system

Abstract

We provide the system. [Solution] A means of receiving user requests and analyzing them using natural language processing, Means for obtaining necessary information based on the analyzed results, A means of organizing and presenting information in a way that is optimal for the user, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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】 There is a problem that it is difficult for users to obtain information on regional information and public services quickly and accurately. In particular, this applies to the elderly, people who are not familiar with digital technology, and individuals who are busy at work or home. As a result, residents need to spend time and effort searching for information, and the problem is that they become passive in participating in community activities. 【Means for Solving the Problems】 【0005】 The system of this invention provides a means for analyzing user requests using natural language processing and quickly obtaining necessary information. Furthermore, it streamlines user information gathering by personalizing information according to individual needs and presenting it in an optimal format. In addition, by incorporating a function to acquire external data in real time and provide the latest information, it aims to improve the quality of life for local residents. 【0006】 "Users" refer to local residents and individuals who use this system. 【0007】 A "request" refers to a question or request that a user enters into this system. 【0008】 "Natural language processing" is a technology that enables computers to analyze and understand everyday human language. 【0009】 "Analysis" is the process of breaking down a request and understanding its meaning and the information it contains. 【0010】 "Information" refers to data that users need, such as local public services, events, and medical facility information. 【0011】 "Acquisition" refers to the act of gathering information from databases or external sources. 【0012】 "Personalization" means optimizing information according to the individual user's needs and history. 【0013】 "Real-time" refers to the immediate acquisition and provision of information that is currently in progress. 【0014】 "External data" refers to information obtained from data sources outside of this system. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, let's explain the terminology used in the following explanation. 【0018】 In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit). 【0019】 In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor. 【0020】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0029】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 This invention's system uses AI technology to support local residents in addressing various life challenges they face. Primarily, the system involves interaction between a server, a terminal, and a user, who then provide information. 【0037】 Users access the system through a terminal and enter requests regarding their needs. The terminal sends these requests to the server. The server performs natural language processing on the received requests and parses their content. Based on the analysis, the server retrieves the necessary information from the database and external data sources. 【0038】 Once information is collected, the server personalizes it by referencing the user's past usage history and settings. It utilizes machine learning algorithms to extract and organize information deemed most beneficial to the user. The server then reconstructs the information in a user-friendly format and sends the generated response to the device. 【0039】 As a concrete example, consider a scenario where a user wants to participate in a local event. When the user types "Tell me about local events this weekend," the server accesses a database of local events and retrieves relevant event information. Then, based on the user's interests and past participation history, it selects the most relevant events and returns detailed information as a response. This process allows the user to obtain the necessary information quickly and efficiently. 【0040】 Each distinctive feature of this system is designed to improve user convenience, and is particularly easy to use for the elderly and users unfamiliar with digital technology. This will improve information access throughout the community and enhance the quality of life for residents. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user operates the terminal to access the system interface and enter a request based on their needs. The terminal holds the entered request and prepares it for transmission. 【0044】 Step 2: 【0045】 The terminal sends a request from the user to the server. The server receives this request and begins preparing for analysis. 【0046】 Step 3: 【0047】 The server uses natural language processing to analyze the intent of incoming requests. This analysis includes extracting keywords and important information from the requests. 【0048】 Step 4: 【0049】 The server accesses an internal database based on the analysis results to search for relevant information. If necessary, it also retrieves information from external APIs and data sources. 【0050】 Step 5: 【0051】 The server references the user's past history and settings, and personalizes the collected information. Machine learning algorithms are used here to select the information that is most relevant to the user. 【0052】 Step 6: 【0053】 The server generates responses to the user based on personalized information. It uses natural language generation technology to construct easily understandable text. 【0054】 Step 7: 【0055】 The server sends the generated response to the terminal. The terminal receives this response and displays it in the user interface. 【0056】 Step 8: 【0057】 The user reviews the information on their device screen and considers the next action as needed. If they have further questions, they can repeat the process from step 1 to obtain more information. 【0058】 (Example 1) 【0059】 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." 【0060】 In modern society, local residents face a variety of life challenges, and there is a need to respond to them quickly and efficiently. However, for people unfamiliar with technology and the elderly, it is difficult to collect necessary information and obtain it in an easily understandable format. In particular, providing personalized information and obtaining real-time information presents a problem because it cannot be easily achieved without specialized knowledge. 【0061】 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. 【0062】 In this invention, the server includes means for receiving user requests and analyzing them using language processing technology, means for obtaining necessary information based on the analysis results, and means for organizing and presenting the information in a format suitable for the user. This makes it possible for even users unfamiliar with technology to quickly and efficiently obtain personalized information and address local community issues. 【0063】 "Receiving user requests" refers to the process of appropriately delivering the user's needs and questions to the server. 【0064】 "Analyzing using language processing technology" refers to the technique of using natural language processing technology to analyze language data and understand the user's intent. 【0065】 "Acquiring necessary information" refers to the process of collecting the required data from databases or external sources based on the analysis results. 【0066】 "Organizing and presenting information in a way that is suitable for the user" is the process of organizing acquired information and providing it in a format that is easy for the user to understand. 【0067】 "Sending information to the server via the terminal" refers to the communication process of transferring a user's request from the terminal to the server. 【0068】 "Providing personalized information" refers to providing information that is individualized based on the user's past activities and settings, and that meets the needs of each user. 【0069】 "Using algorithms to select and reorganize information" refers to the process of using machine learning or other computational methods to select and reorganize collected information in a way that is most relevant to the user. 【0070】 This invention provides a system for solving the daily life problems of local residents. Users access the system using devices such as smartphones and personal computers. A dedicated application or web browser is installed on the user's device. Using this, the user inputs prompt messages related to specific needs. A concrete example of a prompt message is, "Tell me about local events this weekend." 【0071】 The terminal sends the user's prompt text to the server. The server parses the received prompt text using NLTK or spaCy, natural language processing (NLP) libraries implemented in Python. Through language analysis, the server understands the intent of the information the user is seeking. Based on the analysis, the server retrieves relevant information from a database or external data sources on the internet. MySQL® is used for database management in this process. 【0072】 The collected information is personalized on the server side using machine learning algorithms. Frameworks such as TENSORFLOW® are used here. This extracts the most relevant information based on the user's past usage history and settings, and reconstructs the information in a format suitable for the user. The reconstructed information is finally sent from the server to the terminal as an HTML or JSON response. As a result, the user can quickly and efficiently obtain the information they are looking for. 【0073】 This will provide an environment where even the elderly and those unfamiliar with technology can use this system to solve the life challenges they face. For example, by providing event information quickly based on the prompts mentioned above, information access in the community will improve, thereby enhancing the quality of life for residents. 【0074】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0075】 Step 1: 【0076】 Users use devices such as smartphones or personal computers to input prompt messages related to specific needs. An example input here is "Tell me about local events this weekend." This prompt message is entered as data that initiates the system's processing. 【0077】 Step 2: 【0078】 The terminal sends the entered prompt text to the server. The input is text data, and the transmitted data is transferred to the server via the HTTP protocol. This allows the server to recognize the user's request and prepare to begin the next processing step. 【0079】 Step 3: 【0080】 The server analyzes the received prompt message using a natural language processing library. Specifically, it analyzes the language data using tools such as Python's NLTK and spaCy to understand the user's intent. As a result of the analysis, categories and keywords of necessary information are extracted. 【0081】 Step 4: 【0082】 Based on the analysis results, the server retrieves relevant information from the database and external data sources. To do this, the server uses SQL queries to search the MySQL database and extract relevant event information. If external data is required, additional data is obtained via APIs or web scraping. 【0083】 Step 5: 【0084】 The server uses machine learning algorithms to personalize the collected information. It considers the user's past history and settings, utilizing tools like TensorFlow, to select the most relevant information. This results in information optimized for the user. 【0085】 Step 6: 【0086】 The server reconfigures the selected information into a user-friendly format. The reconfigured data is then generated as a response in HTML or JSON format. This response is visually organized and easy for users to access and retrieve information. 【0087】 Step 7: 【0088】 The terminal displays the response received from the server. The response data is immediately displayed on the terminal's screen, allowing the user to quickly find the information they need. This makes it easy for users to obtain the data they require and get information to support their life decisions. 【0089】 (Application Example 1) 【0090】 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." 【0091】 A challenge exists in that local residents often have difficulty efficiently obtaining the information they need in their daily lives. In particular, accessing information is often complex for the elderly and those unfamiliar with digital technology, making quick decision-making difficult. Therefore, there is a need for a system that allows residents to easily obtain real-time, personalized information. 【0092】 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. 【0093】 In this invention, the server includes means for receiving user requests and analyzing them using natural language processing, means for obtaining necessary information based on the analysis results, means for organizing and presenting the information in a format optimal for the user, means for receiving information through voice input and converting it to text using speech recognition technology, and means for providing the obtained information in real time, either by voice or visually. This enables residents to quickly and efficiently access information necessary for their lives and to make decisions smoothly. 【0094】 A "request" is an information request or inquiry issued by a user. 【0095】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0096】 "Analysis" is the process of thoroughly examining given data or information to clarify its meaning and structure. 【0097】 "Information" refers to the data and knowledge that users need. 【0098】 "Organization" means summarizing and structuring the information obtained in an easy-to-understand way. 【0099】 "Presentation" means showing or providing information that is necessary for the user. 【0100】 "Voice input" refers to the process of capturing the user's voice as digital data into the system. 【0101】 "Speech recognition technology" is a technology that converts speech into text. 【0102】 "Text" refers to data or sentences composed of characters. 【0103】 "Real-time" means processing or responding in a near-instantaneous time. 【0104】 The system that realizes this invention is a platform in which servers, terminals, and users interact with each other. This system is implemented using Python and JavaScript (registered trademark), and the Flask framework is used on the server side. The Hugging Face Transformers library is utilized for natural language processing. 