system

Abstract

We provide the system. [Solution] A natural language processing means for processing information received from a user terminal, A search means for searching an information database based on keywords extracted by the natural language processing means, A generation means for generating multiple solutions based on search results, A reference attachment means for attaching information sources related to the generated solution, A display means for displaying the generated solutions on the user 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 in 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】 In order to resolve ambiguities regarding the meanings of technical terms arising during business operations, the scope of responsibilities between departments, past implementation cases, etc., information collection relying on experienced personnel and manual data searches require a great deal of time and effort, hindering productivity improvement. Additionally, if information search and provision are not rapid, there is a problem that it may cause delays in decision-making and incorrect judgments. 【Means for Solving the Problems】 【0005】 This system accepts questions from user terminals, analyzes the information using natural language processing, and searches the company's internal information database based on the extracted keywords. This automatically generates multiple solutions from the search results, adding corresponding information sources as references. The generated solutions are immediately presented to the user terminal, enabling rapid and effective problem solving by providing numerous options. Simultaneously, different search algorithms are utilized to enhance the diversity of solution generation and ensure comprehensive information coverage. 【0006】 A "user terminal" is a computer device used by a user to input or receive information. 【0007】 "Natural language processing means" refers to a computer program or algorithm for analyzing text information sent by a user and extracting important keywords and meanings. 【0008】 An "information database" is a collection of electronic data in which information such as past case studies, manuals, and technical documents within a company are systematically stored. 【0009】 A "search method" is an algorithm or program that searches an information database based on keywords extracted through natural language processing and identifies relevant information. 【0010】 A "generation method" is an algorithm or program that automatically constructs solutions to a user's questions based on information obtained through exploration. 【0011】 "Reference addition means" refers to a device or function for adding links or identifiers to information sources corresponding to the generated solution. 【0012】 "Display means" refers to a screen or software interface on a terminal used to present a solution to the user in an easily viewable format. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 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. 【0017】 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. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 This invention is a system for rapidly providing solutions from diverse information sources within a company, and aims to improve efficiency throughout the stages of information collection, processing, and presentation. The program is implemented using the following methods. 【0035】 First, the user enters their question in text format using their device. The device sends the input data to the server, which then analyzes this data using natural language processing. This allows important keywords and intent to be extracted from the text. For example, if a question such as "How to deal with a defect in a new product" is entered, keywords such as "new product," "defect," and "solution" are recognized as important. 【0036】 The server searches the information database based on the extracted keywords. The search method allows for the rapid identification of relevant documents and past cases. In this step, past cases where similar problems were solved and standard troubleshooting procedures described in the product manual are referenced. 【0037】 Next, the server generates multiple solutions based on the search results. The generation method constructs candidates based on different perspectives and methods, providing the user with choices. For example, it can offer both immediate solutions and long-term solutions. 【0038】 Furthermore, the server adds references such as links and document numbers to the sources of information corresponding to the generated solutions. This allows users to verify the reliability of the presented solutions and refer to further details as needed. 【0039】 Finally, the server returns the generated solution to the user's terminal, which displays it in an easy-to-understand format. The user can then use this information to select the appropriate action to take. This consistent process enables faster and more reliable work execution than ever before. 【0040】 This system is designed to support user decision-making by utilizing diverse information. Specifically, it can be applied to various tasks, including not only troubleshooting but also planning new projects and considering customer response strategies. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user enters their question into the terminal and presses the send button. The terminal then sends the entered question to the server. 【0044】 Step 2: 【0045】 The server passes the user's question to a natural language processing system for text analysis. This allows the server to extract important keywords and context. 【0046】 Step 3: 【0047】 The server searches the information database using the extracted keywords. It utilizes search methods to explore relevant documents and past cases, and identifies the corresponding data. 【0048】 Step 4: 【0049】 The server generates multiple solutions based on the information obtained from the search results. Here, it generates options by considering different perspectives and methods, providing a variety of solutions. 【0050】 Step 5: 【0051】 The server adds references to the relevant sources of information for each solution, including links and document numbers, so that users can later access more details. 【0052】 Step 6: 【0053】 The server sends the generated solutions and references to the user's terminal. The terminal displays them to the user in an easy-to-read format. The user can review the provided solutions and make decisions based on the useful information. 【0054】 (Example 1) 【0055】 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." 【0056】 In today's world, where the sheer volume and diversity of information is increasing, companies need systems to support information gathering, analysis, and decision-making in order to respond quickly and effectively to the problems they face. Current methods make this process cumbersome and time-consuming, which can lead to delays in decision-making and even incorrect judgments. 【0057】 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. 【0058】 In this invention, the server includes a natural language processing means for analyzing information received from a user terminal and extracting important terms, a search means for searching an information set based on the important terms, and a generation means for generating a number of solutions based on the search results. This makes it possible to answer user questions quickly and accurately. 【0059】 A "user terminal" is an electronic device used by a user to input information, and typically includes computers and smartphones. 【0060】 "Natural language processing means" refers to techniques or tools for analyzing input text information and extracting important words or phrases. 【0061】 "Search means" refers to a method or apparatus for searching an information set using extracted terms and obtaining related data. 【0062】 "Generative means" refers to a technology or process for creating and presenting multiple solutions based on search results. 【0063】 "Additional means" refers to a function or mechanism for adding relevant information to the generated solution. 【0064】 "Presentation means" refers to the means for displaying the solution generated on the user terminal, and generally, a display or graphical user interface is used. 【0065】 An "information collection" is a collection of information that is searched, and usually refers to a database system. 【0066】 A description of the embodiment for carrying out the invention will be provided. 【0067】 This invention is a system that provides solutions quickly from diverse information sources within a company. The system is mainly composed of user terminals and a server working together to efficiently collect, analyze, and present various types of information. 【0068】 The user first enters their question in text format using their device. This device is typically a personal computer or smartphone, and the user enters text using a keyboard or touchscreen. Once the user has finished entering the question, the device sends the information to the server. 【0069】 The server uses natural language processing (NLP) techniques to analyze the received information. Specifically, libraries such as Python's NLTK and spaCy are used as natural language processing tools. This allows for the extraction of important words and phrases from text data, enabling an accurate understanding of the user's intent. 【0070】 Next, the server uses the extracted terms to search the information set. SQL databases or Elasticsearch® are commonly used for this search. This allows for the efficient retrieval and reference of relevant documents and past case studies. 【0071】 Subsequently, the server generates numerous solutions using a generative AI model based on the search results. This process considers different perspectives and methods, resulting in a diverse range of solutions. In this process, the AI ​​model utilizes the input data to perform inference. 【0072】 Furthermore, the generated solutions are accompanied by references to relevant information. Specifically, links to information sources and document numbers are added to the solutions, making it easier for users to verify their reliability. 【0073】 Finally, the server sends the generated solution to the user's terminal. The terminal displays the received solution in an easy-to-understand format. This allows the user to make quick decisions based on the presented information. 【0074】 For example, when a user enters a prompt such as, "Please tell me about successful examples of new projects," the system will present relevant success stories that can be used as reference for project planning. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The user uses their device to input questions or problems they want to resolve as text. For example, they might enter a prompt such as "How to fix a bug in the new product." The entered text is then sent by the device to the server via a secure communication method (e.g., HTTPS). 【0078】 Step 2: 【0079】 The server analyzes the received text data using natural language processing (NLTK) tools. This analysis utilizes libraries such as NLTK and spaCy. The text is tokenized, parts of speech are tagged, and important words are extracted from the results. It takes raw text as input and generates extracted keywords as output. 【0080】 Step 3: 【0081】 The server uses the extracted keywords to search the information set. The search process utilizes systems such as SQL databases or Elasticsearch to efficiently identify relevant documents and past cases. The input is keywords, and the output is a set of related data entries. 【0082】 Step 4: 【0083】 The server generates solutions using a generative AI model based on the information obtained through the search. The AI ​​model takes the search results as input and outputs multiple solutions that consider different perspectives and methods. Specifically, these include methods for quick responses as well as solutions from a long-term perspective. 【0084】 Step 5: 【0085】 The server adds references to relevant information for each generated solution. By adding links to information sources and document IDs to the solution, users can later access more detailed information. The input is the solution proposal, and the output is the solution with the added references. 【0086】 Step 6: 【0087】 The server sends the final solution to the user's terminal. The terminal then displays the received solution, using a graphical user interface or similar method to present it clearly. This allows the user to make quick decisions based on the presented information. The output to the terminal is formatted solution information. 【0088】 (Application Example 1) 【0089】 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." 【0090】 With the proliferation of visual information devices, users have a growing need to quickly obtain personalized information from diverse content. However, it is difficult for users to select appropriate content from a large amount of information through visual means, and there is a need for a means to obtain personalized recommendations efficiently and effectively. Conventional technologies have the challenge of not being able to provide recommendations that fully utilize the user's preferences and past viewing history. 【0091】 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. 【0092】 In this invention, the server includes information processing means for processing information received from a user terminal, search means for searching an information aggregate based on concepts extracted by the information processing means, and generation means for generating a variety of options based on the search results. This enables the user to quickly obtain personalized recommendation information and select appropriate content through a visual information device. 【0093】 "Information processing means" refers to a device or process that analyzes information received from a user terminal and extracts important concepts. 【0094】 An "information aggregate" refers to a data storage or database that aggregates and holds related information. 【0095】 A "search method" is a device or process for quickly identifying relevant information within an information aggregate. 【0096】 "Generation means" refers to a device or process for constructing and providing diverse options based on search results. 【0097】 "Recording addition means" refers to a device or process for adding information sources related to the generated choices. 【0098】 "Display means" refers to a device or process that visually presents the generated options to the user. 【0099】 "Adaptive presentation means" refers to a function or process that adaptively displays personalized recommendation information to the user based on the content. 【0100】 This system consists of a user terminal, a server, and an information aggregate. Since the user processes information visually through a head-mounted display, the user first inputs questions related to the visual content into the terminal using a catalyst. 【0101】 The server analyzes user input using information processing tools and extracts important concepts. It uses spaCy, a natural language processing library, to identify meaningful keywords from the input text. In this process, the server manipulates the database using MySQL® to efficiently search for relevant information within the data collection. 【0102】 Based on search results, the server utilizes Scikit-learn to generate personalized recommendations, creating a diverse range of options. This enables adaptive information presentation tailored to the user's preferences and past viewing history. The generated information is further enhanced with additional information through a recording mechanism to increase its reliability. The server then visualizes the generated options using an adaptive presentation mechanism, displaying appropriate information in real time to guide the user. 【0103】 For example, if a user enters "I want to experience the latest sci-fi movie," the system will identify relevant VR content and provide descriptions and recommendations based on its characteristics. An example of a prompt is shown below: "(Request) Action-packed VR content related to sports." 【0104】 In this way, users can enjoy real-time access to information tailored to their needs. 【0105】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0106】 Step 1: 【0107】 The user wears a head-mounted display and inputs questions related to visual content into a terminal. The user's questions are sent to the terminal as input data. 