means for analyzing data and selecting and presenting optimal information to provide information about urban infrastructure. This enables citizens to efficiently obtain information about urban public infrastructure, thereby improving their daily convenience. 【0717】 An "information management unit" is a key component of a system equipped with functions for efficiently collecting, organizing, storing, and retrieving information. 【0718】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0719】 "Indexing" is the process of organizing and systematizing information based on specific criteria in order to improve the efficiency of information retrieval. 【0720】 "Users" refer to individuals or organizations that actually use the system or service. 【0721】 A "question" is a question or request that a user enters into the system to obtain information. 【0722】 "Analysis" is the process of breaking down data into its constituent elements, examining them, and understanding their meaning and relationships. 【0723】 "Related keywords" are important words or phrases extracted to identify the meaning and intent of a question. 【0724】 A "data storage device" is a device or system for storing digital data and retrieving it as needed. 【0725】 "Urban infrastructure" is a general term for the foundations that support urban life, such as transportation, communication, water supply, and educational facilities. 【0726】 "Components of information provision" refer to the functions and processes for organizing and communicating the information necessary for users. 【0727】 "Public infrastructure information" refers to information regarding the operation status and usage guidelines of public services and facilities in cities and regions. 【0728】 The system for realizing this invention includes an information management unit using natural language processing technology and operates on a device such as a smartphone as an application that provides citizens with information on the city's public infrastructure. The server first receives a natural language query from the user, analyzes the content using natural language processing technology, and extracts relevant keywords. For example, natural language processing libraries such as spaCy and NLTK are used for this purpose. Based on the extracted keywords, the server searches for relevant information from a cloud database (e.g., Firebase or AWS DynamoDB), which is a data storage device. 【0729】 The searched information is organized in a re-display order to provide more accurate information. The server uses a recommendation algorithm specialized for urban infrastructure information to select the most relevant information and provide it to the user's terminal. As a result, users can instantly access the necessary administrative information and infrastructure status. 【0730】 For example, if a user enters a question such as "Tell me about local events available this weekend," the generative AI model processes this question and retrieves relevant information using the prompt "Tell me about local events this weekend." This allows the user to quickly find details such as the location and date of the relevant event. 【0731】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0732】 Step 1: 【0733】 The terminal receives a natural language question from the user. This input data is sent directly to the server. This question contains a specific information need. 【0734】 Step 2: 【0735】 The server analyzes the received question using natural language processing techniques. This process extracts keywords using natural language processing libraries such as spaCy and NLTK to understand the intent of the question. The input is the text of the received question, and the output is a list of related keywords. 【0736】 Step 3: 【0737】 The server uses the extracted keywords to search for relevant information in cloud databases (e.g., Firebase, AWS DynamoDB). The search input is a list of keywords, and relevant results are output from the database. 【0738】 Step 4: 【0739】 The server organizes the searched information in a re-display order based on relevance. A recommendation algorithm is used for this organization. Data that matches more accurately is prioritized, generating an optimized list of information. 【0740】 Step 5: 【0741】 The server selects the most appropriate content from the organized information, constructs it in natural language, and provides it to the terminal. Here, a generative AI model is used to generate prompt sentences and present the information. At this stage, the input is organized information, and the output is the constructed response text. 【0742】 Step 6: 【0743】 The terminal displays the response text received from the server to the user. Based on this, the user can satisfy their information needs and obtain information about the city's public infrastructure. 【0744】 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. 【0745】 This invention provides a system for corporate information management that combines natural language processing technology and an emotion engine to deliver more user-friendly information. The embodiments of this invention are described below in detail. 【0746】 This information management system is server-driven, collecting information from various data sources within the company and indexing it using natural language processing technology. This indexing makes the information easily searchable. Users, during their work, can input questions in natural language when they need specific information, and query the system through their terminals. 【0747】 When the server receives a question from a user, it first analyzes the question using natural language processing techniques. This analysis extracts keywords and relevant contextual information, which are then used for searching. In this step, the sentiment engine recognizes the emotions from the user's question and extracts emotional data such as potential stress and urgency. 