【0105】 Users send requests via voice input from devices such as smartphones and smart glasses. This voice data is converted into text data using speech recognition technology such as the Google® Cloud Speech-to-Text API. The server then receives this text data and performs natural language processing using the Transformers library. Based on the parsed request, the server collects necessary information from databases and external APIs, and organizes and personalizes that information. The information is restructured depending on the user's settings and past history. The organized information is then presented to the user as a visual screen or audio output. 【0106】 A concrete application example would be a user asking their smartphone, "What's the weather like today, and what's the traffic information for my nearest station?" The server would analyze the request, instantly retrieve real-time weather forecasts and traffic information, and provide it to the user via voice. 【0107】 Another example of a prompt is, "Tell me about recommended local events I can attend this weekend." This prompt allows users to quickly obtain relevant event information. 【0108】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0109】 Step 1: 【0110】 The device receives voice input from the user. The user uses the microphone to make a voice request, such as, "Tell me today's weather and transportation information for the nearest station." The input is voice data, and the device records this voice in digital format. 【0111】 Step 2: 【0112】 The device converts voice data into text data. The voice input is sent to the Google Cloud Speech-to-Text API, where speech recognition technology is used to convert it into text. The output is the text "Tell me today's weather and transportation information for the nearest station." 【0113】 Step 3: 【0114】 The server receives text data and performs natural language processing. The server uses the Hugging Face Transformers library to parse the text data and understand the request content. The input is text data, and the output is structured information of the request content. 【0115】 Step 4: 【0116】 The server retrieves necessary information from databases and external APIs based on structured information. Based on the parsed request, it accesses weather forecast APIs and traffic information APIs to collect the relevant data. The input is structured information of the request content, and the output is the retrieved real-time weather forecast data and traffic information data. 【0117】 Step 5: 【0118】 The server personalizes and organizes the data it acquires for the user. The server references the user's settings and past history to reconstruct the acquired data in a way that is easy for the user to understand. The input consists of acquired data and user profile information, while the output is personalized information. 【0119】 Step 6: 【0120】 The server sends organized information to the terminal, which then presents the information visually or audibly. The terminal displays the received personalized information on its screen or communicates it to the user audibly using speech synthesis technology. The input is personalized information, and the output is visually or audibly displayed information. 【0121】 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. 【0122】 The system of the present invention, in order to provide multifaceted support for the user's life, is equipped with an emotion recognition function in addition to the conventional information provision function. The user inputs a request using a terminal. The terminal sends the request to the server. The server is equipped with an emotion engine and analyzes the user's emotions based on the content of the received request. 【0123】 The server first understands the content of the request through natural language processing. Then, it uses an emotion engine to analyze the emotional tone and nuances in the text to determine the user's current emotional state. This determination is crucial for making the response to the user more personalized. 【0124】 Based on the analysis results and emotional state, the server retrieves appropriate information from the database and external sources. The retrieved information is then personalized based on the user's past history and the results of the emotional analysis. 【0125】 Ultimately, the server organizes the information in the most appropriate way based on the user's emotions and generates a response using natural language generation technology. This response is then sent to the terminal and presented to the user. 【0126】 As a concrete example, let's assume a user is feeling stressed and is looking for events that can help them relax. When the user requests, "Tell me about events that can help me relax," the server uses an emotion engine to analyze the nuances of stress contained in the request. Based on this analysis, the server quickly and effectively satisfies the user's needs by providing information on relaxation events that are suitable for the user. 【0127】 This system aims to go beyond simply providing information and offer a more empathetic experience that takes into account the user's emotional state, by enabling emotion-based responses. As a result, it can further improve the quality of life for local residents and contribute to the revitalization of local communities. 【0128】 The following describes the processing flow. 【0129】 Step 1: 【0130】 The user accesses the system using a terminal and enters a voluntary request. The terminal prepares this information as a request to the system. 【0131】 Step 2: 【0132】 The terminal sends the user's request to the server. The server receives the request and prepares for the next processing step. 【0133】 Step 3: 【0134】 The server performs natural language processing to parse the request text. This analysis includes a process of extracting the gist of the request and important information. 【0135】 Step 4: 【0136】 The server uses an emotion engine to recognize the user's emotions from the request and evaluate their current emotional state. This enables the provision of information based on the user's emotions. 【0137】 Step 5: 【0138】 Based on the analysis results, the server retrieves necessary information from internal databases and external data sources. At this time, appropriate information selection is made based on emotional information. 【0139】 Step 6: 【0140】 The server references the user's past history and sentiment data to personalize the retrieved information. Machine learning algorithms are applied in this process. 【0141】 Step 7: 【0142】 The server uses natural language generation technology to create responses to the user based on personalized information. The information is organized according to the user's emotions, resulting in relatable content. 【0143】 Step 8: 【0144】 The server sends the generated response to the terminal. The terminal displays this response on its screen and presents it to the user. 【0145】 Step 9: 【0146】 The user reviews the provided information and decides on an action that suits their needs. If further information is needed, they can restart the process from step 1. 【0147】 (Example 2) 【0148】 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." 【0149】 Modern information providers often present information without considering the user's specific situation or emotional state, resulting in a problem where the information provided fails to adequately meet the user's true needs. Furthermore, they struggle to fully meet users' expectations for real-time, immediate information delivery. 【0150】 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. 【0151】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing technology, means for determining the user's emotional state based on the analysis results, and means for acquiring necessary data based on the determined emotional state. This enables the provision of more personalized information according to the user's emotions and enables the efficient provision of timely information. 【0152】 A "user" is an entity that uses a system to obtain information or input requests. 【0153】 A "request" is an instruction from a user to the system to obtain the information or action they need. 【0154】 "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and manipulate human language. 【0155】 "Analysis" is the process of breaking down information and understanding its meaning and intent. 【0156】 "Emotional state" refers to the emotional tone and nuances extracted from the user's request. 【0157】 "Data" refers to a collection of numbers, characters, symbols, and other elements used to represent specific information. 【0158】 "Acquisition" refers to the act of selecting necessary data and information and incorporating them into a system. 【0159】 "Generative technology" refers to the techniques that computers use to create natural-sounding text and information. 【0160】 "User's past usage history" refers to a record of information and actions recorded when a user uses the system. 【0161】 "Current data from external sources" refers to the latest data obtained from external information sources outside the system. 【0162】 This system provides information based on user input, and therefore basically operates with a configuration centered around terminals, servers, and communication networks. 【0163】 The user first uses their device to enter a request for specific information. For example, this might occur if the user types "Tell me about events where I can relax" into their device. This request is then sent to the server via the internet. 【0164】 The server is equipped with software that implements natural language processing technology. This technology analyzes requests sent by users and interprets what they mean. Furthermore, the server has an emotion engine that analyzes the user's emotional state based on the content of the request. Based on this emotional state, the server retrieves more appropriate data. 【0165】 The server quickly retrieves the necessary data through database access technologies and APIs. The retrieved data is then personalized, taking into account the user's past history and emotional state. Generative technologies are then used to organize the information into a user-friendly format. This information is finally transmitted to the terminal via the internet and presented to the user. 【0166】 As a concrete example, consider a scenario where a user requests, "Recommend some movies I can enjoy this weekend." The server analyzes this request using natural language processing technology, taking into account factors such as movie genre and time slot, and further understands the underlying emotions behind the request through an emotion engine. Based on the data obtained, the server selects the most suitable movie information for the user and returns a generated message to the device. A natural response such as, "Here's a recommended comedy movie to help you relax this weekend: XX," would be displayed. 【0167】 A concrete example of a prompt for a generative AI model might be, "If someone is on vacation and looking for a travel destination to relieve stress, what suggestions should I give them?" This prompt requests the AI ​​model to provide information based on user attributes and context. 【0168】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0169】 Step 1: 【0170】 The user enters a request for specific information into the terminal in text format. This request might be in the format of "Tell me about events that can help me relax." The entered text is sent to the server via the internet. 【0171】 Step 2: 【0172】 The server analyzes received requests using natural language processing techniques. The input is the request text from the user, and the output is structured data that captures the intent of the request. The server then performs morphological analysis and intent identification on this structured data. 【0173】 Step 3: 【0174】 The server analyzes the request content using an emotion engine to determine the user's emotional state. The input is structured data, and the output is emotional state data that includes emotional tone and nuances. The emotion engine determines emotions from keywords and phrases in the text. 【0175】 Step 4: 【0176】 The server retrieves necessary information from databases and external APIs based on emotional state data and request content. The input is emotional state and request content, and the output is information appropriate to those inputs. The server accesses databases using efficient queries and collects external information through public APIs. 【0177】 Step 5: 【0178】 The server personalizes the acquired information based on the user's past history and emotional state. The input is the acquired information, and the output is the personalized information. Here, specific patterns and trends from the user's history are taken into consideration, and the priority of the information is changed accordingly. 【0179】 Step 6: 【0180】 The server converts personalized information into an understandable format using natural language generation technology. The input is personalized information, and the output is text generated for the user. The server uses templates to create a natural sentence structure. 【0181】 Step 7: 【0182】 The server sends the final sentence to the terminal via the internet. The input is a sentence generated in natural language, and the output is a text response to the terminal. 【0183】 Step 8: 【0184】 The terminal displays the text response received from the server in the user interface. The input is the text received from the server, and the output is visually presented information. Through this, the user can verify the necessary information. 【0185】 (Application Example 2) 【0186】 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". 【0187】 For the elderly and those requiring care, it is essential to constantly monitor their emotional state and provide appropriate information and support accordingly. However, conventional information provision systems often fail to consider the user's emotions and remain uniform in their information delivery. As a result, necessary support and appropriate information are not provided in a timely manner, leading to decreased user satisfaction. 【0188】 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. 【0189】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing, means for obtaining necessary information based on the analysis results, and means for analyzing the user's emotions and personalizing the response based on those emotions. This makes it possible to provide personalized responses and information that take into account the user's emotional state. 【0190】 "Means of receiving user requests and analyzing them using natural language processing" refers to techniques that obtain inquiries and instructions from users and analyze text data in order to interpret them. 【0191】 "Means of obtaining necessary information based on the analyzed results" refers to techniques that retrieve relevant information from databases or external sources based on results obtained through natural language processing. 