【0108】 Step 2: 【0109】 The terminal sends the entered question to the server. The server uses a natural language processing library (spaCy) to extract important keywords from the question. For example, in the input "I want to experience the latest sci-fi movie," keywords such as "latest," "sci-fi movie," and "experience" are extracted. This is the input to the server, and the extracted keywords become the output for the next processing step. 【0110】 Step 3: 【0111】 The server searches the information repository based on the extracted keywords. It uses MySQL to quickly query the database and identify relevant VR content information and data points. Keywords are input, and related information is output. 【0112】 Step 4: 【0113】 The server uses Scikit-learn to execute a content recommendation algorithm based on the data obtained from the search, taking into account the user's preferences and past viewing history. This algorithm generates personalized recommendation information. The search results are the input, and the recommended content is the output. 【0114】 Step 5: 【0115】 To ensure the reliability of the generated recommendations, the server uses recording mechanisms to add information such as the history of the information source. The recommendation information is the input, and the recorded recommendation information is the output. 【0116】 Step 6: 【0117】 The server uses adaptive presentation means to send recommended information to the user's terminal. The head-mounted display visually presents this information to the user, allowing the user to select appropriate content based on that information. The user visually receives the recommended content via the terminal. The adaptively presented information is the input, and the presentation is the output. 【0118】 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. 【0119】 This invention combines an emotion engine with a system that processes user questions and provides solutions, thereby achieving flexible responses based on the user's emotional state. The following specifically describes an embodiment of the system, including the functions of each component. 【0120】 The user uses the terminal to input a question seeking a solution. In the process of sending this to the server, the terminal invokes an emotion engine, which analyzes the linguistic expressions and context of the user's input to recognize their emotional state. For example, if the user inputs "I'm really troubled by this problem," the system will determine that the user is experiencing emotions such as "confusion" or "anxiety." 【0121】 The server uses natural language processing to analyze important keywords from the received information and searches the information database. In addition to normal information retrieval, the search method optimizes the results by considering emotional information provided by the emotion engine. This allows for the generation of solutions that are appropriate to the user's emotions, even for the same question. 【0122】 The generation mechanism creates solutions tailored to the user's emotional state and presents different methods depending on the situation. For example, if the user indicates "anxiety," the system prioritizes providing solutions that can be implemented quickly. 【0123】 Subsequently, a reference tagging mechanism adds links and identifiers to information sources related to the generated solution, enhancing its reliability and transparency. In addition, relevant reference information suitable for emotionally responsive solutions is also attached, providing users with more options. 【0124】 Ultimately, the server sends data to the terminal to present these solutions, which are then displayed on the terminal. Users can easily review the presented solutions and choose the option that best suits their emotional needs. This process results in a more pleasant user experience and improves the speed and effectiveness of problem-solving. 【0125】 The following describes the processing flow. 【0126】 Step 1: 【0127】 The user enters the question they want answered into the device and sends it. The device receives the input and activates the emotion engine. 【0128】 Step 2: 【0129】 The device passes the input text to an emotion engine, which analyzes the wording and context within the text to recognize the user's emotional state. The emotion engine identifies emotions such as "confusion," "anxiety," and "calmness." 【0130】 Step 3: 【0131】 The terminal sends data to the server, including user input and recognized sentiment information. This prepares the server to process the data based on both sentiment and text. 【0132】 Step 4: 【0133】 The server analyzes the received data using natural language processing techniques and extracts important keywords from the input text. Simultaneously, it continues to refer to sentiment information. 【0134】 Step 5: 【0135】 The server uses extracted keywords and sentiment information to search the information database. The search method adjusts the search criteria according to the sentiment, prioritizing the search for information that best corresponds to the user's emotions. 【0136】 Step 6: 【0137】 The server generates multiple solutions based on the search results. The generation method incorporates emotional information and adapts the ranking and presentation of options to the user's emotions. For example, if the user is highly anxious, it will emphasize quick solutions. 【0138】 Step 7: 【0139】 The server adds information sources related to the generated solution using a reference tagging mechanism. This ensures that users can verify the accuracy of the solution. 【0140】 Step 8: 【0141】 The server sends data to the terminal, including the final solution and its references. The terminal displays this information visually to the user, allowing the user to select the best solution from the displayed options. 【0142】 (Example 2) 【0143】 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 will be referred to as the "terminal." 【0144】 Traditional systems that provide uniform responses or solutions to user inquiries without considering their emotional state struggle to offer optimal solutions and improve the user experience. This leads to decreased problem-solving efficiency and difficulty in achieving sufficient user satisfaction. 【0145】 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. 【0146】 In this invention, the server includes emotion analysis means for recognizing emotional states, natural language processing means for analyzing and retrieving information, and means for optimizing and generating solutions based on emotions. This enables the provision of flexible and appropriate solutions that respond to the user's emotions. 【0147】 "User input" refers to information or questions that a user sends to the system via a terminal. 【0148】 "Sentiment analysis tools" are methods and technologies for recognizing emotional states by analyzing linguistic expressions and context contained in user input. 【0149】 "Information processing means" refers to a method for analyzing input information using natural language processing technology and identifying important elements. 【0150】 An "information collection" refers to the data and knowledge base accumulated within a system, and is a group of information that is subject to retrieval and analysis. 【0151】 A "search method" is a method for examining an information set using analyzed information and obtaining relevant data. 【0152】 A "generation method" is a method that has the function of creating and diversifying solutions that are tailored to the user's emotional state based on acquired information. 【0153】 "Information sources" refer to data and links that are referenced to support the reliability of the generated solution. 【0154】 "Display means" refers to a method of outputting the final generated solution to the user's terminal, making it viewable and operable for the user. 【0155】 This system aims to provide solutions to questions entered by users via a terminal. The system incorporates natural language processing technology with an emotion engine, which analyzes the user's emotional state to generate more appropriate responses. 【0156】 The terminal receives a question from the user as input and sends this question to the server. The emotion engine used here utilizes a natural language processing model to analyze the linguistic expressions and context that appear in the user's input. This allows it to recognize the user's emotional state and send corresponding data to the server. 【0157】 The server searches the information set based on the results of sentiment analysis. In this process, it uses natural language processing techniques to identify important keywords and optimize the information. The generative AI model used by the server has the ability to provide flexible solutions for diverse inputs. 【0158】 For example, a prompt such as "The user's emotion is 'anxiety,' what is the best suggestion to offer as a solution?" is passed to the AI ​​model. This prompt allows the AI ​​model to quickly generate a solution. 【0159】 For example, if a user inputs a concern such as "I've been so busy with work lately that I don't have time to exercise," the system could recognize emotions like "busyness" and "frustration" and suggest short exercises that can be done during work breaks. 【0160】 As described above, the present invention makes it possible to perform flexible information processing in response to the user's emotions and improve the user experience. 【0161】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0162】 Step 1: 【0163】 The user uses a terminal to input a question seeking a solution. The terminal receives this input and invokes the sentiment engine to begin analysis. It extracts linguistic expressions and context from the input text and processes sentiment information as data points. The output generates tags indicating the user's emotional state. 【0164】 Step 2: 【0165】 The terminal sends question data along with emotional information to the server. The server receives this input, analyzes the text using natural language processing tools, and extracts important keywords. This determines the core of the input text. As output, a set of the analyzed keywords is generated. 【0166】 Step 3: 【0167】 The server searches a data set based on keywords and sentiment information. This includes filtering and ranking text data. Information relevant to the emotional state is identified, and sentiment-based responses are optimized. The output provides relevant information and potential solutions tailored to the user's emotions. 【0168】 Step 4: 【0169】 The server uses a generative AI model to generate specific solutions. Example-based instructions are passed to the model as prompts. For example, a prompt might be, "The user's emotion is 'anxiety,' what solution would you recommend?" The generated solutions are diversified and adjusted to best address the user's emotion. 【0170】 Step 5: 【0171】 The server uses reference tagging to attach information sources and identifiers related to the proposed solution. To enhance reliability, relevant links are also attached to the solution. Finally, the polished solution is provided to the user. 【0172】 Step 6: 【0173】 The terminal receives solutions sent from the server and displays them on the screen in a format viewable by the user. Based on this information, the user can choose the option that is right for them and take action. This entire process allows the user to solve problems efficiently. 【0174】 (Application Example 2) 【0175】 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". 【0176】 Traditional systems provided mechanical responses without considering user emotions, resulting in a lack of user experience and reduced effectiveness in problem-solving. Furthermore, standard solutions were often unsatisfactory, especially in situations where emotional support was required. 【0177】 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. 【0178】 In this invention, the server includes a natural language processing means for processing information received from a user terminal, a search means for searching an information database based on keywords extracted by the natural language processing means, an emotion recognition means for analyzing the user's emotions, and a generation means for generating multiple solutions based on the search results and the user's emotional state. This makes it possible to present flexible and appropriate solutions that are in line with the user's emotional state. 【0179】 A "user terminal" is an electronic device used by a user to input and receive information. 【0180】 "Natural language processing means" refers to the process of analyzing text input by a user and extracting important keywords. 【0181】 A "keyword" is a word that is important for information retrieval, extracted from the user's input. 【0182】 An "information database" is a collection of data that stores information related to a user's question. 【0183】 "Search method" refers to the process of searching an information database using keywords and obtaining relevant information. 【0184】 "User emotions" refer to the psychological state that a user expresses when entering information. 【0185】 "Emotion recognition means" refers to the process of analyzing a user's emotions from their text input. 【0186】 "Generation method" refers to the process of creating multiple solutions based on search results and sentiment information. 【0187】 A "solution" is a suggestion or method offered to address a user's questions or problems. 【0188】 The system implementing this invention mainly consists of a server and a user terminal. The user inputs questions or problems through the terminal, and this information is processed by sending it to the server. 【0189】 The server first analyzes user input using natural language processing techniques and extracts important keywords. Specifically, the Python library spaCy is used for natural language processing. Additionally, TextBlob is used to analyze the emotions contained in the user's input. This emotion analysis enables processing that takes the user's psychological state into account. 【0190】 Next, the information database is searched based on the extracted keywords to obtain possible solutions. An SQLite database is used for the search, efficiently filtering relevant information. 【0191】 The server generates the optimal solution based on the search results and the user's emotional state. Relevant information sources are added to the generated solution to enhance its reliability, and it is then sent to the user's terminal. 【0192】 The user device displays the provided solutions to the user, allowing them to easily select an option that suits their emotional state. The front-end of the device is typically built using a framework such as React Native. 【0193】 For example, if a user enters a problem such as "payment failed," the server recognizes the emotion of "confusion" and proposes a solution that prioritizes speed. In this way, users can resolve their problems efficiently while minimizing stress. 【0194】 Examples of prompts to input into a generative AI model include the following: 【0195】 "Analyze the user's emotional state from their input text and propose a solution based on that emotion. Input text: I'm having trouble with my payment." 【0196】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0197】 Step 1: 【0198】 The user uses a terminal to input questions or problems in natural language. The entered text is received by the terminal's interface and sent to the server. The input here is a specific problem statement, such as "the payment didn't go through." 【0199】 Step 2: 【0200】 The server analyzes the received input text using natural language processing techniques. In this step, spaCy is used to extract key keywords from the text. The input is text from the user, and the output is a list of extracted keywords. This process identifies important information. 【0201】 Step 3: 【0202】 Next, the server uses TextBlob to analyze the user's text to determine their emotional state. Raw text from the user is provided as input, and an emotional assessment result is generated as output. This emotional information is then used in subsequent processing to optimize the solution. 【0203】 Step 4: 【0204】 Based on the extracted keywords and sentiment information, the server performs a search on the information database. Using an SQLite database, it retrieves highly relevant information and solutions. The input is a list of keywords and sentiment information, and the output is a set of records of related data. 【0205】 Step 5: 【0206】 The server uses search results and sentiment information to generate multiple optimal solutions. In this step, a generative AI model is used to create solutions and add relevant information sources. The input is search results and sentiment information, and the output is a list of solutions. 【0207】 Step 6: 【0208】 Finally, the generated solution is sent to the user's terminal and displayed to the user on the terminal. Here, a visualized solution is output to the interface for the user to easily review and take action. The displayed information includes steps for the solution and relevant links. 