【0748】 Based on this data, the server searches the database for relevant information. It can adjust the order and amount of information presented, particularly by referencing sentiment data. For example, if a user is dissatisfied, the server can adjust the information to include more detailed and helpful explanations. 【0749】 The server then integrates the search results and automatically generates an answer to the question. This answer may include links to relevant documents and supplementary content. The generated answer is presented to the user via the terminal in an easy-to-understand format. 【0750】 As a concrete example, suppose a user asks a question such as, "I need the meeting notes from yesterday's Project X meeting, and I'm in a hurry." The server receives this question and uses natural language processing to extract keywords such as "Project X," "meeting notes," and "urgent," while also using an emotion engine to recognize the user's level of urgency. The server uses this information to quickly search for the relevant meeting notes and construct an automatically generated response that prioritizes displaying the necessary parts. The terminal then promptly presents the response to the user, taking into consideration the urgency of the matter. 【0751】 This system allows users to receive information optimized according to their emotional state at the time, significantly improving the efficiency of information acquisition in their work. Furthermore, by enhancing their ability to respond, especially in situations involving emotions, it becomes possible to create a more comfortable information environment. 【0752】 The following describes the processing flow. 【0753】 Step 1: 【0754】 The server periodically collects information from data sources within the company. This data includes documents, emails, project history, and FAQs. The collected data is indexed using natural language processing techniques to enable rapid searching. 【0755】 Step 2: 【0756】 Users input questions in natural language into their devices when they need information. The more specific the question, the more accurate the answer will be. 【0757】 Step 3: 【0758】 The terminal receives the question entered by the user and sends it to the server. The question is passed to the server as unstructured text data. 【0759】 Step 4: 【0760】 The server analyzes the received question using natural language processing techniques. This analysis extracts keywords and context from the question, forming the basis for information retrieval. 【0761】 Step 5: 【0762】 The server uses an emotion engine to recognize the emotions contained in the user's questions. This allows it to detect emotional data such as stress, urgency, and excitement, and to consider these factors when presenting information. 【0763】 Step 6: 【0764】 The server searches an indexed database using the extracted keywords and sentiment data. This process applies algorithms to quickly identify relevant materials. 【0765】 Step 7: 【0766】 The server aggregates search results and re-ranks them, taking into account the user's emotional state. Here, information is prioritized according to emotional data. 【0767】 Step 8: 【0768】 The server automatically generates appropriate answers to questions based on the selected information. These generated answers also include links to relevant additional information and documentation. 【0769】 Step 9: 【0770】 The device presents the generated answers to the user. This presentation is designed to be clear and efficient so that the user can immediately utilize the information. 【0771】 Specifically, through this process, users can receive emotionally sensitive responses, reducing stress during information acquisition. Furthermore, information prioritization is optimized according to the user's situation, and necessary information is provided quickly. 【0772】 (Example 2) 【0773】 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". 【0774】 There is a need to effectively manage information resources scattered throughout the company and provide an environment where users can quickly and accurately obtain information. Furthermore, it is necessary to improve work efficiency by presenting information in a way that is tailored to the user's emotions and the urgency of their questions. 【0775】 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. 【0776】 In this invention, the server includes means for organizing information using natural language processing technology, means for analyzing user questions and extracting relevant features, and means for retrieving relevant information from data storage using emotional information and generating a tailored response. This enables the provision of optimal information in response to user questions, and in particular, enables the accurate presentation of information according to emotions and urgency. 【0777】 An "information processing device" is a device that effectively manages information within a company and quickly provides users with the information they need. 【0778】 "Natural language processing technology" is a technology that enables computers to understand human language, and involves analyzing text data to understand its meaning and structure. 【0779】 "Feature extraction" means extracting useful information and data points for analysis from the input data. 【0780】 "Emotional information" refers to data used to determine emotions and urgency from user input, and is taken into consideration when presenting information. 【0781】 "Data storage area" refers to the location or system where information is stored, and is used during retrieval. 【0782】 "Searching for related information" means finding useful data or documents from data storage in response to a query. 【0783】 A "tailored response" is a response customized according to the user's emotions and the content of their question, with the aim of providing the most relevant information. 【0784】 This invention is a system for streamlining information processing within a company and providing optimized information in response to user inquiries. This system primarily consists of servers, terminals, and users, and aims to effectively manage and provide information. 