【0192】 "Means of organizing and presenting information in a way that is optimal for the user" refers to technologies that organize acquired information according to its purpose and situation, and present it in a way that is easy for the user to understand. 【0193】 "Means of analyzing user emotions and personalizing responses based on those emotions" refers to technology that identifies the user's emotional state and generates individually tailored responses based on the results of that identification. 【0194】 "A means of acquiring real-time data from external sources and providing users with immediate information and responses tailored to their emotions" refers to a technology that instantly incorporates the latest information from external sources and uses it to create immediate responses that match the user's emotions. 【0195】 This system has the ability to effectively analyze user requests and personalize responses by providing an interface equipped with emotion recognition technology. The system consists of a server with emotion recognition capabilities and a terminal that enables interaction with the user. The terminal receives a voice or text request from the user and initiates a series of processes. 【0196】 The server first uses a natural language processing engine to analyze the user's request. The software used here includes natural language processing libraries such as spaCy and NLTK. This allows the content of the text to be understood, and the next stage is sentiment analysis. The server's sentiment analysis engine uses tools such as the Google Cloud Natural Language API to analyze the user's emotional tone in detail. 【0197】 Once the user's emotions are identified, the server retrieves appropriate information from a database or external source based on the results. A system like MySQL can be used for database management. Next, a natural language generation engine, such as the OpenAI® GPT model, is used to generate a personalized response. This response is sent to the terminal and presented to the user in an appropriate format. 【0198】 As a concrete example, if an elderly person in a nursing home says, "I want to relax today," the device sends that request to the server. If the emotion analysis determines that the person is seeking relaxation, the server can generate suggestions for calming music or simple relaxation exercises and present them to the user. 【0199】 An example of a prompt for a generative AI model is, "If the user is seeking relaxation, generate personalized relaxation content." 【0200】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0201】 Step 1: 【0202】 The device receives a voice or text request from the user. The voice data obtained as input is converted into text data using speech recognition technology. The text obtained in this process forms the basis for the next analysis. 【0203】 Step 2: 【0204】 The server analyzes the text data using a natural language processing engine (e.g., spaCy or NLTK). Here, morphological and syntactic analysis are performed to analyze the meaning of the input text and understand its content. The server receives the output, which includes the text's structure and semantic information. 【0205】 Step 3: 【0206】 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to evaluate the emotional tone of the analyzed text. Emotion analysis analyzes language patterns and nuances to identify the user's emotional state (e.g., joy, sadness, stress). The result of the emotional state assessment is then output. 【0207】 Step 4: 【0208】 The server retrieves relevant information from databases and external sources based on the user's emotional state. Input includes the results of the emotional analysis and the user's profile information. Output provides relaxation content and suggestions tailored to the user. 【0209】 Step 5: 【0210】 The server uses a generative AI model (e.g., the OpenAI GPT model) to generate personalized responses. The input consists of acquired information and sentiment analysis results. The generative AI model uses this to create response text in natural language, providing specific suggestions and information as output. 【0211】 Step 6: 【0212】 The device presents the response sent from the server to the user. Presentation methods include text display, text-to-speech, and playback of visual content. This allows the user to receive information and suggestions optimized for their emotions. 【0213】 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. 【0214】 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. 【0215】 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. 【0216】 [Second Embodiment] 【0217】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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). 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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". 【0229】 This invention's system uses AI technology to support local residents in addressing various life challenges they face. Primarily, the system involves interaction between a server, a terminal, and a user, who then provide information. 【0230】 Users access the system through a terminal and enter requests regarding their needs. The terminal sends these requests to the server. The server performs natural language processing on the received requests and parses their content. Based on the analysis, the server retrieves the necessary information from the database and external data sources. 【0231】 Once information is collected, the server personalizes it by referencing the user's past usage history and settings. It utilizes machine learning algorithms to extract and organize information deemed most beneficial to the user. The server then reconstructs the information in a user-friendly format and sends the generated response to the device. 【0232】 As a concrete example, consider a scenario where a user wants to participate in a local event. When the user types "Tell me about local events this weekend," the server accesses a database of local events and retrieves relevant event information. Then, based on the user's interests and past participation history, it selects the most relevant events and returns detailed information as a response. This process allows the user to obtain the necessary information quickly and efficiently. 【0233】 Each distinctive feature of this system is designed to improve user convenience, and is particularly easy to use for the elderly and users unfamiliar with digital technology. This will improve information access throughout the community and enhance the quality of life for residents. 【0234】 The following describes the processing flow. 【0235】 Step 1: 【0236】 The user operates the terminal to access the system interface and enter a request based on their needs. The terminal holds the entered request and prepares it for transmission. 【0237】 Step 2: 【0238】 The terminal sends a request from the user to the server. The server receives this request and begins preparing for analysis. 【0239】 Step 3: 【0240】 The server uses natural language processing to analyze the intent of incoming requests. This analysis includes extracting keywords and important information from the requests. 【0241】 Step 4: 【0242】 The server accesses an internal database based on the analysis results to search for relevant information. If necessary, it also retrieves information from external APIs and data sources. 【0243】 Step 5: 【0244】 The server references the user's past history and settings, and personalizes the collected information. Machine learning algorithms are used here to select the information that is most relevant to the user. 【0245】 Step 6: 【0246】 The server generates responses to the user based on personalized information. It uses natural language generation technology to construct easily understandable text. 【0247】 Step 7: 【0248】 The server sends the generated response to the terminal. The terminal receives this response and displays it in the user interface. 【0249】 Step 8: 【0250】 The user reviews the information on their device screen and considers the next action as needed. If they have further questions, they can repeat the process from step 1 to obtain more information. 【0251】 (Example 1) 【0252】 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." 【0253】 In modern society, local residents face a variety of life challenges, and there is a need to respond to them quickly and efficiently. However, for people unfamiliar with technology and the elderly, it is difficult to collect necessary information and obtain it in an easily understandable format. In particular, providing personalized information and obtaining real-time information presents a problem because it cannot be easily achieved without specialized knowledge. 【0254】 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. 【0255】 In this invention, the server includes means for receiving user requests and analyzing them using language processing technology, means for obtaining necessary information based on the analysis results, and means for organizing and presenting the information in a format suitable for the user. This makes it possible for even users unfamiliar with technology to quickly and efficiently obtain personalized information and address local community issues. 【0256】 "Receiving user requests" refers to the process of appropriately delivering the user's needs and questions to the server. 【0257】 "Analyzing using language processing technology" refers to the technique of using natural language processing technology to analyze language data and understand the user's intent. 【0258】 "Acquiring necessary information" refers to the process of collecting the required data from databases or external sources based on the analysis results. 【0259】 "Organizing and presenting information in a way that is suitable for the user" is the process of organizing acquired information and providing it in a format that is easy for the user to understand. 【0260】 "Sending information to the server via the terminal" refers to the communication process of transferring a user's request from the terminal to the server. 【0261】 "Providing personalized information" refers to providing information that is individualized based on the user's past activities and settings, and that meets the needs of each user. 【0262】 "Using algorithms to select and reorganize information" refers to the process of using machine learning or other computational methods to select and reorganize collected information in a way that is most relevant to the user. 【0263】 This invention provides a system for solving the daily life problems of local residents. Users access the system using devices such as smartphones and personal computers. A dedicated application or web browser is installed on the user's device. Using this, the user inputs prompt messages related to specific needs. A concrete example of a prompt message is, "Tell me about local events this weekend." 【0264】 The terminal sends the user's prompt text to the server. The server parses the received prompt text using NLTK or spaCy, which are natural language processing (NLP) libraries implemented in Python. Through language analysis, the server understands the intent of the information the user is seeking. Based on the analysis, the server retrieves relevant information from a database or external data sources on the internet. MySQL is used for database management in this process. 【0265】 The collected information is personalized on the server side using machine learning algorithms. Frameworks such as TensorFlow are used here. This extracts the most relevant information based on the user's past usage history and settings, and reconstructs the information in a format suitable for the user. The reconstructed information is finally sent from the server to the terminal as an HTML or JSON response. As a result, the user can quickly and efficiently obtain the information they are looking for. 【0266】 This will provide an environment where even the elderly and those unfamiliar with technology can use this system to solve the life challenges they face. For example, by providing event information quickly based on the prompts mentioned above, information access in the community will improve, thereby enhancing the quality of life for residents. 【0267】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0268】 Step 1: 【0269】 Users use devices such as smartphones or personal computers to input prompt messages related to specific needs. An example input here is "Tell me about local events this weekend." This prompt message is entered as data that initiates the system's processing. 【0270】 Step 2: 【0271】 The terminal sends the entered prompt text to the server. The input is text data, and the transmitted data is transferred to the server via the HTTP protocol. This allows the server to recognize the user's request and prepare to begin the next processing step. 【0272】 Step 3: 【0273】 The server analyzes the received prompt message using a natural language processing library. Specifically, it analyzes the language data using tools such as Python's NLTK and spaCy to understand the user's intent. As a result of the analysis, categories and keywords of necessary information are extracted. 【0274】 Step 4: 【0275】 Based on the analysis results, the server retrieves relevant information from the database and external data sources. To do this, the server uses SQL queries to search the MySQL database and extract relevant event information. If external data is required, additional data is obtained via APIs or web scraping. 【0276】 Step 5: 【0277】 The server uses machine learning algorithms to personalize the collected information. It considers the user's past history and settings, utilizing tools like TensorFlow, to select the most relevant information. This results in information optimized for the user. 【0278】 Step 6: 【0279】 The server reconfigures the selected information into a user-friendly format. The reconfigured data is then generated as a response in HTML or JSON format. This response is visually organized and easy for users to access and retrieve information. 【0280】 Step 7: 【0281】 The terminal displays the response received from the server. The response data is immediately displayed on the terminal's screen, allowing the user to quickly find the information they need. This makes it easy for users to obtain the data they require and get information to support their life decisions. 【0282】 (Application Example 1) 【0283】 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." 【0284】 There is a problem that it is difficult for local residents to efficiently obtain the information necessary in their lives. In particular, for the elderly and those unfamiliar with digital technology, accessing information is often complex and it is difficult to make decisions quickly. Therefore, there is a need for a system that allows residents to easily obtain real-time and personalized information. 【0285】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means. 