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 [Second Embodiment] 【0213】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0214】 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. 【0215】 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). 【0216】 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. 【0217】 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. 【0218】 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). 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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". 【0225】 This invention is a system for rapidly providing solutions from diverse information sources within a company, and aims to improve efficiency throughout the stages of information collection, processing, and presentation. The program is implemented using the following methods. 【0226】 First, the user enters their question in text format using their device. The device sends the input data to the server, which then analyzes this data using natural language processing. This allows important keywords and intent to be extracted from the text. For example, if a question such as "How to deal with a defect in a new product" is entered, keywords such as "new product," "defect," and "solution" are recognized as important. 【0227】 The server searches the information database based on the extracted keywords. The search method allows for the rapid identification of relevant documents and past cases. In this step, past cases where similar problems were solved and standard troubleshooting procedures described in the product manual are referenced. 【0228】 Next, the server generates multiple solutions based on the search results. The generation method constructs candidates based on different perspectives and methods, providing the user with choices. For example, it can offer both immediate solutions and long-term solutions. 【0229】 Furthermore, the server adds references such as links and document numbers to the sources of information corresponding to the generated solutions. This allows users to verify the reliability of the presented solutions and refer to further details as needed. 【0230】 Finally, the server returns the generated solution to the user's terminal, which displays it in an easy-to-understand format. The user can then use this information to select the appropriate action to take. This consistent process enables faster and more reliable work execution than ever before. 【0231】 This system is designed to support user decision-making by utilizing diverse information. Specifically, it can be applied to various tasks, including not only troubleshooting but also planning new projects and considering customer response strategies. 【0232】 The following describes the processing flow. 【0233】 Step 1: 【0234】 The user enters their question into the terminal and presses the send button. The terminal then sends the entered question to the server. 【0235】 Step 2: 【0236】 The server passes the user's question to a natural language processing system for text analysis. This allows the server to extract important keywords and context. 【0237】 Step 3: 【0238】 The server searches the information database using the extracted keywords. It utilizes search methods to explore relevant documents and past cases, and identifies the corresponding data. 【0239】 Step 4: 【0240】 The server generates multiple solutions based on the information obtained from the search results. Here, it generates options by considering different perspectives and methods, providing a variety of solutions. 【0241】 Step 5: 【0242】 The server adds references to the relevant sources of information for each solution, including links and document numbers, so that users can later access more details. 【0243】 Step 6: 【0244】 The server sends the generated solutions and references to the user's terminal. The terminal displays them to the user in an easy-to-read format. The user can review the provided solutions and make decisions based on the useful information. 【0245】 (Example 1) 【0246】 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." 【0247】 In today's world, where the sheer volume and diversity of information is increasing, companies need systems to support information gathering, analysis, and decision-making in order to respond quickly and effectively to the problems they face. Current methods make this process cumbersome and time-consuming, which can lead to delays in decision-making and even incorrect judgments. 【0248】 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. 【0249】 In this invention, the server includes a natural language processing means for analyzing information received from a user terminal and extracting important terms, a search means for searching an information set based on the important terms, and a generation means for generating a number of solutions based on the search results. This makes it possible to answer user questions quickly and accurately. 【0250】 A "user terminal" is an electronic device used by a user to input information, and typically includes computers and smartphones. 【0251】 "Natural language processing means" refers to techniques or tools for analyzing input text information and extracting important words or phrases. 【0252】 "Search means" refers to a method or apparatus for searching an information set using extracted terms and obtaining related data. 【0253】 "Generative means" refers to a technology or process for creating and presenting multiple solutions based on search results. 【0254】 "Additional means" refers to a function or mechanism for adding relevant information to the generated solution. 【0255】 "Presentation means" refers to the means for displaying the solution generated on the user terminal, and generally, a display or graphical user interface is used. 【0256】 An "information collection" is a collection of information that is searched, and usually refers to a database system. 【0257】 A description of the embodiment for carrying out the invention will be provided. 【0258】 This invention is a system that provides solutions quickly from diverse information sources within a company. The system is mainly composed of user terminals and a server working together to efficiently collect, analyze, and present various types of information. 【0259】 The user first enters their question in text format using their device. This device is typically a personal computer or smartphone, and the user enters text using a keyboard or touchscreen. Once the user has finished entering the question, the device sends the information to the server. 【0260】 The server uses natural language processing (NLP) techniques to analyze the received information. Specifically, libraries such as Python's NLTK and spaCy are used as natural language processing tools. This allows for the extraction of important words and phrases from text data, enabling an accurate understanding of the user's intent. 【0261】 Next, the server uses the extracted keywords to search the information set. SQL databases or Elasticsearch are commonly used for this search. This allows for the efficient retrieval and reference of relevant documents and past case studies. 【0262】 Subsequently, the server generates numerous solutions using a generative AI model based on the search results. This process considers different perspectives and methods, resulting in a diverse range of solutions. In this process, the AI ​​model utilizes the input data to perform inference. 【0263】 Furthermore, the generated solutions are accompanied by references to relevant information. Specifically, links to information sources and document numbers are added to the solutions, making it easier for users to verify their reliability. 【0264】 Finally, the server sends the generated solution to the user's terminal. The terminal displays the received solution in an easy-to-understand format. This allows the user to make quick decisions based on the presented information. 【0265】 For example, when a user enters a prompt such as, "Please tell me about successful examples of new projects," the system will present relevant success stories that can be used as reference for project planning. 【0266】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0267】 Step 1: 【0268】 The user uses their device to input questions or problems they want to resolve as text. For example, they might enter a prompt such as "How to fix a bug in the new product." The entered text is then sent by the device to the server via a secure communication method (e.g., HTTPS). 【0269】 Step 2: 【0270】 The server analyzes the received text data using natural language processing (NLTK) tools. This analysis utilizes libraries such as NLTK and spaCy. The text is tokenized, parts of speech are tagged, and important words are extracted from the results. It takes raw text as input and generates extracted keywords as output. 【0271】 Step 3: 【0272】 The server uses the extracted keywords to search the information set. The search process utilizes systems such as SQL databases or Elasticsearch to efficiently identify relevant documents and past cases. The input is keywords, and the output is a set of related data entries. 【0273】 Step 4: 【0274】 The server generates solutions using a generative AI model based on the information obtained through the search. The AI ​​model takes the search results as input and outputs multiple solutions that consider different perspectives and methods. Specifically, these include methods for quick responses as well as solutions from a long-term perspective. 【0275】 Step 5: 【0276】 The server adds references to relevant information for each generated solution. By adding links to information sources and document IDs to the solution, users can later access more detailed information. The input is the solution proposal, and the output is the solution with the added references. 【0277】 Step 6: 【0278】 The server sends the final solution to the user's terminal. The terminal then displays the received solution, using a graphical user interface or similar method to present it clearly. This allows the user to make quick decisions based on the presented information. The output to the terminal is formatted solution information. 【0279】 (Application Example 1) 【0280】 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." 【0281】 With the proliferation of visual information devices, users have a growing need to quickly obtain personalized information from diverse content. However, it is difficult for users to select appropriate content from a large amount of information through visual means, and there is a need for a means to obtain personalized recommendations efficiently and effectively. Conventional technologies have the challenge of not being able to provide recommendations that fully utilize the user's preferences and past viewing history. 【0282】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0283】 In this invention, the server includes information processing means for processing information received from a user terminal, search means for searching for an information aggregate based on the concepts extracted by the information processing means, and generation means for generating various options based on the search results. Thereby, the user can quickly obtain individualized recommendation information, and appropriate content selection is possible through a visual information device. 【0284】 The "information processing means" is a device or process that analyzes information received from a user terminal and extracts important concepts. 【0285】 The "information aggregate" refers to a data storage or database that aggregates and holds related information. 【0286】 The "search means" is a device or process for quickly identifying related information from within an information aggregate. 【0287】 The "generation means" is a device or process for constructing and providing various options based on search results. 【0288】 The "recording addition means" is a device or process for adding an information source related to the generated options. 【0289】 The "display means" is a device or process for visually presenting the generated options to the user. 【0290】 The "adaptive presentation means" is a function or process for adaptively presenting individualized recommendation information to the user based on content. 【0291】 This system consists of a user terminal, a server, and an information aggregate. Since the user processes information visually through a head-mounted display, the user first inputs questions related to the visual content into the terminal using a catalyst. 【0292】 The server analyzes user input using information processing tools and extracts important concepts. It uses spaCy, a natural language processing library, to identify meaningful keywords from the input text. In this process, the server manipulates the database using MySQL to efficiently search for relevant information within the data collection. 【0293】 Based on search results, the server utilizes Scikit-learn to generate personalized recommendations, creating a diverse range of options. This enables adaptive information presentation tailored to the user's preferences and past viewing history. The generated information is further enhanced with additional information through a recording mechanism to increase its reliability. The server then visualizes the generated options using an adaptive presentation mechanism, displaying appropriate information in real time to guide the user. 【0294】 For example, if a user enters "I want to experience the latest sci-fi movie," the system will identify relevant VR content and provide descriptions and recommendations based on its characteristics. An example of a prompt is shown below: "(Request) Action-packed VR content related to sports." 【0295】 In this way, users can enjoy real-time access to information tailored to their needs. 【0296】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0297】 Step 1: 【0298】 The user wears a head-mounted display and enters a question related to visual content into the terminal. The user's question is sent to the terminal as input data. 【0299】 Step 2: 【0300】 The terminal sends the entered question to the server. The server uses a natural language processing library (spaCy) to extract important keywords from the question. For example, for an input like "I want to experience the latest science fiction movies", keywords such as "latest", "science fiction movies", and "experience" are extracted. This is the input to the server, and the extracted keywords become the output for the next processing. 【0301】 Step 3: 【0302】 The server searches for an information aggregate based on the extracted keywords. It uses MySQL to query the database quickly and identify relevant VR content information and data points. With the keywords as input, relevant information is output. 【0303】 Step 4: 【0304】 The server executes a content recommendation algorithm that takes into account the user's preferences and past viewing history using Scikit-learn based on the data obtained from the search. This algorithm generates individualized recommendation information. The search results are the input, and the recommended content is the output. 【0305】 Step 5: 【0306】 The server uses a recording addition means to add information such as the history of the information source to give reliability to the generated recommendation information. The recommendation information is the input, and the recommendation information with recording added is the output. 【0307】 Step 6: 【0308】 The server uses adaptive presentation means to send recommended information to the user's terminal. The head-mounted display visually presents this information to the user, allowing the user to select appropriate content based on that information. The user visually receives the recommended content via the terminal. The adaptively presented information is the input, and the presentation is the output. 【0309】 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. 【0310】 This invention combines an emotion engine with a system that processes user questions and provides solutions, thereby achieving flexible responses based on the user's emotional state. The following specifically describes an embodiment of the system, including the functions of each component. 【0311】 The user uses the terminal to input a question seeking a solution. In the process of sending this to the server, the terminal invokes an emotion engine, which analyzes the linguistic expressions and context of the user's input to recognize their emotional state. For example, if the user inputs "I'm really troubled by this problem," the system will determine that the user is experiencing emotions such as "confusion" or "anxiety." 