【0785】 First, the server collects information from various data sources within the company. These data sources include document management systems, email, and databases. The server then analyzes and indexes this information using natural language processing technologies (e.g., spaCy or NLTK). This organizes the information into an efficiently searchable format. 【0786】 Next, the user inputs a question in natural language and sends it to the server via their device. This input is done using a web browser or desktop application. The server analyzes the user's question and extracts key keywords and relevant contextual information. Furthermore, it uses an emotion engine to identify emotional information from the user's question, extracting emotional data such as urgency and dissatisfaction. 【0787】 Based on the extracted information, the server searches the database for relevant information. During this process, the search results are adjusted based on sentiment data. For example, if high urgency is recognized, the search is optimized to prioritize the presentation of necessary information. Then, using the search results, the server automatically generates answers to the user's questions using a generative AI model (e.g., a GPT model). These answers may include links to relevant additional information and documents. 【0788】 Finally, the device provides the generated response to the user, presenting it in a format that takes the user's situation into particular. This allows the user to quickly and effectively obtain the necessary information. 【0789】 As a concrete example, consider a scenario where a user asks a question such as, "I urgently need the meeting notes from yesterday's Project X." In this case, the server analyzes the question and extracts keywords such as "Project X," "meeting notes," and "urgent," along with an indication of urgency. Based on this information, it quickly searches for relevant meeting notes and displays them preferentially through the user's device. 【0790】 An example of a prompt message is, "Please provide the latest information on Project X. I'm in a hurry." This example demonstrates how to provide optimal information tailored to the user's needs. 【0791】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0792】 Step 1: 【0793】 The server collects information from data sources within the company (e.g., document management systems, email, databases). At this stage, it receives raw data obtained from the data sources as input. Subsequently, the collected data is analyzed using natural language processing techniques (e.g., spaCy, NLTK), organized, and indexed. Specifically, it extracts topics and keywords from the text and generates an index to facilitate information retrieval. As a result, the indexed data is output. 【0794】 Step 2: 【0795】 Users input necessary information for their work and send natural language questions to the server using their terminal. At this time, the user's specific questions and information requests (e.g., "Please provide the meeting notes for Project X.") are treated as input. 【0796】 Step 3: 【0797】 The server analyzes the received question. This analysis uses natural language processing techniques to extract key keywords and relevant contextual information from the input text. Specifically, this step involves tokenizing the text and performing morphological and dependency analysis. Then, an emotion engine is used to identify the user's emotional information (e.g., urgency, dissatisfaction) from the question. As a result of the analysis, keywords and emotional information are output. 【0798】 Step 4: 【0799】 Based on the analysis results from step 3, the server searches for relevant information from the indexed database. Specifically, it performs keyword matching and relevance scoring, and prioritizes information while considering sentiment. This search operation outputs highly relevant and well-organized information. 【0800】 Step 5: 【0801】 The server uses the search results to automatically generate answers to the user's questions using a generative AI model (e.g., a GPT-based model). This step integrates the search results with generative AI technology to generate contextually natural-sounding text. The final answer may include links to relevant documents and supplementary information, which are then output. 【0802】 Step 6: 【0803】 The device provides the generated response to the user. Specifically, it displays the response on the user interface, visually highlighting information that is particularly urgent or requires further detail. Here, the necessary information is presented clearly so that it is immediately available to the user. As a result of this step, the user can quickly obtain the information that meets their needs. 【0804】 (Application Example 2) 【0805】 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". 【0806】 In today's world, the importance of providing information that takes users' emotions into consideration is increasing, but conventional information processing systems have the challenge of not being able to provide optimal information that reflects the emotional state of users. Furthermore, when users try to acquire information while feeling anxious or stressed, conventional information presentation may not alleviate that anxiety, potentially resulting in decreased user satisfaction. Therefore, there is a need for means to quickly and effectively acquire the information that users need. 【0807】 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. 【0808】 In this invention, the server includes means for organizing information using natural language understanding technology, means for receiving and analyzing natural language inquiries from users to extract relevant linguistic elements, and means for analyzing the user's emotional state and optimizing the content and order of responses based on the results. This makes it possible to provide information optimized according to the user's emotional state. 【0809】 An "information processing device" is a system consisting of hardware and software for efficiently managing and processing information. 