【0286】 In this invention, the server includes means for receiving a user's request and analyzing it using natural language processing, means for obtaining necessary information based on the analyzed result, means for organizing and presenting the information in an optimal form for the user, means for receiving information through voice input and converting it into text using voice recognition technology, and means for providing the obtained information in real time either audibly or visually. As a result, residents can quickly and efficiently access the information necessary for their lives and make decisions smoothly. 【0287】 A "request" refers to an information request or inquiry issued by a user. 【0288】 "Natural language processing" is a technology by which a computer understands and analyzes human language. 【0289】 "Analysis" means to investigate the given data or information in detail and clarify its meaning and structure. 【0290】 "Information" refers to the data and knowledge required by a user. 【0291】 "Organization" means to summarize and structure the obtained information in an understandable way. 【0292】 "Presentation" means to show or provide the necessary information to the user. 【0293】 "Voice input" refers to the process of capturing the user's voice as digital data into the system. 【0294】 "Speech recognition technology" is a technology that converts speech into text. 【0295】 "Text" refers to data or sentences composed of characters. 【0296】 "Real-time" means processing or responding in a near-instantaneous time. 【0297】 The system that realizes this invention is a platform in which servers, terminals, and users interact with each other. This system is implemented using Python and JavaScript, and the Flask framework is used on the server side. The Hugging Face Transformers library is utilized for natural language processing. 【0298】 Users send requests via voice input from devices such as smartphones or smart glasses. This voice data is converted into text data using speech recognition technologies such as the Google Cloud Speech-to-Text API. The server then receives this text data and performs natural language processing using the Transformers library. Based on the parsed request, the server collects necessary information from databases and external APIs, and organizes and personalizes that information. The information is restructured depending on the user's settings and past history. The organized information is then presented to the user as a visual screen or audio output. 【0299】 A concrete application example would be a user asking their smartphone, "What's the weather like today, and what's the traffic information for my nearest station?" The server would analyze the request, instantly retrieve real-time weather forecasts and traffic information, and provide it to the user via voice. 【0300】 Also, as an example of a prompt sentence, "Tell me recommended local events that I can participate in this weekend" can be cited. With this prompt, the user can quickly obtain relevant event information. 【0301】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0302】 Step 1: 【0303】 The terminal receives the user's voice input. The user uses the microphone to make a voice request such as "Tell me today's weather and the traffic information of the nearest station". The input is voice data, and the terminal records this voice in digital form. 【0304】 Step 2: 【0305】 The terminal converts the voice data into text data. The voice input is sent to the Google Cloud Speech-to-Text API and converted into text using speech recognition technology. The output is text data saying "Tell me today's weather and the traffic information of the nearest station". 【0306】 Step 3: 【0307】 The server receives the text data and performs natural language processing. The server uses the Hugging Face's Transformers library to analyze the text data and understand the content of the request. The input is text data, and the output is the structured information of the request content. 【0308】 Step 4: 【0309】 The server obtains the necessary information from the database or external API based on the structured information. Based on the analyzed request, it accesses the weather forecast API and traffic information API to collect the corresponding data. The input is the structured information of the request content, and the output is the obtained real-time weather forecast data and traffic information data. 【0310】 Step 5: 【0311】 The server personalizes and organizes the data it acquires for the user. The server references the user's settings and past history to reconstruct the acquired data in a way that is easy for the user to understand. The input consists of acquired data and user profile information, while the output is personalized information. 【0312】 Step 6: 【0313】 The server sends organized information to the terminal, which then presents the information visually or audibly. The terminal displays the received personalized information on its screen or communicates it to the user audibly using speech synthesis technology. The input is personalized information, and the output is visually or audibly displayed information. 【0314】 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. 【0315】 The system of the present invention, in order to provide multifaceted support for the user's life, is equipped with an emotion recognition function in addition to the conventional information provision function. The user inputs a request using a terminal. The terminal sends the request to the server. The server is equipped with an emotion engine and analyzes the user's emotions based on the content of the received request. 【0316】 The server first understands the content of the request through natural language processing. Then, it uses an emotion engine to analyze the emotional tone and nuances in the text to determine the user's current emotional state. This determination is crucial for making the response to the user more personalized. 【0317】 Based on the analysis results and emotional state, the server retrieves appropriate information from the database and external sources. The retrieved information is then personalized based on the user's past history and the results of the emotional analysis. 【0318】 Ultimately, the server organizes the information in the most appropriate way based on the user's emotions and generates a response using natural language generation technology. This response is then sent to the terminal and presented to the user. 【0319】 As a concrete example, let's assume a user is feeling stressed and is looking for events that can help them relax. When the user requests, "Tell me about events that can help me relax," the server uses an emotion engine to analyze the nuances of stress contained in the request. Based on this analysis, the server quickly and effectively satisfies the user's needs by providing information on relaxation events that are suitable for the user. 【0320】 This system aims to go beyond simply providing information and offer a more empathetic experience that takes into account the user's emotional state, by enabling emotion-based responses. As a result, it can further improve the quality of life for local residents and contribute to the revitalization of local communities. 【0321】 The following describes the processing flow. 【0322】 Step 1: 【0323】 The user accesses the system using a terminal and enters a voluntary request. The terminal prepares this information as a request to the system. 【0324】 Step 2: 【0325】 The terminal sends the user's request to the server. The server receives the request and prepares for the next processing step. 【0326】 Step 3: 【0327】 The server performs natural language processing to parse the request text. This analysis includes a process of extracting the gist of the request and important information. 【0328】 Step 4: 【0329】 The server uses an emotion engine to recognize the user's emotions from the request and evaluate their current emotional state. This enables the provision of information based on the user's emotions. 【0330】 Step 5: 【0331】 Based on the analysis results, the server retrieves necessary information from internal databases and external data sources. At this time, appropriate information selection is made based on emotional information. 【0332】 Step 6: 【0333】 The server references the user's past history and sentiment data to personalize the retrieved information. Machine learning algorithms are applied in this process. 【0334】 Step 7: 【0335】 The server uses natural language generation technology to create responses to the user based on personalized information. The information is organized according to the user's emotions, resulting in relatable content. 【0336】 Step 8: 【0337】 The server sends the generated response to the terminal. The terminal displays this response on its screen and presents it to the user. 【0338】 Step 9: 【0339】 The user reviews the provided information and decides on an action that suits their needs. If further information is needed, they can restart the process from step 1. 【0340】 (Example 2) 【0341】 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". 【0342】 Modern information providers often present information without considering the user's specific situation or emotional state, resulting in a problem where the information provided fails to adequately meet the user's true needs. Furthermore, they struggle to fully meet users' expectations for real-time, immediate information delivery. 【0343】 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. 【0344】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing technology, means for determining the user's emotional state based on the analysis results, and means for acquiring necessary data based on the determined emotional state. This enables the provision of more personalized information according to the user's emotions and enables the efficient provision of timely information. 【0345】 A "user" is an entity that uses a system to obtain information or input requests. 【0346】 A "request" is an instruction from a user to the system to obtain the information or action they need. 【0347】 "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and manipulate human language. 【0348】 "Analysis" is the process of breaking down information and understanding its meaning and intent. 【0349】 "Emotional state" refers to the emotional tone and nuances extracted from the user's request. 【0350】 "Data" refers to a collection of numbers, characters, symbols, and other elements used to represent specific information. 【0351】 "Acquisition" refers to the act of selecting necessary data and information and incorporating them into a system. 【0352】 "Generative technology" refers to the techniques that computers use to create natural-sounding text and information. 【0353】 "User's past usage history" refers to a record of information and actions recorded when a user uses the system. 【0354】 "Current data from external sources" refers to the latest data obtained from external information sources outside the system. 【0355】 This system provides information based on user input, and therefore basically operates with a configuration centered around terminals, servers, and communication networks. 【0356】 The user first uses their device to enter a request for specific information. For example, this might occur if the user types "Tell me about events where I can relax" into their device. This request is then sent to the server via the internet. 【0357】 The server is equipped with software that implements natural language processing technology. This technology analyzes requests sent by users and interprets what they mean. Furthermore, the server has an emotion engine that analyzes the user's emotional state based on the content of the request. Based on this emotional state, the server retrieves more appropriate data. 【0358】 The server quickly retrieves the necessary data through database access technologies and APIs. The retrieved data is then personalized, taking into account the user's past history and emotional state. Generative technologies are then used to organize the information into a user-friendly format. This information is finally transmitted to the terminal via the internet and presented to the user. 【0359】 As a concrete example, consider a scenario where a user requests, "Recommend some movies I can enjoy this weekend." The server analyzes this request using natural language processing technology, taking into account factors such as movie genre and time slot, and further understands the underlying emotions behind the request through an emotion engine. Based on the data obtained, the server selects the most suitable movie information for the user and returns a generated message to the device. A natural response such as, "Here's a recommended comedy movie to help you relax this weekend: XX," would be displayed. 【0360】 A concrete example of a prompt for a generative AI model might be, "If someone is on vacation and looking for a travel destination to relieve stress, what suggestions should I give them?" This prompt requests the AI ​​model to provide information based on user attributes and context. 【0361】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0362】 Step 1: 【0363】 The user enters a request for specific information into the terminal in text format. This request might be in the format of "Tell me about events that can help me relax." The entered text is sent to the server via the internet. 【0364】 Step 2: 【0365】 The server analyzes received requests using natural language processing techniques. The input is the request text from the user, and the output is structured data that captures the intent of the request. The server then performs morphological analysis and intent identification on this structured data. 【0366】 Step 3: 【0367】 The server analyzes the request content using an emotion engine to determine the user's emotional state. The input is structured data, and the output is emotional state data that includes emotional tone and nuances. The emotion engine determines emotions from keywords and phrases in the text. 【0368】 Step 4: 【0369】 The server retrieves necessary information from databases and external APIs based on emotional state data and request content. The input is emotional state and request content, and the output is information appropriate to those inputs. The server accesses databases using efficient queries and collects external information through public APIs. 【0370】 Step 5: 【0371】 The server personalizes the acquired information based on the user's past history and emotional state. The input is the acquired information, and the output is the personalized information. Here, specific patterns and trends from the user's history are taken into consideration, and the priority of the information is changed accordingly. 