【0312】 The server uses natural language processing to analyze important keywords from the received information and searches the information database. In addition to normal information retrieval, the search method optimizes the results by considering emotional information provided by the emotion engine. This allows for the generation of solutions that are appropriate to the user's emotions, even for the same question. 【0313】 The generation mechanism creates solutions tailored to the user's emotional state and presents different methods depending on the situation. For example, if the user indicates "anxiety," the system prioritizes providing solutions that can be implemented quickly. 【0314】 Subsequently, a reference tagging mechanism adds links and identifiers to information sources related to the generated solution, enhancing its reliability and transparency. In addition, relevant reference information suitable for emotionally responsive solutions is also attached, providing users with more options. 【0315】 Ultimately, the server sends data to the terminal to present these solutions, which are then displayed on the terminal. Users can easily review the presented solutions and choose the option that best suits their emotional needs. This process results in a more pleasant user experience and improves the speed and effectiveness of problem-solving. 【0316】 The following describes the processing flow. 【0317】 Step 1: 【0318】 The user enters the question they want answered into the device and sends it. The device receives the input and activates the emotion engine. 【0319】 Step 2: 【0320】 The device passes the input text to an emotion engine, which analyzes the wording and context within the text to recognize the user's emotional state. The emotion engine identifies emotions such as "confusion," "anxiety," and "calmness." 【0321】 Step 3: 【0322】 The terminal sends data to the server, including user input and recognized sentiment information. This prepares the server to process the data based on both sentiment and text. 【0323】 Step 4: 【0324】 The server analyzes the received data using natural language processing techniques and extracts important keywords from the input text. Simultaneously, it continues to refer to sentiment information. 【0325】 Step 5: 【0326】 The server uses extracted keywords and sentiment information to search the information database. The search method adjusts the search criteria according to the sentiment, prioritizing the search for information that best corresponds to the user's emotions. 【0327】 Step 6: 【0328】 The server generates multiple solutions based on the search results. The generation method incorporates emotional information and adapts the ranking and presentation of options to the user's emotions. For example, if the user is highly anxious, it will emphasize quick solutions. 【0329】 Step 7: 【0330】 The server adds information sources related to the generated solution using a reference tagging mechanism. This ensures that users can verify the accuracy of the solution. 【0331】 Step 8: 【0332】 The server sends data to the terminal, including the final solution and its references. The terminal displays this information visually to the user, allowing the user to select the best solution from the displayed options. 【0333】 (Example 2) 【0334】 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". 【0335】 Traditional systems that provide uniform responses or solutions to user inquiries without considering their emotional state struggle to offer optimal solutions and improve the user experience. This leads to decreased problem-solving efficiency and difficulty in achieving sufficient user satisfaction. 【0336】 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. 【0337】 In this invention, the server includes emotion analysis means for recognizing emotional states, natural language processing means for analyzing and retrieving information, and means for optimizing and generating solutions based on emotions. This enables the provision of flexible and appropriate solutions that respond to the user's emotions. 【0338】 "User input" refers to information or questions that a user sends to the system via a terminal. 【0339】 "Sentiment analysis tools" are methods and technologies for recognizing emotional states by analyzing linguistic expressions and context contained in user input. 【0340】 "Information processing means" refers to a method for analyzing input information using natural language processing technology and identifying important elements. 【0341】 An "information collection" refers to the data and knowledge base accumulated within a system, and is a group of information that is subject to retrieval and analysis. 【0342】 A "search method" is a method for examining an information set using analyzed information and obtaining relevant data. 【0343】 A "generation method" is a method that has the function of creating and diversifying solutions that are tailored to the user's emotional state based on acquired information. 【0344】 "Information sources" refer to data and links that are referenced to support the reliability of the generated solution. 【0345】 "Display means" refers to a method of outputting the final generated solution to the user's terminal, making it viewable and operable for the user. 【0346】 This system aims to provide solutions to questions entered by users via a terminal. The system incorporates natural language processing technology with an emotion engine, which analyzes the user's emotional state to generate more appropriate responses. 【0347】 The terminal receives a question from the user as input and sends this question to the server. The emotion engine used here utilizes a natural language processing model to analyze the linguistic expressions and context that appear in the user's input. This allows it to recognize the user's emotional state and send corresponding data to the server. 【0348】 The server searches the information set based on the results of sentiment analysis. In this process, it uses natural language processing techniques to identify important keywords and optimize the information. The generative AI model used by the server has the ability to provide flexible solutions for diverse inputs. 【0349】 For example, a prompt such as "The user's emotion is 'anxiety,' what is the best suggestion to offer as a solution?" is passed to the AI ​​model. This prompt allows the AI ​​model to quickly generate a solution. 【0350】 For example, if a user inputs a concern such as "I've been so busy with work lately that I don't have time to exercise," the system could recognize emotions like "busyness" and "frustration" and suggest short exercises that can be done during work breaks. 【0351】 As described above, the present invention makes it possible to perform flexible information processing in response to the user's emotions and improve the user experience. 【0352】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0353】 Step 1: 【0354】 The user uses a terminal to input a question seeking a solution. The terminal receives this input and invokes the sentiment engine to begin analysis. It extracts linguistic expressions and context from the input text and processes sentiment information as data points. The output generates tags indicating the user's emotional state. 【0355】 Step 2: 【0356】 The terminal sends question data along with emotional information to the server. The server receives this input, analyzes the text using natural language processing tools, and extracts important keywords. This determines the core of the input text. As output, a set of the analyzed keywords is generated. 【0357】 Step 3: 【0358】 The server searches a data set based on keywords and sentiment information. This includes filtering and ranking text data. Information relevant to the emotional state is identified, and sentiment-based responses are optimized. The output provides relevant information and potential solutions tailored to the user's emotions. 【0359】 Step 4: 【0360】 The server uses a generative AI model to generate specific solutions. Example-based instructions are passed to the model as prompts. For example, a prompt might be, "The user's emotion is 'anxiety,' what solution would you recommend?" The generated solutions are diversified and adjusted to best address the user's emotion. 【0361】 Step 5: 【0362】 The server uses reference tagging to attach information sources and identifiers related to the proposed solution. To enhance reliability, relevant links are also attached to the solution. Finally, the polished solution is provided to the user. 【0363】 Step 6: 【0364】 The terminal receives solutions sent from the server and displays them on the screen in a format viewable by the user. Based on this information, the user can choose the option that is right for them and take action. This entire process allows the user to solve problems efficiently. 【0365】 (Application Example 2) 【0366】 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." 【0367】 Traditional systems provided mechanical responses without considering user emotions, resulting in a lack of user experience and reduced effectiveness in problem-solving. Furthermore, standard solutions were often unsatisfactory, especially in situations where emotional support was required. 【0368】 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. 【0369】 In this invention, the server includes a natural language processing means for processing information received from a user terminal, a search means for searching an information database based on keywords extracted by the natural language processing means, an emotion recognition means for analyzing the user's emotions, and a generation means for generating multiple solutions based on the search results and the user's emotional state. This makes it possible to present flexible and appropriate solutions that are in line with the user's emotional state. 【0370】 A "user terminal" is an electronic device used by a user to input and receive information. 【0371】 "Natural language processing means" refers to the process of analyzing text input by a user and extracting important keywords. 【0372】 A "keyword" is a word that is important for information retrieval, extracted from the user's input. 【0373】 An "information database" is a collection of data that stores information related to a user's question. 【0374】 "Search method" refers to the process of searching an information database using keywords and obtaining relevant information. 【0375】 "User emotions" refer to the psychological state that a user expresses when entering information. 【0376】 "Emotion recognition means" refers to the process of analyzing a user's emotions from their text input. 【0377】 "Generation method" refers to the process of creating multiple solutions based on search results and sentiment information. 【0378】 A "solution" is a suggestion or method offered to address a user's questions or problems. 【0379】 The system implementing this invention mainly consists of a server and a user terminal. The user inputs questions or problems through the terminal, and this information is processed by sending it to the server. 【0380】 The server first analyzes user input using natural language processing techniques and extracts important keywords. Specifically, the Python library spaCy is used for natural language processing. Additionally, TextBlob is used to analyze the emotions contained in the user's input. This emotion analysis enables processing that takes the user's psychological state into account. 【0381】 Next, the information database is searched based on the extracted keywords to obtain possible solutions. An SQLite database is used for the search, efficiently filtering relevant information. 【0382】 The server generates the optimal solution based on the search results and the user's emotional state. Relevant information sources are added to the generated solution to enhance its reliability, and it is then sent to the user's terminal. 【0383】 The user device displays the provided solutions to the user, allowing them to easily select an option that suits their emotional state. The front-end of the device is typically built using a framework such as React Native. 【0384】 For example, if a user enters a problem such as "payment failed," the server recognizes the emotion of "confusion" and proposes a solution that prioritizes speed. In this way, users can resolve their problems efficiently while minimizing stress. 【0385】 Examples of prompts to input into a generative AI model include the following: 【0386】 "Analyze the user's emotional state from their input text and propose a solution based on that emotion. Input text: I'm having trouble with my payment." 【0387】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0388】 Step 1: 【0389】 The user uses a terminal to input questions or problems in natural language. The entered text is received by the terminal's interface and sent to the server. The input here is a specific problem statement, such as "the payment didn't go through." 【0390】 Step 2: 【0391】 The server analyzes the received input text using natural language processing techniques. In this step, spaCy is used to extract key keywords from the text. The input is text from the user, and the output is a list of extracted keywords. This process identifies important information. 【0392】 Step 3: 【0393】 Next, the server uses TextBlob to analyze the user's text to determine their emotional state. Raw text from the user is provided as input, and an emotional assessment result is generated as output. This emotional information is then used in subsequent processing to optimize the solution. 【0394】 Step 4: 【0395】 Based on the extracted keywords and sentiment information, the server performs a search on the information database. Using an SQLite database, it retrieves highly relevant information and solutions. The input is a list of keywords and sentiment information, and the output is a set of records of related data. 【0396】 Step 5: 【0397】 The server uses search results and sentiment information to generate multiple optimal solutions. In this step, a generative AI model is used to create solutions and add relevant information sources. The input is search results and sentiment information, and the output is a list of solutions. 【0398】 Step 6: 【0399】 Finally, the generated solution is sent to the user's terminal and displayed to the user on the terminal. Here, a visualized solution is output to the interface for the user to easily review and take action. The displayed information includes steps for the solution and relevant links. 【0400】 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. 【0401】 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. 【0402】 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. 【0403】 [Third Embodiment] 【0404】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0405】 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. 【0406】 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). 【0407】 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. 【0408】 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. 【0409】 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). 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 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. 【0414】 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. 【0415】 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". 【0416】 This invention is a system for rapidly providing solutions from diverse information sources within a company, and aims to improve efficiency throughout the stages of information collection, processing, and presentation. The program is implemented using the following methods. 【0417】 First, the user enters their question in text format using their device. The device sends the input data to the server, which then analyzes this data using natural language processing. This allows important keywords and intent to be extracted from the text. For example, if a question such as "How to deal with a defect in a new product" is entered, keywords such as "new product," "defect," and "solution" are recognized as important. 【0418】 The server searches the information database based on the extracted keywords. The search method allows for the rapid identification of relevant documents and past cases. In this step, past cases where similar problems were solved and standard troubleshooting procedures described in the product manual are referenced. 【0419】 Next, the server generates multiple solutions based on the search results. The generation method constructs candidates based on different perspectives and methods, providing the user with choices. For example, it can offer both immediate solutions and long-term solutions. 