【0810】 "Natural language understanding technology" is a technology that mechanically analyzes human language and extracts structured information. 【0811】 A "user" is a person or organization that attempts to obtain information using the system. 【0812】 A "natural language query" is a question or request made to a system using the language that humans use in everyday life. 【0813】 "Analysis" is the act of breaking down and examining given data or information based on a specific purpose, and deriving its characteristics and relationships. 【0814】 "Linguistic elements" are elements such as words and phrases that have meaning and structure within natural language. 【0815】 "Exploration" is the process of identifying and retrieving information according to a specific purpose. 【0816】 An "information set" is a collection of data and information stored in various formats. 【0817】 A "response" is the answer or information that a system returns in response to an inquiry or request. 【0818】 A "user terminal" is a device or interface used by a user to receive output from an information processing device and perform operations on it. 【0819】 "Emotional state" refers to the psychological state that a user experiences in a particular situation, and includes feelings such as anxiety, joy, and urgency. 【0820】 "Optimization" refers to adjusting processes and results to their most efficient and effective state according to specific objectives and conditions. 【0821】 This invention relates to a system centered on an information processing device that enables the provision of information while considering the user's emotional state. The system includes a process of analyzing natural language inquiries from users and extracting relevant linguistic elements, utilizing sophisticated natural language understanding technology. 【0822】 The core server of the system performs natural language processing using spaCy and analyzes the user's emotional state using TextBlob. Based on the key keywords and emotional data obtained in this step, it efficiently searches for relevant information from data sets such as Firebase Realtime Database. Based on the searched information, the server uses Adobe's generative AI model to generate the most relevant response for the user. 【0823】 The user terminal is responsible for presenting the generated responses in both visual and textual formats. The interface on this terminal is optimized for rapid and appropriate information transmission, enabling the presentation of information that reflects the user's emotional state. 【0824】 For example, if a user makes a natural language inquiry such as, "I would like to know specific security advice for when I leave my home," the server will analyze this inquiry, extract relevant security information, and ultimately display advice on the user's device that will allow them to act with peace of mind. 【0825】 An example of a prompt used in a generative AI model would be, "Please provide steps and advice for optimizing home security while you are away." 【0826】 This system allows users to receive timely and accurate information optimized based on their emotions at the time. 【0827】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0828】 Step 1: 【0829】 The user enters a query in natural language via their device. The input sentence might be something like, "I'd like some specific security advice for when I'm away from home." This query is sent to the server as text data. 【0830】 Step 2: 【0831】 The server analyzes the received text data using the spaCy library. During this analysis, the server extracts key keywords and contextual information from the text. For example, keywords such as "home," "away," "security," and "advice" are obtained. This information is then used as input for the next process. 【0832】 Step 3: 【0833】 The server performs sentiment analysis on the parsed text data using TextBlob. In this step, the user's emotional state, such as the degree of anxiety or reassurance, is output as numerical data. This makes it possible to present information tailored to the user's psychological state. 【0834】 Step 4: 【0835】 The server uses the extracted keywords and sentiment data to search for relevant information in the Firebase Realtime Database. For example, it might search for information on crime prevention measures or the latest security guidelines. This information is then compiled as the server's output. 【0836】 Step 5: 【0837】 Based on the information explored, the server uses a generative AI model to generate the most suitable response for the user. The response generated at this stage is specific advice, such as "Placing security cameras would be effective." This response is then obtained as the next output. 【0838】 Step 6: 【0839】 The generated response is sent to the terminal and presented to the user. The terminal displays the response in a visually easy-to-understand format. The user can view this and obtain the information they need. 【0840】 This series of processes allows users to efficiently obtain the most relevant information based on their emotional state and other relevant data. 【0841】 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. 【0842】 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. 【0843】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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." 【0850】 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. 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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 as being incorporated by reference. 【0862】 The following is further disclosed regarding the embodiments described above. 【0863】 (Claim 1) 【0864】 An information management device provides a means for indexing information using natural language processing technology, 【0865】 A means of receiving natural language questions from users, analyzing them, and extracting relevant keywords, 【0866】 A means of searching for relevant information from a database using extracted keywords, 【0867】 A means for automatically generating answers to user questions based on search results, 【0868】 A means of presenting the generated response to the user's terminal, 【0869】 A system that includes this. 