【0372】 Step 6: 【0373】 The server converts personalized information into an understandable format using natural language generation technology. The input is personalized information, and the output is text generated for the user. The server uses templates to create a natural sentence structure. 【0374】 Step 7: 【0375】 The server sends the final sentence to the terminal via the internet. The input is a sentence generated in natural language, and the output is a text response to the terminal. 【0376】 Step 8: 【0377】 The terminal displays the text response received from the server in the user interface. The input is the text received from the server, and the output is visually presented information. Through this, the user can verify the necessary information. 【0378】 (Application Example 2) 【0379】 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." 【0380】 For the elderly and those requiring care, it is essential to constantly monitor their emotional state and provide appropriate information and support accordingly. However, conventional information provision systems often fail to consider the user's emotions and remain uniform in their information delivery. As a result, necessary support and appropriate information are not provided in a timely manner, leading to decreased user satisfaction. 【0381】 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. 【0382】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing, means for obtaining necessary information based on the analysis results, and means for analyzing the user's emotions and personalizing the response based on those emotions. This makes it possible to provide personalized responses and information that take into account the user's emotional state. 【0383】 "Means of receiving user requests and analyzing them using natural language processing" refers to techniques that obtain inquiries and instructions from users and analyze text data in order to interpret them. 【0384】 "Means of obtaining necessary information based on the analyzed results" refers to techniques that retrieve relevant information from databases or external sources based on results obtained through natural language processing. 【0385】 "Means of organizing and presenting information in a way that is optimal for the user" refers to technologies that organize acquired information according to its purpose and situation, and present it in a way that is easy for the user to understand. 【0386】 "Means of analyzing user emotions and personalizing responses based on those emotions" refers to technology that identifies the user's emotional state and generates individually tailored responses based on the results of that identification. 【0387】 "A means of acquiring real-time data from external sources and providing users with immediate information and responses tailored to their emotions" refers to a technology that instantly incorporates the latest information from external sources and uses it to create immediate responses that match the user's emotions. 【0388】 This system has the ability to effectively analyze user requests and personalize responses by providing an interface equipped with emotion recognition technology. The system consists of a server with emotion recognition capabilities and a terminal that enables interaction with the user. The terminal receives a voice or text request from the user and initiates a series of processes. 【0389】 The server first uses a natural language processing engine to analyze the user's request. The software used here includes natural language processing libraries such as spaCy and NLTK. This allows the content of the text to be understood, and the next stage is sentiment analysis. The server's sentiment analysis engine uses tools such as the Google Cloud Natural Language API to analyze the user's emotional tone in detail. 【0390】 Once the user's emotions are identified, the server retrieves appropriate information from a database or external source based on the results. A system like MySQL can be used for database management. Next, a natural language generation engine, such as the OpenAI GPT model, is used to generate a personalized response. This response is sent to the terminal and presented to the user in an appropriate format. 【0391】 As a concrete example, if an elderly person in a nursing home says, "I want to relax today," the device sends that request to the server. If the emotion analysis determines that the person is seeking relaxation, the server can generate suggestions for calming music or simple relaxation exercises and present them to the user. 【0392】 An example of a prompt for a generative AI model is, "If the user is seeking relaxation, generate personalized relaxation content." 【0393】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0394】 Step 1: 【0395】 The device receives a voice or text request from the user. The voice data obtained as input is converted into text data using speech recognition technology. The text obtained in this process forms the basis for the next analysis. 【0396】 Step 2: 【0397】 The server analyzes the text data using a natural language processing engine (e.g., spaCy or NLTK). Here, morphological and syntactic analysis are performed to analyze the meaning of the input text and understand its content. The server receives the output, which includes the text's structure and semantic information. 【0398】 Step 3: 【0399】 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to evaluate the emotional tone of the analyzed text. Emotion analysis analyzes language patterns and nuances to identify the user's emotional state (e.g., joy, sadness, stress). The result of the emotional state assessment is then output. 【0400】 Step 4: 【0401】 The server retrieves relevant information from databases and external sources based on the user's emotional state. Input includes the results of the emotional analysis and the user's profile information. Output provides relaxation content and suggestions tailored to the user. 【0402】 Step 5: 【0403】 The server uses a generative AI model (e.g., the OpenAI GPT model) to generate personalized responses. The input consists of acquired information and sentiment analysis results. The generative AI model uses this to create response text in natural language, providing specific suggestions and information as output. 【0404】 Step 6: 【0405】 The device presents the response sent from the server to the user. Presentation methods include text display, text-to-speech, and playback of visual content. This allows the user to receive information and suggestions optimized for their emotions. 【0406】 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. 【0407】 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. 【0408】 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. 【0409】 [Third Embodiment] 【0410】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0411】 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. 【0412】 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). 【0413】 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. 【0414】 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. 【0415】 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). 【0416】 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. 【0417】 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. 【0418】 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. 【0419】 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. 【0420】 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. 【0421】 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". 【0422】 This invention's system uses AI technology to support local residents in addressing various life challenges they face. Primarily, the system involves interaction between a server, a terminal, and a user, who then provide information. 【0423】 Users access the system through a terminal and enter requests regarding their needs. The terminal sends these requests to the server. The server performs natural language processing on the received requests and parses their content. Based on the analysis, the server retrieves the necessary information from the database and external data sources. 【0424】 Once information is collected, the server personalizes it by referencing the user's past usage history and settings. It utilizes machine learning algorithms to extract and organize information deemed most beneficial to the user. The server then reconstructs the information in a user-friendly format and sends the generated response to the device. 【0425】 As a concrete example, consider a scenario where a user wants to participate in a local event. When the user types "Tell me about local events this weekend," the server accesses a database of local events and retrieves relevant event information. Then, based on the user's interests and past participation history, it selects the most relevant events and returns detailed information as a response. This process allows the user to obtain the necessary information quickly and efficiently. 【0426】 Each distinctive feature of this system is designed to improve user convenience, and is particularly easy to use for the elderly and users unfamiliar with digital technology. This will improve information access throughout the community and enhance the quality of life for residents. 【0427】 The following describes the processing flow. 【0428】 Step 1: 【0429】 The user operates the terminal to access the system interface and enter a request based on their needs. The terminal holds the entered request and prepares it for transmission. 【0430】 Step 2: 【0431】 The terminal sends a request from the user to the server. The server receives this request and begins preparing for analysis. 【0432】 Step 3: 【0433】 The server uses natural language processing to analyze the intent of incoming requests. This analysis includes extracting keywords and important information from the requests. 【0434】 Step 4: 【0435】 The server accesses an internal database based on the analysis results to search for relevant information. If necessary, it also retrieves information from external APIs and data sources. 【0436】 Step 5: 【0437】 The server references the user's past history and settings, and personalizes the collected information. Machine learning algorithms are used here to select the information that is most relevant to the user. 【0438】 Step 6: 【0439】 The server generates responses to the user based on personalized information. It uses natural language generation technology to construct easily understandable text. 【0440】 Step 7: 【0441】 The server sends the generated response to the terminal. The terminal receives this response and displays it in the user interface. 【0442】 Step 8: 【0443】 The user reviews the information on their device screen and considers the next action as needed. If they have further questions, they can repeat the process from step 1 to obtain more information. 【0444】 (Example 1) 【0445】 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." 【0446】 In modern society, local residents face a variety of life challenges, and there is a need to respond to them quickly and efficiently. However, for people unfamiliar with technology and the elderly, it is difficult to collect necessary information and obtain it in an easily understandable format. In particular, providing personalized information and obtaining real-time information presents a problem because it cannot be easily achieved without specialized knowledge. 【0447】 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. 【0448】 In this invention, the server includes means for receiving user requests and analyzing them using language processing technology, means for obtaining necessary information based on the analysis results, and means for organizing and presenting the information in a format suitable for the user. This makes it possible for even users unfamiliar with technology to quickly and efficiently obtain personalized information and address local community issues. 【0449】 "Receiving user requests" refers to the process of appropriately delivering the user's needs and questions to the server. 【0450】 "Analyzing using language processing technology" refers to the technique of using natural language processing technology to analyze language data and understand the user's intent. 【0451】 "Acquiring necessary information" refers to the process of collecting the required data from databases or external sources based on the analysis results. 【0452】 "Organizing and presenting information in a way that is suitable for the user" is the process of organizing acquired information and providing it in a format that is easy for the user to understand. 【0453】 "Sending information to the server via the terminal" refers to the communication process of transferring a user's request from the terminal to the server. 【0454】 "Providing personalized information" refers to providing information that is individualized based on the user's past activities and settings, and that meets the needs of each user. 【0455】 "Using algorithms to select and reorganize information" refers to the process of using machine learning or other computational methods to select and reorganize collected information in a way that is most relevant to the user. 【0456】 This invention provides a system for solving the daily life problems of local residents. Users access the system using devices such as smartphones and personal computers. A dedicated application or web browser is installed on the user's device. Using this, the user inputs prompt messages related to specific needs. A concrete example of a prompt message is, "Tell me about local events this weekend." 【0457】 The terminal sends the user's prompt text to the server. The server parses the received prompt text using NLTK or spaCy, which are natural language processing (NLP) libraries implemented in Python. Through language analysis, the server understands the intent of the information the user is seeking. Based on the analysis, the server retrieves relevant information from a database or external data sources on the internet. MySQL is used for database management in this process. 【0458】 The collected information is personalized on the server side using machine learning algorithms. Frameworks such as TensorFlow are used here. This extracts the most relevant information based on the user's past usage history and settings, and reconstructs the information in a format suitable for the user. The reconstructed information is finally sent from the server to the terminal as an HTML or JSON response. As a result, the user can quickly and efficiently obtain the information they are looking for. 【0459】 This will provide an environment where even the elderly and those unfamiliar with technology can use this system to solve the life challenges they face. For example, by providing event information quickly based on the prompts mentioned above, information access in the community will improve, thereby enhancing the quality of life for residents. 