【0420】 Furthermore, the server adds references such as links and document numbers to the sources of information corresponding to the generated solutions. This allows users to verify the reliability of the presented solutions and refer to further details as needed. 【0421】 Finally, the server returns the generated solution to the user's terminal, which displays it in an easy-to-understand format. The user can then use this information to select the appropriate action to take. This consistent process enables faster and more reliable work execution than ever before. 【0422】 This system is designed to support user decision-making by utilizing diverse information. Specifically, it can be applied to various tasks, including not only troubleshooting but also planning new projects and considering customer response strategies. 【0423】 The following describes the processing flow. 【0424】 Step 1: 【0425】 The user enters their question into the terminal and presses the send button. The terminal then sends the entered question to the server. 【0426】 Step 2: 【0427】 The server passes the user's question to a natural language processing system for text analysis. This allows the server to extract important keywords and context. 【0428】 Step 3: 【0429】 The server searches the information database using the extracted keywords. It utilizes search methods to explore relevant documents and past cases, and identifies the corresponding data. 【0430】 Step 4: 【0431】 The server generates multiple solutions based on the information obtained from the search results. Here, it generates options by considering different perspectives and methods, providing a variety of solutions. 【0432】 Step 5: 【0433】 The server adds references to the relevant sources of information for each solution, including links and document numbers, so that users can later access more details. 【0434】 Step 6: 【0435】 The server sends the generated solutions and references to the user's terminal. The terminal displays them to the user in an easy-to-read format. The user can review the provided solutions and make decisions based on the useful information. 【0436】 (Example 1) 【0437】 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." 【0438】 In today's world, where the sheer volume and diversity of information is increasing, companies need systems to support information gathering, analysis, and decision-making in order to respond quickly and effectively to the problems they face. Current methods make this process cumbersome and time-consuming, which can lead to delays in decision-making and even incorrect judgments. 【0439】 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. 【0440】 In this invention, the server includes a natural language processing means for analyzing information received from a user terminal and extracting important terms, a search means for searching an information set based on the important terms, and a generation means for generating a number of solutions based on the search results. This makes it possible to answer user questions quickly and accurately. 【0441】 A "user terminal" is an electronic device used by a user to input information, and typically includes computers and smartphones. 【0442】 "Natural language processing means" refers to techniques or tools for analyzing input text information and extracting important words or phrases. 【0443】 "Search means" refers to a method or apparatus for searching an information set using extracted terms and obtaining related data. 【0444】 "Generative means" refers to a technology or process for creating and presenting multiple solutions based on search results. 【0445】 "Additional means" refers to a function or mechanism for adding relevant information to the generated solution. 【0446】 "Presentation means" refers to the means for displaying the solution generated on the user terminal, and generally, a display or graphical user interface is used. 【0447】 An "information collection" is a collection of information that is searched, and usually refers to a database system. 【0448】 A description of the embodiment for carrying out the invention will be provided. 【0449】 This invention is a system that provides solutions quickly from diverse information sources within a company. The system is mainly composed of user terminals and a server working together to efficiently collect, analyze, and present various types of information. 【0450】 The user first enters their question in text format using their device. This device is typically a personal computer or smartphone, and the user enters text using a keyboard or touchscreen. Once the user has finished entering the question, the device sends the information to the server. 【0451】 The server uses natural language processing (NLP) techniques to analyze the received information. Specifically, libraries such as Python's NLTK and spaCy are used as natural language processing tools. This allows for the extraction of important words and phrases from text data, enabling an accurate understanding of the user's intent. 【0452】 Next, the server uses the extracted keywords to search the information set. SQL databases or Elasticsearch are commonly used for this search. This allows for the efficient retrieval and reference of relevant documents and past case studies. 【0453】 Subsequently, the server generates numerous solutions using a generative AI model based on the search results. This process considers different perspectives and methods, resulting in a diverse range of solutions. In this process, the AI ​​model utilizes the input data to perform inference. 【0454】 Furthermore, the generated solutions are accompanied by references to relevant information. Specifically, links to information sources and document numbers are added to the solutions, making it easier for users to verify their reliability. 【0455】 Finally, the server sends the generated solution to the user's terminal. The terminal displays the received solution in an easy-to-understand format. This allows the user to make quick decisions based on the presented information. 【0456】 For example, when a user enters a prompt such as, "Please tell me about successful examples of new projects," the system will present relevant success stories that can be used as reference for project planning. 【0457】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0458】 Step 1: 【0459】 The user uses their device to input questions or problems they want to resolve as text. For example, they might enter a prompt such as "How to fix a bug in the new product." The entered text is then sent by the device to the server via a secure communication method (e.g., HTTPS). 【0460】 Step 2: 【0461】 The server analyzes the received text data using natural language processing (NLTK) tools. This analysis utilizes libraries such as NLTK and spaCy. The text is tokenized, parts of speech are tagged, and important words are extracted from the results. It takes raw text as input and generates extracted keywords as output. 【0462】 Step 3: 【0463】 The server uses the extracted keywords to search the information set. The search process utilizes systems such as SQL databases or Elasticsearch to efficiently identify relevant documents and past cases. The input is keywords, and the output is a set of related data entries. 【0464】 Step 4: 【0465】 The server generates solutions using a generative AI model based on the information obtained through the search. The AI ​​model takes the search results as input and outputs multiple solutions that consider different perspectives and methods. Specifically, these include methods for quick responses as well as solutions from a long-term perspective. 【0466】 Step 5: 【0467】 The server adds references to relevant information for each generated solution. By adding links to information sources and document IDs to the solution, users can later access more detailed information. The input is the solution proposal, and the output is the solution with the added references. 【0468】 Step 6: 【0469】 The server sends the final solution to the user's terminal. The terminal then displays the received solution, using a graphical user interface or similar method to present it clearly. This allows the user to make quick decisions based on the presented information. The output to the terminal is formatted solution information. 【0470】 (Application Example 1) 【0471】 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." 【0472】 With the proliferation of visual information devices, users have a growing need to quickly obtain personalized information from diverse content. However, it is difficult for users to select appropriate content from a large amount of information through visual means, and there is a need for a means to obtain personalized recommendations efficiently and effectively. Conventional technologies have the challenge of not being able to provide recommendations that fully utilize the user's preferences and past viewing history. 【0473】 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. 【0474】 In this invention, the server includes information processing means for processing information received from a user terminal, search means for searching an information aggregate based on concepts extracted by the information processing means, and generation means for generating a variety of options based on the search results. This enables the user to quickly obtain personalized recommendation information and select appropriate content through a visual information device. 【0475】 "Information processing means" refers to a device or process that analyzes information received from a user terminal and extracts important concepts. 【0476】 An "information aggregate" refers to a data storage or database that aggregates and holds related information. 【0477】 A "search method" is a device or process for quickly identifying relevant information within an information aggregate. 【0478】 "Generation means" refers to a device or process for constructing and providing diverse options based on search results. 【0479】 "Recording addition means" refers to a device or process for adding information sources related to the generated choices. 【0480】 "Display means" refers to a device or process that visually presents the generated options to the user. 【0481】 "Adaptive presentation means" refers to a function or process that adaptively displays personalized recommendation information to the user based on the content. 【0482】 This system consists of a user terminal, a server, and an information aggregate. Since the user processes information visually through a head-mounted display, the user first inputs questions related to the visual content into the terminal using a catalyst. 【0483】 The server analyzes user input using information processing tools and extracts important concepts. It uses spaCy, a natural language processing library, to identify meaningful keywords from the input text. In this process, the server manipulates the database using MySQL to efficiently search for relevant information within the data collection. 【0484】 Based on search results, the server utilizes Scikit-learn to generate personalized recommendations, creating a diverse range of options. This enables adaptive information presentation tailored to the user's preferences and past viewing history. The generated information is further enhanced with additional information through a recording mechanism to increase its reliability. The server then visualizes the generated options using an adaptive presentation mechanism, displaying appropriate information in real time to guide the user. 【0485】 For example, if a user enters "I want to experience the latest sci-fi movie," the system will identify relevant VR content and provide descriptions and recommendations based on its characteristics. An example of a prompt is shown below: "(Request) Action-packed VR content related to sports." 【0486】 In this way, users can enjoy real-time access to information tailored to their needs. 【0487】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0488】 Step 1: 【0489】 The user wears a head-mounted display and inputs questions related to visual content into a terminal. The user's questions are sent to the terminal as input data. 【0490】 Step 2: 【0491】 The terminal sends the entered question to the server. The server uses a natural language processing library (spaCy) to extract important keywords from the question. For example, in the input "I want to experience the latest sci-fi movie," keywords such as "latest," "sci-fi movie," and "experience" are extracted. This is the input to the server, and the extracted keywords become the output for the next processing step. 【0492】 Step 3: 【0493】 The server searches the information repository based on the extracted keywords. It uses MySQL to quickly query the database and identify relevant VR content information and data points. Keywords are input, and related information is output. 【0494】 Step 4: 【0495】 The server uses Scikit-learn to execute a content recommendation algorithm based on the data obtained from the search, taking into account the user's preferences and past viewing history. This algorithm generates personalized recommendation information. The search results are the input, and the recommended content is the output. 【0496】 Step 5: 【0497】 To ensure the reliability of the generated recommendations, the server uses recording mechanisms to add information such as the history of the information source. The recommendation information is the input, and the recorded recommendation information is the output. 【0498】 Step 6: 【0499】 The server uses adaptive presentation means to send recommended information to the user's terminal. The head-mounted display visually presents this information to the user, allowing the user to select appropriate content based on that information. The user visually receives the recommended content via the terminal. The adaptively presented information is the input, and the presentation is the output. 【0500】 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. 【0501】 This invention combines an emotion engine with a system that processes user questions and provides solutions, thereby achieving flexible responses based on the user's emotional state. The following specifically describes an embodiment of the system, including the functions of each component. 【0502】 The user uses the terminal to input a question seeking a solution. In the process of sending this to the server, the terminal invokes an emotion engine, which analyzes the linguistic expressions and context of the user's input to recognize their emotional state. For example, if the user inputs "I'm really troubled by this problem," the system will determine that the user is experiencing emotions such as "confusion" or "anxiety." 【0503】 The server uses natural language processing to analyze important keywords from the received information and searches the information database. In addition to normal information retrieval, the search method optimizes the results by considering emotional information provided by the emotion engine. This allows for the generation of solutions that are appropriate to the user's emotions, even for the same question. 【0504】 The generation mechanism creates solutions tailored to the user's emotional state and presents different methods depending on the situation. For example, if the user indicates "anxiety," the system prioritizes providing solutions that can be implemented quickly. 【0505】 Subsequently, a reference tagging mechanism adds links and identifiers to information sources related to the generated solution, enhancing its reliability and transparency. In addition, relevant reference information suitable for emotionally responsive solutions is also attached, providing users with more options. 【0506】 Ultimately, the server sends data to the terminal to present these solutions, which are then displayed on the terminal. Users can easily review the presented solutions and choose the option that best suits their emotional needs. This process results in a more pleasant user experience and improves the speed and effectiveness of problem-solving. 【0507】 The following describes the processing flow. 【0508】 Step 1: 【0509】 The user enters the question they want answered into the device and sends it. The device receives the input and activates the emotion engine. 【0510】 Step 2: 【0511】 The device passes the input text to an emotion engine, which analyzes the wording and context within the text to recognize the user's emotional state. The emotion engine identifies emotions such as "confusion," "anxiety," and "calmness." 【0512】 Step 3: 【0513】 The terminal sends data to the server, including user input and recognized sentiment information. This prepares the server to process the data based on both sentiment and text. 