【0870】 (Claim 2) 【0871】 The system according to claim 1, further comprising means for identifying a business category related to a question based on an analysis of the received question. 【0872】 (Claim 3) 【0873】 The system according to claim 1, further comprising means for re-ranking search results and selecting and presenting the most relevant information. 【0874】 "Example 1" 【0875】 (Claim 1) 【0876】 A means of acquiring information and formatting text data, 【0877】 A means of indexing information using natural language processing technology, 【0878】 A means of receiving instructions in natural language from the user, analyzing them, and extracting related words, 【0879】 A means of searching for related information from a data set using extracted words, 【0880】 A means for automatically generating responses to user instructions based on search results, 【0881】 A method for generating answers in natural language using a generative AI model, 【0882】 A means for displaying the generated answer on a display device, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, further comprising means for identifying a business classification related to an instruction based on an analysis of the received instruction. 【0886】 (Claim 3) 【0887】 The system according to claim 1, further comprising means for re-evaluating search results and selecting and presenting the most relevant information. 【0888】 "Application Example 1" 【0889】 (Claim 1) 【0890】 In the information management unit, there is a component that indexes information using natural language processing technology, 【0891】 A component that receives natural language questions from users, analyzes them, and extracts relevant keywords, 【0892】 A component that searches for relevant information from a data storage device using extracted keywords, 【0893】 Components that automatically generate responses to user inquiries based on search results, 【0894】 Components that present the generated response to the user's terminal, 【0895】 Information provision components that enable users to input questions regarding information about urban infrastructure, 【0896】 A component that analyzes data to provide information on public infrastructure within a city, selects and presents the most relevant information, 【0897】 A system that includes this. 【0898】 (Claim 2) 【0899】 The system according to claim 1, further comprising a component that identifies a field category related to a question based on an analysis of the question received. 【0900】 (Claim 3) 【0901】 The system according to claim 1, further comprising a component that sorts search results in the order they are displayed and selects and presents the most relevant information. 【0902】 "Example 2 of combining an emotion engine" 【0903】 (Claim 1) 【0904】 In an information processing device, a means for organizing information using natural language processing technology, 【0905】 A means of receiving natural language questions from users, analyzing them, and extracting relevant features, 【0906】 A means for retrieving relevant information from data storage using extracted features and emotional information, 【0907】 A means of automatically generating answers to user questions based on search results, and adjusting their priority and content, 【0908】 A means of presenting the generated response to the user's device, 【0909】 A system that includes this. 【0910】 (Claim 2) 【0911】 The system according to claim 1, further comprising means for identifying sentiment information from a question and adjusting the presentation of information in the search results. 【0912】 (Claim 3) 【0913】 The system according to claim 1, further comprising means for providing links to documents and supplementary information related to the generated response, thereby facilitating information retrieval. 【0914】 "Application example 2 when combining with an emotional engine" 【0915】 (Claim 1) 【0916】 In an information processing device, a means for organizing information using natural language understanding technology, 【0917】 A means of receiving and analyzing natural language queries from users to extract relevant linguistic elements, 【0918】 A means of searching for related information from an information set using extracted linguistic elements, 【0919】 A means for automatically generating responses to user inquiries based on search results, 【0920】 A means for presenting the generated response to the user's terminal, 【0921】 A means for analyzing the user's emotional state and optimizing the content and order of responses based on the results, 【0922】 A system that includes this. 【0923】 (Claim 2) 【0924】 The system according to claim 1, further comprising means for identifying a business category related to an inquiry based on an analysis of the received inquiry. 【0925】 (Claim 3) 【0926】 The system according to claim 1, further comprising means for re-ranking the search results and selecting and presenting the most relevant information. [Explanation of symbols] 【0927】 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

[Claim 1] An information management device provides a means for indexing information using natural language processing technology, A means of receiving natural language questions from users, analyzing them, and extracting relevant keywords, A means of searching for relevant information from a database using extracted keywords, A means for automatically generating answers to user questions based on search results, A means of presenting the generated response to the user's terminal, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for identifying a business category related to a question based on an analysis of the received question. [Claim 3] The system according to claim 1, further comprising means for re-ranking search results and selecting and presenting the most relevant information.

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