【0460】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0461】 Step 1: 【0462】 Users use devices such as smartphones or personal computers to input prompt messages related to specific needs. An example input here is "Tell me about local events this weekend." This prompt message is entered as data that initiates the system's processing. 【0463】 Step 2: 【0464】 The terminal sends the entered prompt text to the server. The input is text data, and the transmitted data is transferred to the server via the HTTP protocol. This allows the server to recognize the user's request and prepare to begin the next processing step. 【0465】 Step 3: 【0466】 The server analyzes the received prompt message using a natural language processing library. Specifically, it analyzes the language data using tools such as Python's NLTK and spaCy to understand the user's intent. As a result of the analysis, categories and keywords of necessary information are extracted. 【0467】 Step 4: 【0468】 Based on the analysis results, the server retrieves relevant information from the database and external data sources. To do this, the server uses SQL queries to search the MySQL database and extract relevant event information. If external data is required, additional data is obtained via APIs or web scraping. 【0469】 Step 5: 【0470】 The server uses machine learning algorithms to personalize the collected information. It considers the user's past history and settings, utilizing tools like TensorFlow, to select the most relevant information. This results in information optimized for the user. 【0471】 Step 6: 【0472】 The server reconfigures the selected information into a user-friendly format. The reconfigured data is then generated as a response in HTML or JSON format. This response is visually organized and easy for users to access and retrieve information. 【0473】 Step 7: 【0474】 The terminal displays the response received from the server. The response data is immediately displayed on the terminal's screen, allowing the user to quickly find the information they need. This makes it easy for users to obtain the data they require and get information to support their life decisions. 【0475】 (Application Example 1) 【0476】 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." 【0477】 A challenge exists in that local residents often have difficulty efficiently obtaining the information they need in their daily lives. In particular, accessing information is often complex for the elderly and those unfamiliar with digital technology, making quick decision-making difficult. Therefore, there is a need for a system that allows residents to easily obtain real-time, personalized information. 【0478】 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. 【0479】 In this invention, the server includes means for receiving user requests and analyzing them using natural language processing, means for obtaining necessary information based on the analysis results, means for organizing and presenting the information in a format optimal for the user, means for receiving information through voice input and converting it to text using speech recognition technology, and means for providing the obtained information in real time, either by voice or visually. This enables residents to quickly and efficiently access information necessary for their lives and to make decisions smoothly. 【0480】 A "request" is an information request or inquiry issued by a user. 【0481】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0482】 "Analysis" is the process of thoroughly examining given data or information to clarify its meaning and structure. 【0483】 "Information" refers to the data and knowledge that users need. 【0484】 "Organization" means summarizing and structuring the information obtained in an easy-to-understand way. 【0485】 "Presentation" means showing or providing information that is necessary for the user. 【0486】 "Voice input" refers to the process of capturing the user's voice as digital data into the system. 【0487】 "Speech recognition technology" is a technology that converts speech into text. 【0488】 "Text" refers to data or sentences composed of characters. 【0489】 "Real-time" means processing or responding in a near-instantaneous time. 【0490】 The system that realizes this invention is a platform in which servers, terminals, and users interact with each other. This system is implemented using Python and JavaScript, and the Flask framework is used on the server side. The Hugging Face Transformers library is utilized for natural language processing. 【0491】 Users send requests via voice input from devices such as smartphones or smart glasses. This voice data is converted into text data using speech recognition technologies such as the Google Cloud Speech-to-Text API. The server then receives this text data and performs natural language processing using the Transformers library. Based on the parsed request, the server collects necessary information from databases and external APIs, and organizes and personalizes that information. The information is restructured depending on the user's settings and past history. The organized information is then presented to the user as a visual screen or audio output. 【0492】 A concrete application example would be a user asking their smartphone, "What's the weather like today, and what's the traffic information for my nearest station?" The server would analyze the request, instantly retrieve real-time weather forecasts and traffic information, and provide it to the user via voice. 【0493】 Another example of a prompt is, "Tell me about recommended local events I can attend this weekend." This prompt allows users to quickly obtain relevant event information. 【0494】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0495】 Step 1: 【0496】 The device receives voice input from the user. The user uses the microphone to make a voice request, such as, "Tell me today's weather and transportation information for the nearest station." The input is voice data, and the device records this voice in digital format. 【0497】 Step 2: 【0498】 The device converts voice data into text data. The voice input is sent to the Google Cloud Speech-to-Text API, where speech recognition technology is used to convert it into text. The output is the text "Tell me today's weather and transportation information for the nearest station." 【0499】 Step 3: 【0500】 The server receives text data and performs natural language processing. The server uses the Hugging Face Transformers library to parse the text data and understand the request content. The input is text data, and the output is structured information of the request content. 【0501】 Step 4: 【0502】 The server retrieves necessary information from databases and external APIs based on structured information. Based on the parsed request, it accesses weather forecast APIs and traffic information APIs to collect the relevant data. The input is structured information of the request content, and the output is the retrieved real-time weather forecast data and traffic information data. 【0503】 Step 5: 【0504】 The server personalizes and organizes the data it acquires for the user. The server references the user's settings and past history to reconstruct the acquired data in a way that is easy for the user to understand. The input consists of acquired data and user profile information, while the output is personalized information. 【0505】 Step 6: 【0506】 The server sends organized information to the terminal, which then presents the information visually or audibly. The terminal displays the received personalized information on its screen or communicates it to the user audibly using speech synthesis technology. The input is personalized information, and the output is visually or audibly displayed information. 【0507】 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. 【0508】 The system of the present invention, in order to provide multifaceted support for the user's life, is equipped with an emotion recognition function in addition to the conventional information provision function. The user inputs a request using a terminal. The terminal sends the request to the server. The server is equipped with an emotion engine and analyzes the user's emotions based on the content of the received request. 【0509】 The server first understands the content of the request through natural language processing. Then, it uses an emotion engine to analyze the emotional tone and nuances in the text to determine the user's current emotional state. This determination is crucial for making the response to the user more personalized. 【0510】 Based on the analysis results and emotional state, the server retrieves appropriate information from the database and external sources. The retrieved information is then personalized based on the user's past history and the results of the emotional analysis. 【0511】 Ultimately, the server organizes the information in the most appropriate way based on the user's emotions and generates a response using natural language generation technology. This response is then sent to the terminal and presented to the user. 【0512】 As a concrete example, let's assume a user is feeling stressed and is looking for events that can help them relax. When the user requests, "Tell me about events that can help me relax," the server uses an emotion engine to analyze the nuances of stress contained in the request. Based on this analysis, the server quickly and effectively satisfies the user's needs by providing information on relaxation events that are suitable for the user. 【0513】 This system aims to go beyond simply providing information and offer a more empathetic experience that takes into account the user's emotional state, by enabling emotion-based responses. As a result, it can further improve the quality of life for local residents and contribute to the revitalization of local communities. 【0514】 The following describes the processing flow. 【0515】 Step 1: 【0516】 The user accesses the system using a terminal and enters a voluntary request. The terminal prepares this information as a request to the system. 【0517】 Step 2: 【0518】 The terminal sends the user's request to the server. The server receives the request and prepares for the next processing step. 【0519】 Step 3: 【0520】 The server performs natural language processing to parse the request text. This analysis includes a process of extracting the gist of the request and important information. 【0521】 Step 4: 【0522】 The server uses an emotion engine to recognize the user's emotions from the request and evaluate their current emotional state. This enables the provision of information based on the user's emotions. 【0523】 Step 5: 【0524】 Based on the analysis results, the server retrieves necessary information from internal databases and external data sources. At this time, appropriate information selection is made based on emotional information. 【0525】 Step 6: 【0526】 The server references the user's past history and sentiment data to personalize the retrieved information. Machine learning algorithms are applied in this process. 【0527】 Step 7: 【0528】 The server uses natural language generation technology to create responses to the user based on personalized information. The information is organized according to the user's emotions, resulting in relatable content. 【0529】 Step 8: 【0530】 The server sends the generated response to the terminal. The terminal displays this response on its screen and presents it to the user. 【0531】 Step 9: 【0532】 The user reviews the provided information and decides on an action that suits their needs. If further information is needed, they can restart the process from step 1. 【0533】 (Example 2) 【0534】 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." 【0535】 Modern information providers often present information without considering the user's specific situation or emotional state, resulting in a problem where the information provided fails to adequately meet the user's true needs. Furthermore, they struggle to fully meet users' expectations for real-time, immediate information delivery. 【0536】 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. 【0537】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing technology, means for determining the user's emotional state based on the analysis results, and means for acquiring necessary data based on the determined emotional state. This enables the provision of more personalized information according to the user's emotions and enables the efficient provision of timely information. 【0538】 A "user" is an entity that uses a system to obtain information or input requests. 【0539】 A "request" is an instruction from a user to the system to obtain the information or action they need. 【0540】 "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and manipulate human language. 【0541】 "Analysis" is the process of breaking down information and understanding its meaning and intent. 【0542】 "Emotional state" refers to the emotional tone and nuances extracted from the user's request. 【0543】 "Data" refers to a collection of numbers, characters, symbols, and other elements used to represent specific information. 【0544】 "Acquisition" refers to the act of selecting necessary data and information and incorporating them into a system. 【0545】 "Generative technology" refers to the techniques that computers use to create natural-sounding text and information. 【0546】 "User's past usage history" refers to a record of information and actions recorded when a user uses the system. 【0547】 "Current data from external sources" refers to the latest data obtained from external information sources outside the system. 【0548】 This system provides information based on user input, and therefore basically operates with a configuration centered around terminals, servers, and communication networks. 【0549】 The user first uses their device to enter a request for specific information. For example, this might occur if the user types "Tell me about events where I can relax" into their device. This request is then sent to the server via the internet. 【0550】 The server is equipped with software that implements natural language processing technology. This technology analyzes requests sent by users and interprets what they mean. Furthermore, the server has an emotion engine that analyzes the user's emotional state based on the content of the request. Based on this emotional state, the server retrieves more appropriate data. 【0551】 The server quickly retrieves the necessary data through database access technologies and APIs. The retrieved data is then personalized, taking into account the user's past history and emotional state. Generative technologies are then used to organize the information into a user-friendly format. This information is finally transmitted to the terminal via the internet and presented to the user. 