【0514】 Step 4: 【0515】 The server analyzes the received data using natural language processing techniques and extracts important keywords from the input text. Simultaneously, it continues to refer to sentiment information. 【0516】 Step 5: 【0517】 The server uses extracted keywords and sentiment information to search the information database. The search method adjusts the search criteria according to the sentiment, prioritizing the search for information that best corresponds to the user's emotions. 【0518】 Step 6: 【0519】 The server generates multiple solutions based on the search results. The generation method incorporates emotional information and adapts the ranking and presentation of options to the user's emotions. For example, if the user is highly anxious, it will emphasize quick solutions. 【0520】 Step 7: 【0521】 The server adds information sources related to the generated solution using a reference tagging mechanism. This ensures that users can verify the accuracy of the solution. 【0522】 Step 8: 【0523】 The server sends data to the terminal, including the final solution and its references. The terminal displays this information visually to the user, allowing the user to select the best solution from the displayed options. 【0524】 (Example 2) 【0525】 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." 【0526】 Traditional systems that provide uniform responses or solutions to user inquiries without considering their emotional state struggle to offer optimal solutions and improve the user experience. This leads to decreased problem-solving efficiency and difficulty in achieving sufficient user satisfaction. 【0527】 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. 【0528】 In this invention, the server includes emotion analysis means for recognizing emotional states, natural language processing means for analyzing and retrieving information, and means for optimizing and generating solutions based on emotions. This enables the provision of flexible and appropriate solutions that respond to the user's emotions. 【0529】 "User input" refers to information or questions that a user sends to the system via a terminal. 【0530】 "Sentiment analysis tools" are methods and technologies for recognizing emotional states by analyzing linguistic expressions and context contained in user input. 【0531】 "Information processing means" refers to a method for analyzing input information using natural language processing technology and identifying important elements. 【0532】 An "information collection" refers to the data and knowledge base accumulated within a system, and is a group of information that is subject to retrieval and analysis. 【0533】 A "search method" is a method for examining an information set using analyzed information and obtaining relevant data. 【0534】 A "generation method" is a method that has the function of creating and diversifying solutions that are tailored to the user's emotional state based on acquired information. 【0535】 "Information sources" refer to data and links that are referenced to support the reliability of the generated solution. 【0536】 "Display means" refers to a method of outputting the final generated solution to the user's terminal, making it viewable and operable for the user. 【0537】 This system aims to provide solutions to questions entered by users via a terminal. The system incorporates natural language processing technology with an emotion engine, which analyzes the user's emotional state to generate more appropriate responses. 【0538】 The terminal receives a question from the user as input and sends this question to the server. The emotion engine used here utilizes a natural language processing model to analyze the linguistic expressions and context that appear in the user's input. This allows it to recognize the user's emotional state and send corresponding data to the server. 【0539】 The server searches the information set based on the results of sentiment analysis. In this process, it uses natural language processing techniques to identify important keywords and optimize the information. The generative AI model used by the server has the ability to provide flexible solutions for diverse inputs. 【0540】 For example, a prompt such as "The user's emotion is 'anxiety,' what is the best suggestion to offer as a solution?" is passed to the AI ​​model. This prompt allows the AI ​​model to quickly generate a solution. 【0541】 For example, if a user inputs a concern such as "I've been so busy with work lately that I don't have time to exercise," the system could recognize emotions like "busyness" and "frustration" and suggest short exercises that can be done during work breaks. 【0542】 As described above, the present invention makes it possible to perform flexible information processing in response to the user's emotions and improve the user experience. 【0543】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0544】 Step 1: 【0545】 The user uses a terminal to input a question seeking a solution. The terminal receives this input and invokes the sentiment engine to begin analysis. It extracts linguistic expressions and context from the input text and processes sentiment information as data points. The output generates tags indicating the user's emotional state. 【0546】 Step 2: 【0547】 The terminal sends question data along with emotional information to the server. The server receives this input, analyzes the text using natural language processing tools, and extracts important keywords. This determines the core of the input text. As output, a set of the analyzed keywords is generated. 【0548】 Step 3: 【0549】 The server searches a data set based on keywords and sentiment information. This includes filtering and ranking text data. Information relevant to the emotional state is identified, and sentiment-based responses are optimized. The output provides relevant information and potential solutions tailored to the user's emotions. 【0550】 Step 4: 【0551】 The server uses a generative AI model to generate specific solutions. Example-based instructions are passed to the model as prompts. For example, a prompt might be, "The user's emotion is 'anxiety,' what solution would you recommend?" The generated solutions are diversified and adjusted to best address the user's emotion. 【0552】 Step 5: 【0553】 The server uses reference tagging to attach information sources and identifiers related to the proposed solution. To enhance reliability, relevant links are also attached to the solution. Finally, the polished solution is provided to the user. 【0554】 Step 6: 【0555】 The terminal receives solutions sent from the server and displays them on the screen in a format viewable by the user. Based on this information, the user can choose the option that is right for them and take action. This entire process allows the user to solve problems efficiently. 【0556】 (Application Example 2) 【0557】 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." 【0558】 Traditional systems provided mechanical responses without considering user emotions, resulting in a lack of user experience and reduced effectiveness in problem-solving. Furthermore, standard solutions were often unsatisfactory, especially in situations where emotional support was required. 【0559】 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. 【0560】 In this invention, the server includes a natural language processing means for processing information received from a user terminal, a search means for searching an information database based on keywords extracted by the natural language processing means, an emotion recognition means for analyzing the user's emotions, and a generation means for generating multiple solutions based on the search results and the user's emotional state. This makes it possible to present flexible and appropriate solutions that are in line with the user's emotional state. 【0561】 A "user terminal" is an electronic device used by a user to input and receive information. 【0562】 "Natural language processing means" refers to the process of analyzing text input by a user and extracting important keywords. 【0563】 A "keyword" is a word that is important for information retrieval, extracted from the user's input. 【0564】 An "information database" is a collection of data that stores information related to a user's question. 【0565】 "Search method" refers to the process of searching an information database using keywords and obtaining relevant information. 【0566】 "User emotions" refer to the psychological state that a user expresses when entering information. 【0567】 "Emotion recognition means" refers to the process of analyzing a user's emotions from their text input. 【0568】 "Generation method" refers to the process of creating multiple solutions based on search results and sentiment information. 【0569】 A "solution" is a suggestion or method offered to address a user's questions or problems. 【0570】 The system implementing this invention mainly consists of a server and a user terminal. The user inputs questions or problems through the terminal, and this information is processed by sending it to the server. 【0571】 The server first analyzes user input using natural language processing techniques and extracts important keywords. Specifically, the Python library spaCy is used for natural language processing. Additionally, TextBlob is used to analyze the emotions contained in the user's input. This emotion analysis enables processing that takes the user's psychological state into account. 【0572】 Next, the information database is searched based on the extracted keywords to obtain possible solutions. An SQLite database is used for the search, efficiently filtering relevant information. 【0573】 The server generates the optimal solution based on the search results and the user's emotional state. Relevant information sources are added to the generated solution to enhance its reliability, and it is then sent to the user's terminal. 【0574】 The user device displays the provided solutions to the user, allowing them to easily select an option that suits their emotional state. The front-end of the device is typically built using a framework such as React Native. 【0575】 For example, if a user enters a problem such as "payment failed," the server recognizes the emotion of "confusion" and proposes a solution that prioritizes speed. In this way, users can resolve their problems efficiently while minimizing stress. 【0576】 Examples of prompts to input into a generative AI model include the following: 【0577】 "Analyze the user's emotional state from their input text and propose a solution based on that emotion. Input text: I'm having trouble with my payment." 【0578】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0579】 Step 1: 【0580】 The user uses a terminal to input questions or problems in natural language. The entered text is received by the terminal's interface and sent to the server. The input here is a specific problem statement, such as "the payment didn't go through." 【0581】 Step 2: 【0582】 The server analyzes the received input text using natural language processing techniques. In this step, spaCy is used to extract key keywords from the text. The input is text from the user, and the output is a list of extracted keywords. This process identifies important information. 【0583】 Step 3: 【0584】 Next, the server uses TextBlob to analyze the user's text to determine their emotional state. Raw text from the user is provided as input, and an emotional assessment result is generated as output. This emotional information is then used in subsequent processing to optimize the solution. 【0585】 Step 4: 【0586】 Based on the extracted keywords and sentiment information, the server performs a search on the information database. Using an SQLite database, it retrieves highly relevant information and solutions. The input is a list of keywords and sentiment information, and the output is a set of records of related data. 【0587】 Step 5: 【0588】 The server uses search results and sentiment information to generate multiple optimal solutions. In this step, a generative AI model is used to create solutions and add relevant information sources. The input is search results and sentiment information, and the output is a list of solutions. 【0589】 Step 6: 【0590】 Finally, the generated solution is sent to the user's terminal and displayed to the user on the terminal. Here, a visualized solution is output to the interface for the user to easily review and take action. The displayed information includes steps for the solution and relevant links. 【0591】 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. 【0592】 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. 【0593】 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. 【0594】 [Fourth Embodiment] 【0595】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0596】 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. 【0597】 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). 【0598】 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. 【0599】 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. 【0600】 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). 【0601】 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. 【0602】 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. 【0603】 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. 【0604】 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. 【0605】 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. 【0606】 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. 【0607】 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". 【0608】 This invention is a system for rapidly providing solutions from diverse information sources within a company, and aims to improve efficiency throughout the stages of information collection, processing, and presentation. The program is implemented using the following methods. 【0609】 First, the user enters their question in text format using their device. The device sends the input data to the server, which then analyzes this data using natural language processing. This allows important keywords and intent to be extracted from the text. For example, if a question such as "How to deal with a defect in a new product" is entered, keywords such as "new product," "defect," and "solution" are recognized as important. 【0610】 The server searches the information database based on the extracted keywords. The search method allows for the rapid identification of relevant documents and past cases. In this step, past cases where similar problems were solved and standard troubleshooting procedures described in the product manual are referenced. 【0611】 Next, the server generates multiple solutions based on the search results. The generation method constructs candidates based on different perspectives and methods, providing the user with choices. For example, it can offer both immediate solutions and long-term solutions. 【0612】 Furthermore, the server adds references such as links and document numbers to the sources of information corresponding to the generated solutions. This allows users to verify the reliability of the presented solutions and refer to further details as needed. 【0613】 Finally, the server returns the generated solution to the user's terminal, which displays it in an easy-to-understand format. The user can then use this information to select the appropriate action to take. This consistent process enables faster and more reliable work execution than ever before. 【0614】 This system is designed to support user decision-making by utilizing diverse information. Specifically, it can be applied to various tasks, including not only troubleshooting but also planning new projects and considering customer response strategies. 【0615】 The following describes the processing flow. 【0616】 Step 1: 【0617】 The user enters their question into the terminal and presses the send button. The terminal then sends the entered question to the server. 【0618】 Step 2: 【0619】 The server passes the user's question to a natural language processing system for text analysis. This allows the server to extract important keywords and context. 【0620】 Step 3: 【0621】 The server searches the information database using the extracted keywords. It utilizes search methods to explore relevant documents and past cases, and identifies the corresponding data. 【0622】 Step 4: 【0623】 The server generates multiple solutions based on the information obtained from the search results. Here, it generates options by considering different perspectives and methods, providing a variety of solutions. 【0624】 Step 5: 【0625】 The server adds references to the relevant sources of information for each solution, including links and document numbers, so that users can later access more details. 【0626】 Step 6: 【0627】 The server sends the generated solutions and references to the user's terminal. The terminal displays them to the user in an easy-to-read format. The user can review the provided solutions and make decisions based on the useful information. 