【0552】 As a concrete example, consider a scenario where a user requests, "Recommend some movies I can enjoy this weekend." The server analyzes this request using natural language processing technology, taking into account factors such as movie genre and time slot, and further understands the underlying emotions behind the request through an emotion engine. Based on the data obtained, the server selects the most suitable movie information for the user and returns a generated message to the device. A natural response such as, "Here's a recommended comedy movie to help you relax this weekend: XX," would be displayed. 【0553】 A concrete example of a prompt for a generative AI model might be, "If someone is on vacation and looking for a travel destination to relieve stress, what suggestions should I give them?" This prompt requests the AI ​​model to provide information based on user attributes and context. 【0554】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0555】 Step 1: 【0556】 The user enters a request for specific information into the terminal in text format. This request might be in the format of "Tell me about events that can help me relax." The entered text is sent to the server via the internet. 【0557】 Step 2: 【0558】 The server analyzes received requests using natural language processing techniques. The input is the request text from the user, and the output is structured data that captures the intent of the request. The server then performs morphological analysis and intent identification on this structured data. 【0559】 Step 3: 【0560】 The server analyzes the request content using an emotion engine to determine the user's emotional state. The input is structured data, and the output is emotional state data that includes emotional tone and nuances. The emotion engine determines emotions from keywords and phrases in the text. 【0561】 Step 4: 【0562】 The server retrieves necessary information from databases and external APIs based on emotional state data and request content. The input is emotional state and request content, and the output is information appropriate to those inputs. The server accesses databases using efficient queries and collects external information through public APIs. 【0563】 Step 5: 【0564】 The server personalizes the acquired information based on the user's past history and emotional state. The input is the acquired information, and the output is the personalized information. Here, specific patterns and trends from the user's history are taken into consideration, and the priority of the information is changed accordingly. 【0565】 Step 6: 【0566】 The server converts personalized information into an understandable format using natural language generation technology. The input is personalized information, and the output is text generated for the user. The server uses templates to create a natural sentence structure. 【0567】 Step 7: 【0568】 The server sends the final sentence to the terminal via the internet. The input is a sentence generated in natural language, and the output is a text response to the terminal. 【0569】 Step 8: 【0570】 The terminal displays the text response received from the server in the user interface. The input is the text received from the server, and the output is visually presented information. Through this, the user can verify the necessary information. 【0571】 (Application Example 2) 【0572】 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." 【0573】 For the elderly and those requiring care, it is essential to constantly monitor their emotional state and provide appropriate information and support accordingly. However, conventional information provision systems often fail to consider the user's emotions and remain uniform in their information delivery. As a result, necessary support and appropriate information are not provided in a timely manner, leading to decreased user satisfaction. 【0574】 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. 【0575】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing, means for obtaining necessary information based on the analysis results, and means for analyzing the user's emotions and personalizing the response based on those emotions. This makes it possible to provide personalized responses and information that take into account the user's emotional state. 【0576】 "Means of receiving user requests and analyzing them using natural language processing" refers to techniques that obtain inquiries and instructions from users and analyze text data in order to interpret them. 【0577】 "Means of obtaining necessary information based on the analyzed results" refers to techniques that retrieve relevant information from databases or external sources based on results obtained through natural language processing. 【0578】 "Means of organizing and presenting information in a way that is optimal for the user" refers to technologies that organize acquired information according to its purpose and situation, and present it in a way that is easy for the user to understand. 【0579】 "Means of analyzing user emotions and personalizing responses based on those emotions" refers to technology that identifies the user's emotional state and generates individually tailored responses based on the results of that identification. 【0580】 "A means of acquiring real-time data from external sources and providing users with immediate information and responses tailored to their emotions" refers to a technology that instantly incorporates the latest information from external sources and uses it to create immediate responses that match the user's emotions. 【0581】 This system has the ability to effectively analyze user requests and personalize responses by providing an interface equipped with emotion recognition technology. The system consists of a server with emotion recognition capabilities and a terminal that enables interaction with the user. The terminal receives a voice or text request from the user and initiates a series of processes. 【0582】 The server first uses a natural language processing engine to analyze the user's request. The software used here includes natural language processing libraries such as spaCy and NLTK. This allows the content of the text to be understood, and the next stage is sentiment analysis. The server's sentiment analysis engine uses tools such as the Google Cloud Natural Language API to analyze the user's emotional tone in detail. 【0583】 Once the user's emotions are identified, the server retrieves appropriate information from a database or external source based on the results. A system like MySQL can be used for database management. Next, a natural language generation engine, such as the OpenAI GPT model, is used to generate a personalized response. This response is sent to the terminal and presented to the user in an appropriate format. 【0584】 As a concrete example, if an elderly person in a nursing home says, "I want to relax today," the device sends that request to the server. If the emotion analysis determines that the person is seeking relaxation, the server can generate suggestions for calming music or simple relaxation exercises and present them to the user. 【0585】 An example of a prompt for a generative AI model is, "If the user is seeking relaxation, generate personalized relaxation content." 【0586】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0587】 Step 1: 【0588】 The device receives a voice or text request from the user. The voice data obtained as input is converted into text data using speech recognition technology. The text obtained in this process forms the basis for the next analysis. 【0589】 Step 2: 【0590】 The server analyzes the text data using a natural language processing engine (e.g., spaCy or NLTK). Here, morphological and syntactic analysis are performed to analyze the meaning of the input text and understand its content. The server receives the output, which includes the text's structure and semantic information. 【0591】 Step 3: 【0592】 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to evaluate the emotional tone of the analyzed text. Emotion analysis analyzes language patterns and nuances to identify the user's emotional state (e.g., joy, sadness, stress). The result of the emotional state assessment is then output. 【0593】 Step 4: 【0594】 The server retrieves relevant information from databases and external sources based on the user's emotional state. Input includes the results of the emotional analysis and the user's profile information. Output provides relaxation content and suggestions tailored to the user. 【0595】 Step 5: 【0596】 The server uses a generative AI model (e.g., the OpenAI GPT model) to generate personalized responses. The input consists of acquired information and sentiment analysis results. The generative AI model uses this to create response text in natural language, providing specific suggestions and information as output. 【0597】 Step 6: 【0598】 The device presents the response sent from the server to the user. Presentation methods include text display, text-to-speech, and playback of visual content. This allows the user to receive information and suggestions optimized for their emotions. 【0599】 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. 【0600】 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. 【0601】 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. 【0602】 [Fourth Embodiment] 【0603】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0604】 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. 【0605】 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). 【0606】 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. 【0607】 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. 【0608】 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). 【0609】 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. 【0610】 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. 【0611】 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. 【0612】 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. 【0613】 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. 【0614】 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. 【0615】 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". 【0616】 This invention's system uses AI technology to support local residents in addressing various life challenges they face. Primarily, the system involves interaction between a server, a terminal, and a user, who then provide information. 【0617】 Users access the system through a terminal and enter requests regarding their needs. The terminal sends these requests to the server. The server performs natural language processing on the received requests and parses their content. Based on the analysis, the server retrieves the necessary information from the database and external data sources. 【0618】 Once information is collected, the server personalizes it by referencing the user's past usage history and settings. It utilizes machine learning algorithms to extract and organize information deemed most beneficial to the user. The server then reconstructs the information in a user-friendly format and sends the generated response to the device. 【0619】 As a concrete example, consider a scenario where a user wants to participate in a local event. When the user types "Tell me about local events this weekend," the server accesses a database of local events and retrieves relevant event information. Then, based on the user's interests and past participation history, it selects the most relevant events and returns detailed information as a response. This process allows the user to obtain the necessary information quickly and efficiently. 【0620】 Each distinctive feature of this system is designed to improve user convenience, and is particularly easy to use for the elderly and users unfamiliar with digital technology. This will improve information access throughout the community and enhance the quality of life for residents. 【0621】 The following describes the processing flow. 【0622】 Step 1: 【0623】 The user operates the terminal to access the system interface and enter a request based on their needs. The terminal holds the entered request and prepares it for transmission. 【0624】 Step 2: 【0625】 The terminal sends a request from the user to the server. The server receives this request and begins preparing for analysis. 【0626】 Step 3: 【0627】 The server uses natural language processing to analyze the intent of incoming requests. This analysis includes extracting keywords and important information from the requests. 【0628】 Step 4: 【0629】 The server accesses an internal database based on the analysis results to search for relevant information. If necessary, it also retrieves information from external APIs and data sources. 【0630】 Step 5: 【0631】 The server references the user's past history and settings, and personalizes the collected information. Machine learning algorithms are used here to select the information that is most relevant to the user. 【0632】 Step 6: 【0633】 The server generates responses to the user based on personalized information. It uses natural language generation technology to construct easily understandable text. 【0634】 Step 7: 【0635】 The server sends the generated response to the terminal. The terminal receives this response and displays it in the user interface. 【0636】 Step 8: 【0637】 The user reviews the information on their device screen and considers the next action as needed. If they have further questions, they can repeat the process from step 1 to obtain more information. 【0638】 (Example 1) 【0639】 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". 【0640】 In modern society, local residents face a variety of life challenges, and there is a need to respond to them quickly and efficiently. However, for people unfamiliar with technology and the elderly, it is difficult to collect necessary information and obtain it in an easily understandable format. In particular, providing personalized information and obtaining real-time information presents a problem because it cannot be easily achieved without specialized knowledge. 【0641】 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. 【0642】 In this invention, the server includes means for receiving user requests and analyzing them using language processing technology, means for obtaining necessary information based on the analysis results, and means for organizing and presenting the information in a format suitable for the user. This makes it possible for even users unfamiliar with technology to quickly and efficiently obtain personalized information and address local community issues. 【0643】 "Receiving user requests" refers to the process of appropriately delivering the user's needs and questions to the server. 【0644】 "Analyzing using language processing technology" refers to the technique of using natural language processing technology to analyze language data and understand the user's intent. 【0645】 "Acquiring necessary information" refers to the process of collecting the required data from databases or external sources based on the analysis results. 