【0628】 (Example 1) 【0629】 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". 【0630】 In today's world, where the sheer volume and diversity of information is increasing, companies need systems to support information gathering, analysis, and decision-making in order to respond quickly and effectively to the problems they face. Current methods make this process cumbersome and time-consuming, which can lead to delays in decision-making and even incorrect judgments. 【0631】 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. 【0632】 In this invention, the server includes a natural language processing means for analyzing information received from a user terminal and extracting important terms, a search means for searching an information set based on the important terms, and a generation means for generating a number of solutions based on the search results. This makes it possible to answer user questions quickly and accurately. 【0633】 A "user terminal" is an electronic device used by a user to input information, and typically includes computers and smartphones. 【0634】 "Natural language processing means" refers to techniques or tools for analyzing input text information and extracting important words or phrases. 【0635】 "Search means" refers to a method or apparatus for searching an information set using extracted terms and obtaining related data. 【0636】 "Generative means" refers to a technology or process for creating and presenting multiple solutions based on search results. 【0637】 "Additional means" refers to a function or mechanism for adding relevant information to the generated solution. 【0638】 "Presentation means" refers to the means for displaying the solution generated on the user terminal, and generally, a display or graphical user interface is used. 【0639】 An "information collection" is a collection of information that is searched, and usually refers to a database system. 【0640】 A description of the embodiment for carrying out the invention will be provided. 【0641】 This invention is a system that provides solutions quickly from diverse information sources within a company. The system is mainly composed of user terminals and a server working together to efficiently collect, analyze, and present various types of information. 【0642】 The user first enters their question in text format using their device. This device is typically a personal computer or smartphone, and the user enters text using a keyboard or touchscreen. Once the user has finished entering the question, the device sends the information to the server. 【0643】 The server uses natural language processing (NLP) techniques to analyze the received information. Specifically, libraries such as Python's NLTK and spaCy are used as natural language processing tools. This allows for the extraction of important words and phrases from text data, enabling an accurate understanding of the user's intent. 【0644】 Next, the server uses the extracted keywords to search the information set. SQL databases or Elasticsearch are commonly used for this search. This allows for the efficient retrieval and reference of relevant documents and past case studies. 【0645】 Subsequently, the server generates numerous solutions using a generative AI model based on the search results. This process considers different perspectives and methods, resulting in a diverse range of solutions. In this process, the AI ​​model utilizes the input data to perform inference. 【0646】 Furthermore, the generated solutions are accompanied by references to relevant information. Specifically, links to information sources and document numbers are added to the solutions, making it easier for users to verify their reliability. 【0647】 Finally, the server sends the generated solution to the user's terminal. The terminal displays the received solution in an easy-to-understand format. This allows the user to make quick decisions based on the presented information. 【0648】 For example, when a user enters a prompt such as, "Please tell me about successful examples of new projects," the system will present relevant success stories that can be used as reference for project planning. 【0649】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0650】 Step 1: 【0651】 The user uses their device to input questions or problems they want to resolve as text. For example, they might enter a prompt such as "How to fix a bug in the new product." The entered text is then sent by the device to the server via a secure communication method (e.g., HTTPS). 【0652】 Step 2: 【0653】 The server analyzes the received text data using natural language processing (NLTK) tools. This analysis utilizes libraries such as NLTK and spaCy. The text is tokenized, parts of speech are tagged, and important words are extracted from the results. It takes raw text as input and generates extracted keywords as output. 【0654】 Step 3: 【0655】 The server uses the extracted keywords to search the information set. The search process utilizes systems such as SQL databases or Elasticsearch to efficiently identify relevant documents and past cases. The input is keywords, and the output is a set of related data entries. 【0656】 Step 4: 【0657】 The server generates solutions using a generative AI model based on the information obtained through the search. The AI ​​model takes the search results as input and outputs multiple solutions that consider different perspectives and methods. Specifically, these include methods for quick responses as well as solutions from a long-term perspective. 【0658】 Step 5: 【0659】 The server adds references to relevant information for each generated solution. By adding links to information sources and document IDs to the solution, users can later access more detailed information. The input is the solution proposal, and the output is the solution with the added references. 【0660】 Step 6: 【0661】 The server sends the final solution to the user's terminal. The terminal then displays the received solution, using a graphical user interface or similar method to present it clearly. This allows the user to make quick decisions based on the presented information. The output to the terminal is formatted solution information. 【0662】 (Application Example 1) 【0663】 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". 【0664】 With the proliferation of visual information devices, users have a growing need to quickly obtain personalized information from diverse content. However, it is difficult for users to select appropriate content from a large amount of information through visual means, and there is a need for a means to obtain personalized recommendations efficiently and effectively. Conventional technologies have the challenge of not being able to provide recommendations that fully utilize the user's preferences and past viewing history. 【0665】 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. 【0666】 In this invention, the server includes information processing means for processing information received from a user terminal, search means for searching an information aggregate based on concepts extracted by the information processing means, and generation means for generating a variety of options based on the search results. This enables the user to quickly obtain personalized recommendation information and select appropriate content through a visual information device. 【0667】 "Information processing means" refers to a device or process that analyzes information received from a user terminal and extracts important concepts. 【0668】 An "information aggregate" refers to a data storage or database that aggregates and holds related information. 【0669】 A "search method" is a device or process for quickly identifying relevant information within an information aggregate. 【0670】 "Generation means" refers to a device or process for constructing and providing diverse options based on search results. 【0671】 "Recording addition means" refers to a device or process for adding information sources related to the generated choices. 【0672】 "Display means" refers to a device or process that visually presents the generated options to the user. 【0673】 "Adaptive presentation means" refers to a function or process that adaptively displays personalized recommendation information to the user based on the content. 【0674】 This system consists of a user terminal, a server, and an information aggregate. Since the user processes information visually through a head-mounted display, the user first inputs questions related to the visual content into the terminal using a catalyst. 【0675】 The server analyzes user input using information processing tools and extracts important concepts. It uses spaCy, a natural language processing library, to identify meaningful keywords from the input text. In this process, the server manipulates the database using MySQL to efficiently search for relevant information within the data collection. 【0676】 Based on search results, the server utilizes Scikit-learn to generate personalized recommendations, creating a diverse range of options. This enables adaptive information presentation tailored to the user's preferences and past viewing history. The generated information is further enhanced with additional information through a recording mechanism to increase its reliability. The server then visualizes the generated options using an adaptive presentation mechanism, displaying appropriate information in real time to guide the user. 【0677】 For example, if a user enters "I want to experience the latest sci-fi movie," the system will identify relevant VR content and provide descriptions and recommendations based on its characteristics. An example of a prompt is shown below: "(Request) Action-packed VR content related to sports." 【0678】 In this way, users can enjoy real-time access to information tailored to their needs. 【0679】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0680】 Step 1: 【0681】 The user wears a head-mounted display and inputs questions related to visual content into a terminal. The user's questions are sent to the terminal as input data. 【0682】 Step 2: 【0683】 The terminal sends the entered question to the server. The server uses a natural language processing library (spaCy) to extract important keywords from the question. For example, in the input "I want to experience the latest sci-fi movie," keywords such as "latest," "sci-fi movie," and "experience" are extracted. This is the input to the server, and the extracted keywords become the output for the next processing step. 【0684】 Step 3: 【0685】 The server searches the information repository based on the extracted keywords. It uses MySQL to quickly query the database and identify relevant VR content information and data points. Keywords are input, and related information is output. 【0686】 Step 4: 【0687】 The server uses Scikit-learn to execute a content recommendation algorithm based on the data obtained from the search, taking into account the user's preferences and past viewing history. This algorithm generates personalized recommendation information. The search results are the input, and the recommended content is the output. 【0688】 Step 5: 【0689】 To ensure the reliability of the generated recommendations, the server uses recording mechanisms to add information such as the history of the information source. The recommendation information is the input, and the recorded recommendation information is the output. 【0690】 Step 6: 【0691】 The server uses adaptive presentation means to send recommended information to the user's terminal. The head-mounted display visually presents this information to the user, allowing the user to select appropriate content based on that information. The user visually receives the recommended content via the terminal. The adaptively presented information is the input, and the presentation is the output. 【0692】 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. 【0693】 This invention combines an emotion engine with a system that processes user questions and provides solutions, thereby achieving flexible responses based on the user's emotional state. The following specifically describes an embodiment of the system, including the functions of each component. 【0694】 The user uses the terminal to input a question seeking a solution. In the process of sending this to the server, the terminal invokes an emotion engine, which analyzes the linguistic expressions and context of the user's input to recognize their emotional state. For example, if the user inputs "I'm really troubled by this problem," the system will determine that the user is experiencing emotions such as "confusion" or "anxiety." 【0695】 The server uses natural language processing to analyze important keywords from the received information and searches the information database. In addition to normal information retrieval, the search method optimizes the results by considering emotional information provided by the emotion engine. This allows for the generation of solutions that are appropriate to the user's emotions, even for the same question. 【0696】 The generation mechanism creates solutions tailored to the user's emotional state and presents different methods depending on the situation. For example, if the user indicates "anxiety," the system prioritizes providing solutions that can be implemented quickly. 【0697】 Subsequently, a reference tagging mechanism adds links and identifiers to information sources related to the generated solution, enhancing its reliability and transparency. In addition, relevant reference information suitable for emotionally responsive solutions is also attached, providing users with more options. 【0698】 Ultimately, the server sends data to the terminal to present these solutions, which are then displayed on the terminal. Users can easily review the presented solutions and choose the option that best suits their emotional needs. This process results in a more pleasant user experience and improves the speed and effectiveness of problem-solving. 【0699】 The following describes the processing flow. 【0700】 Step 1: 【0701】 The user enters the question they want answered into the device and sends it. The device receives the input and activates the emotion engine. 【0702】 Step 2: 【0703】 The device passes the input text to an emotion engine, which analyzes the wording and context within the text to recognize the user's emotional state. The emotion engine identifies emotions such as "confusion," "anxiety," and "calmness." 【0704】 Step 3: 【0705】 The terminal sends data to the server, including user input and recognized sentiment information. This prepares the server to process the data based on both sentiment and text. 【0706】 Step 4: 【0707】 The server analyzes the received data using natural language processing techniques and extracts important keywords from the input text. Simultaneously, it continues to refer to sentiment information. 【0708】 Step 5: 【0709】 The server uses extracted keywords and sentiment information to search the information database. The search method adjusts the search criteria according to the sentiment, prioritizing the search for information that best corresponds to the user's emotions. 【0710】 Step 6: 【0711】 The server generates multiple solutions based on the search results. The generation method incorporates emotional information and adapts the ranking and presentation of options to the user's emotions. For example, if the user is highly anxious, it will emphasize quick solutions. 【0712】 Step 7: 【0713】 The server adds information sources related to the generated solution using a reference tagging mechanism. This ensures that users can verify the accuracy of the solution. 【0714】 Step 8: 【0715】 The server sends data to the terminal, including the final solution and its references. The terminal displays this information visually to the user, allowing the user to select the best solution from the displayed options. 【0716】 (Example 2) 【0717】 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". 【0718】 Traditional systems that provide uniform responses or solutions to user inquiries without considering their emotional state struggle to offer optimal solutions and improve the user experience. This leads to decreased problem-solving efficiency and difficulty in achieving sufficient user satisfaction. 【0719】 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. 【0720】 In this invention, the server includes emotion analysis means for recognizing emotional states, natural language processing means for analyzing and retrieving information, and means for optimizing and generating solutions based on emotions. This enables the provision of flexible and appropriate solutions that respond to the user's emotions. 【0721】 "User input" refers to information or questions that a user sends to the system via a terminal. 【0722】 "Sentiment analysis tools" are methods and technologies for recognizing emotional states by analyzing linguistic expressions and context contained in user input. 【0723】 "Information processing means" refers to a method for analyzing input information using natural language processing technology and identifying important elements. 【0724】 An "information collection" refers to the data and knowledge base accumulated within a system, and is a group of information that is subject to retrieval and analysis. 【0725】 A "search method" is a method for examining an information set using analyzed information and obtaining relevant data. 【0726】 A "generation method" is a method that has the function of creating and diversifying solutions that are tailored to the user's emotional state based on acquired information. 【0727】 "Information sources" refer to data and links that are referenced to support the reliability of the generated solution. 【0728】 "Display means" refers to a method of outputting the final generated solution to the user's terminal, making it viewable and operable for the user. 【0729】 This system aims to provide solutions to questions entered by users via a terminal. The system incorporates natural language processing technology with an emotion engine, which analyzes the user's emotional state to generate more appropriate responses. 【0730】 The terminal receives a question from the user as input and sends this question to the server. The emotion engine used here utilizes a natural language processing model to analyze the linguistic expressions and context that appear in the user's input. This allows it to recognize the user's emotional state and send corresponding data to the server. 【0731】 The server searches the information set based on the results of sentiment analysis. In this process, it uses natural language processing techniques to identify important keywords and optimize the information. The generative AI model used by the server has the ability to provide flexible solutions for diverse inputs. 【0732】 For example, a prompt such as "The user's emotion is 'anxiety,' what is the best suggestion to offer as a solution?" is passed to the AI ​​model. This prompt allows the AI ​​model to quickly generate a solution. 【0733】 For example, if a user inputs a concern such as "I've been so busy with work lately that I don't have time to exercise," the system could recognize emotions like "busyness" and "frustration" and suggest short exercises that can be done during work breaks. 【0734】 As described above, the present invention makes it possible to perform flexible information processing in response to the user's emotions and improve the user experience. 【0735】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0736】 Step 1: 【0737】 The user uses a terminal to input a question seeking a solution. The terminal receives this input and invokes the sentiment engine to begin analysis. It extracts linguistic expressions and context from the input text and processes sentiment information as data points. The output generates tags indicating the user's emotional state. 【0738】 Step 2: 【0739】 The terminal sends question data along with emotional information to the server. The server receives this input, analyzes the text using natural language processing tools, and extracts important keywords. This determines the core of the input text. As output, a set of the analyzed keywords is generated. 【0740】 Step 3: 【0741】 The server searches a data set based on keywords and sentiment information. This includes filtering and ranking text data. Information relevant to the emotional state is identified, and sentiment-based responses are optimized. The output provides relevant information and potential solutions tailored to the user's emotions. 【0742】 Step 4: 【0743】 The server uses a generative AI model to generate specific solutions. Example-based instructions are passed to the model as prompts. For example, a prompt might be, "The user's emotion is 'anxiety,' what solution would you recommend?" The generated solutions are diversified and adjusted to best address the user's emotion. 【0744】 Step 5: 【0745】 The server uses reference tagging to attach information sources and identifiers related to the proposed solution. To enhance reliability, relevant links are also attached to the solution. Finally, the polished solution is provided to the user. 【0746】 Step 6: 【0747】 The terminal receives solutions sent from the server and displays them on the screen in a format viewable by the user. Based on this information, the user can choose the option that is right for them and take action. This entire process allows the user to solve problems efficiently. 【0748】 (Application Example 2) 【0749】 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". 【0750】 Traditional systems provided mechanical responses without considering user emotions, resulting in a lack of user experience and reduced effectiveness in problem-solving. Furthermore, standard solutions were often unsatisfactory, especially in situations where emotional support was required. 【0751】 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. 【0752】 In this invention, the server includes a natural language processing means for processing information received from a user terminal, a search means for searching an information database based on keywords extracted by the natural language processing means, an emotion recognition means for analyzing the user's emotions, and a generation means for generating multiple solutions based on the search results and the user's emotional state. This makes it possible to present flexible and appropriate solutions that are in line with the user's emotional state. 【0753】 A "user terminal" is an electronic device used by a user to input and receive information. 【0754】 "Natural language processing means" refers to the process of analyzing text input by a user and extracting important keywords. 【0755】 A "keyword" is a word that is important for information retrieval, extracted from the user's input. 【0756】 An "information database" is a collection of data that stores information related to a user's question. 【0757】 "Search method" refers to the process of searching an information database using keywords and obtaining relevant information. 【0758】 "User emotions" refer to the psychological state that a user expresses when entering information. 【0759】 "Emotion recognition means" refers to the process of analyzing a user's emotions from their text input. 【0760】 "Generation method" refers to the process of creating multiple solutions based on search results and sentiment information. 【0761】 A "solution" is a suggestion or method offered to address a user's questions or problems. 【0762】 The system implementing this invention mainly consists of a server and a user terminal. The user inputs questions or problems through the terminal, and this information is processed by sending it to the server. 【0763】 The server first analyzes user input using natural language processing techniques and extracts important keywords. Specifically, the Python library spaCy is used for natural language processing. Additionally, TextBlob is used to analyze the emotions contained in the user's input. This emotion analysis enables processing that takes the user's psychological state into account. 【0764】 Next, the information database is searched based on the extracted keywords to obtain possible solutions. An SQLite database is used for the search, efficiently filtering relevant information. 【0765】 The server generates the optimal solution based on the search results and the user's emotional state. Relevant information sources are added to the generated solution to enhance its reliability, and it is then sent to the user's terminal. 【0766】 The user device displays the provided solutions to the user, allowing them to easily select an option that suits their emotional state. The front-end of the device is typically built using a framework such as React Native. 【0767】 For example, if a user enters a problem such as "payment failed," the server recognizes the emotion of "confusion" and proposes a solution that prioritizes speed. In this way, users can resolve their problems efficiently while minimizing stress. 【0768】 Examples of prompts to input into a generative AI model include the following: 【0769】 "Analyze the user's emotional state from their input text and propose a solution based on that emotion. Input text: I'm having trouble with my payment." 【0770】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0771】 Step 1: 【0772】 The user uses a terminal to input questions or problems in natural language. The entered text is received by the terminal's interface and sent to the server. The input here is a specific problem statement, such as "the payment didn't go through." 【0773】 Step 2: 【0774】 The server analyzes the received input text using natural language processing techniques. In this step, spaCy is used to extract key keywords from the text. The input is text from the user, and the output is a list of extracted keywords. This process identifies important information. 【0775】 Step 3: 【0776】 Next, the server uses TextBlob to analyze the user's text to determine their emotional state. Raw text from the user is provided as input, and an emotional assessment result is generated as output. This emotional information is then used in subsequent processing to optimize the solution. 【0777】 Step 4: 【0778】 Based on the extracted keywords and sentiment information, the server performs a search on the information database. Using an SQLite database, it retrieves highly relevant information and solutions. The input is a list of keywords and sentiment information, and the output is a set of records of related data. 【0779】 Step 5: 【0780】 The server uses search results and sentiment information to generate multiple optimal solutions. In this step, a generative AI model is used to create solutions and add relevant information sources. The input is search results and sentiment information, and the output is a list of solutions. 【0781】 Step 6: 【0782】 Finally, the generated solution is sent to the user's terminal and displayed to the user on the terminal. Here, a visualized solution is output to the interface for the user to easily review and take action. The displayed information includes steps for the solution and relevant links. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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." 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 The following is further disclosed regarding the embodiments described above. 【0805】 (Claim 1) 【0806】 A natural language processing means for processing information received from a user terminal, 【0807】 A search means for searching an information database based on keywords extracted by the natural language processing means, 【0808】 A generation means for generating multiple solutions based on search results, 【0809】 A reference attachment means for attaching information sources related to the generated solution, 【0810】 A display means for displaying the generated solutions on the user terminal, 【0811】 A system that includes this. 【0812】 (Claim 2) 【0813】 The system according to claim 1, further comprising means for receiving a question input from a user and automatically generating a response to said question. 【0814】 (Claim 3) 【0815】 The system according to claim 1, further comprising means of using different search algorithms to ensure diversity of the generated solutions. 【0816】 "Example 1" 【0817】 (Claim 1) 【0818】 A natural language processing means that analyzes information received from a user terminal and extracts important words and phrases, 【0819】 A search method for searching an information set based on the relevant important phrase, 【0820】 A generation method for generating numerous solutions based on search results, 【0821】 An additional means for adding references to information sources related to the generated solution, 【0822】 A presentation means for presenting the generated solution to the user terminal, 【0823】 A system that includes this. 【0824】 (Claim 2) 【0825】 The system according to claim 1, further comprising means for receiving a question input from a user and automatically generating a response to that question. 【0826】 (Claim 3) 【0827】 The system according to claim 1, further comprising means of using different search techniques to improve the diversity of the generated solutions. 【0828】 "Application Example 1" 【0829】 (Claim 1) 【0830】 Information processing means for processing information received from a user terminal, 【0831】 A search means for searching an information aggregate based on the concepts extracted by the information processing means, 【0832】 A generation means for generating diverse options based on search results, 【0833】 A means for adding information sources related to the generated options, 【0834】 A display means for displaying the generated options on the user terminal, 【0835】 An adaptive presentation method that presents personalized recommendation information based on content, 【0836】 A system that includes this. 【0837】 (Claim 2) 【0838】 The system according to claim 1, further comprising means for receiving a request input from a user and automatically generating a response to said request. 【0839】 (Claim 3) 【0840】 The system according to claim 1, further comprising means of using different search methods to ensure diversity among the generated options. 【0841】 "Example 2 of combining an emotion engine" 【0842】 (Claim 1) 【0843】 A sentiment analysis tool that receives user input and recognizes the emotional state, 【0844】 An information processing means that analyzes emotional states and input information using natural language processing technology, 【0845】 A search method that searches an information set based on analyzed information and obtains optimized results based on emotion, 【0846】 Based on the acquired results, a means of generating solutions tailored to the emotional state and diversifying them, 【0847】 A means of adding relevant information sources to the solution and increasing its reliability, 【0848】 A display means that ultimately communicates the solution to the user terminal, 【0849】 A system that includes this. 【0850】 (Claim 2) 【0851】 The system according to claim 1, further comprising means for providing an automatically generated response in accordance with emotions. 【0852】 (Claim 3) 【0853】 The system according to claim 1, further comprising means of taking emotional information into consideration and using different search methods to ensure diversity of the generated solutions. 【0854】 "Application example 2 when combining with an emotional engine" 【0855】 (Claim 1) 【0856】 A natural language processing means for processing information received from a user terminal, 【0857】 A search means for searching an information database based on keywords extracted by the natural language processing means, 【0858】 A generation means for generating multiple solutions based on search results and the user's emotional state, 【0859】 A reliability enhancement means that adds information sources related to the generated solution, 【0860】 A visualization means for displaying the generated solutions on the user's terminal, 【0861】 A system that includes emotion recognition means for analyzing the user's emotions. 【0862】 (Claim 2) 【0863】 The system according to claim 1, further comprising a process for receiving a question input from a user and automatically generating a response to the question according to the user's emotional state. 【0864】 (Claim 3) 【0865】 The system according to claim 1, further comprising a process that uses different search algorithms and sentiment data to ensure diversity and sentiment relevance of the generated solutions. [Explanation of Symbols] 【0866】 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] A natural language processing means for processing information received from a user terminal, A search means for searching an information database based on keywords extracted by the natural language processing means, A generation means for generating multiple solutions based on search results, A reference attachment means for attaching information sources related to the generated solution, A display means for displaying the generated solutions on the user terminal, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for receiving a question input from a user and automatically generating a response to said question. [Claim 3] The system according to claim 1, further comprising means of using different search algorithms to ensure diversity among the generated solutions.

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