【0646】 "Organizing and presenting information in a way that is suitable for the user" is the process of organizing acquired information and providing it in a format that is easy for the user to understand. 【0647】 "Sending information to the server via the terminal" refers to the communication process of transferring a user's request from the terminal to the server. 【0648】 "Providing personalized information" refers to providing information that is individualized based on the user's past activities and settings, and that meets the needs of each user. 【0649】 "Using algorithms to select and reorganize information" refers to the process of using machine learning or other computational methods to select and reorganize collected information in a way that is most relevant to the user. 【0650】 This invention provides a system for solving the daily life problems of local residents. Users access the system using devices such as smartphones and personal computers. A dedicated application or web browser is installed on the user's device. Using this, the user inputs prompt messages related to specific needs. A concrete example of a prompt message is, "Tell me about local events this weekend." 【0651】 The terminal sends the user's prompt text to the server. The server parses the received prompt text using NLTK or spaCy, which are natural language processing (NLP) libraries implemented in Python. Through language analysis, the server understands the intent of the information the user is seeking. Based on the analysis, the server retrieves relevant information from a database or external data sources on the internet. MySQL is used for database management in this process. 【0652】 The collected information is personalized on the server side using machine learning algorithms. Frameworks such as TensorFlow are used here. This extracts the most relevant information based on the user's past usage history and settings, and reconstructs the information in a format suitable for the user. The reconstructed information is finally sent from the server to the terminal as an HTML or JSON response. As a result, the user can quickly and efficiently obtain the information they are looking for. 【0653】 This will provide an environment where even the elderly and those unfamiliar with technology can use this system to solve the life challenges they face. For example, by providing event information quickly based on the prompts mentioned above, information access in the community will improve, thereby enhancing the quality of life for residents. 【0654】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0655】 Step 1: 【0656】 Users use devices such as smartphones or personal computers to input prompt messages related to specific needs. An example input here is "Tell me about local events this weekend." This prompt message is entered as data that initiates the system's processing. 【0657】 Step 2: 【0658】 The terminal sends the entered prompt text to the server. The input is text data, and the transmitted data is transferred to the server via the HTTP protocol. This allows the server to recognize the user's request and prepare to begin the next processing step. 【0659】 Step 3: 【0660】 The server analyzes the received prompt message using a natural language processing library. Specifically, it analyzes the language data using tools such as Python's NLTK and spaCy to understand the user's intent. As a result of the analysis, categories and keywords of necessary information are extracted. 【0661】 Step 4: 【0662】 Based on the analysis results, the server retrieves relevant information from the database and external data sources. To do this, the server uses SQL queries to search the MySQL database and extract relevant event information. If external data is required, additional data is obtained via APIs or web scraping. 【0663】 Step 5: 【0664】 The server uses machine learning algorithms to personalize the collected information. It considers the user's past history and settings, utilizing tools like TensorFlow, to select the most relevant information. This results in information optimized for the user. 【0665】 Step 6: 【0666】 The server reconfigures the selected information into a user-friendly format. The reconfigured data is then generated as a response in HTML or JSON format. This response is visually organized and easy for users to access and retrieve information. 【0667】 Step 7: 【0668】 The terminal displays the response received from the server. The response data is immediately displayed on the terminal's screen, allowing the user to quickly find the information they need. This makes it easy for users to obtain the data they require and get information to support their life decisions. 【0669】 (Application Example 1) 【0670】 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". 【0671】 A challenge exists in that local residents often have difficulty efficiently obtaining the information they need in their daily lives. In particular, accessing information is often complex for the elderly and those unfamiliar with digital technology, making quick decision-making difficult. Therefore, there is a need for a system that allows residents to easily obtain real-time, personalized information. 【0672】 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. 【0673】 In this invention, the server includes means for receiving user requests and analyzing them using natural language processing, means for obtaining necessary information based on the analysis results, means for organizing and presenting the information in a format optimal for the user, means for receiving information through voice input and converting it to text using speech recognition technology, and means for providing the obtained information in real time, either by voice or visually. This enables residents to quickly and efficiently access information necessary for their lives and to make decisions smoothly. 【0674】 A "request" is an information request or inquiry issued by a user. 【0675】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0676】 "Analysis" is the process of thoroughly examining given data or information to clarify its meaning and structure. 【0677】 "Information" refers to the data and knowledge that users need. 【0678】 "Organization" means summarizing and structuring the information obtained in an easy-to-understand way. 【0679】 "Presentation" means showing or providing information that is necessary for the user. 【0680】 "Voice input" refers to the process of capturing the user's voice as digital data into the system. 【0681】 "Speech recognition technology" is a technology that converts speech into text. 【0682】 "Text" refers to data or sentences composed of characters. 【0683】 "Real-time" means processing or responding in a near-instantaneous time. 【0684】 The system that realizes this invention is a platform in which servers, terminals, and users interact with each other. This system is implemented using Python and JavaScript, and the Flask framework is used on the server side. The Hugging Face Transformers library is utilized for natural language processing. 【0685】 Users send requests via voice input from devices such as smartphones or smart glasses. This voice data is converted into text data using speech recognition technologies such as the Google Cloud Speech-to-Text API. The server then receives this text data and performs natural language processing using the Transformers library. Based on the parsed request, the server collects necessary information from databases and external APIs, and organizes and personalizes that information. The information is restructured depending on the user's settings and past history. The organized information is then presented to the user as a visual screen or audio output. 【0686】 A concrete application example would be a user asking their smartphone, "What's the weather like today, and what's the traffic information for my nearest station?" The server would analyze the request, instantly retrieve real-time weather forecasts and traffic information, and provide it to the user via voice. 【0687】 Another example of a prompt is, "Tell me about recommended local events I can attend this weekend." This prompt allows users to quickly obtain relevant event information. 【0688】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0689】 Step 1: 【0690】 The device receives voice input from the user. The user uses the microphone to make a voice request, such as, "Tell me today's weather and transportation information for the nearest station." The input is voice data, and the device records this voice in digital format. 【0691】 Step 2: 【0692】 The device converts voice data into text data. The voice input is sent to the Google Cloud Speech-to-Text API, where speech recognition technology is used to convert it into text. The output is the text "Tell me today's weather and transportation information for the nearest station." 【0693】 Step 3: 【0694】 The server receives text data and performs natural language processing. The server uses the Hugging Face Transformers library to parse the text data and understand the request content. The input is text data, and the output is structured information of the request content. 【0695】 Step 4: 【0696】 The server retrieves necessary information from databases and external APIs based on structured information. Based on the parsed request, it accesses weather forecast APIs and traffic information APIs to collect the relevant data. The input is structured information of the request content, and the output is the retrieved real-time weather forecast data and traffic information data. 【0697】 Step 5: 【0698】 The server personalizes and organizes the data it acquires for the user. The server references the user's settings and past history to reconstruct the acquired data in a way that is easy for the user to understand. The input consists of acquired data and user profile information, while the output is personalized information. 【0699】 Step 6: 【0700】 The server sends organized information to the terminal, which then presents the information visually or audibly. The terminal displays the received personalized information on its screen or communicates it to the user audibly using speech synthesis technology. The input is personalized information, and the output is visually or audibly displayed information. 【0701】 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. 【0702】 The system of the present invention, in order to provide multifaceted support for the user's life, is equipped with an emotion recognition function in addition to the conventional information provision function. The user inputs a request using a terminal. The terminal sends the request to the server. The server is equipped with an emotion engine and analyzes the user's emotions based on the content of the received request. 【0703】 The server first understands the content of the request through natural language processing. Then, it uses an emotion engine to analyze the emotional tone and nuances in the text to determine the user's current emotional state. This determination is crucial for making the response to the user more personalized. 【0704】 Based on the analysis results and emotional state, the server retrieves appropriate information from the database and external sources. The retrieved information is then personalized based on the user's past history and the results of the emotional analysis. 【0705】 Ultimately, the server organizes the information in the most appropriate way based on the user's emotions and generates a response using natural language generation technology. This response is then sent to the terminal and presented to the user. 【0706】 As a concrete example, let's assume a user is feeling stressed and is looking for events that can help them relax. When the user requests, "Tell me about events that can help me relax," the server uses an emotion engine to analyze the nuances of stress contained in the request. Based on this analysis, the server quickly and effectively satisfies the user's needs by providing information on relaxation events that are suitable for the user. 【0707】 This system aims to go beyond simply providing information and offer a more empathetic experience that takes into account the user's emotional state, by enabling emotion-based responses. As a result, it can further improve the quality of life for local residents and contribute to the revitalization of local communities. 【0708】 The following describes the processing flow. 【0709】 Step 1: 【0710】 The user accesses the system using a terminal and enters a voluntary request. The terminal prepares this information as a request to the system. 【0711】 Step 2: 【0712】 The terminal sends the user's request to the server. The server receives the request and prepares for the next processing step. 【0713】 Step 3: 【0714】 The server performs natural language processing to parse the request text. This analysis includes a process of extracting the gist of the request and important information. 【0715】 Step 4: 【0716】 The server uses an emotion engine to recognize the user's emotions from the request and evaluate their current emotional state. This enables the provision of information based on the user's emotions. 【0717】 Step 5: 【0718】 Based on the analysis results, the server retrieves necessary information from internal databases and external data sources. At this time, appropriate information selection is made based on emotional information. 【0719】 Step 6: 【0720】 The server references the user's past history and sentiment data to personalize the retrieved information. Machine learning algorithms are applied in this process. 【0721】 Step 7: 【0722】 The server uses natural language generation technology to create responses to the user based on personalized information. The information is organized according to the user's emotions, resulting in relatable content. 【0723】 Step 8: 【0724】 The server sends the generated response to the terminal. The terminal displays this response on its screen and presents it to the user. 【0725】 Step 9: 【0726】 The user reviews the provided information and decides on an action that suits their needs. If further information is needed, they can restart the process from step 1. 【0727】 (Example 2) 【0728】 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". 【0729】 Modern information providers often present information without considering the user's specific situation or emotional state, resulting in a problem where the information provided fails to adequately meet the user's true needs. Furthermore, they struggle to fully meet users' expectations for real-time, immediate information delivery. 【0730】 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. 【0731】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing technology, means for determining the user's emotional state based on the analysis results, and means for acquiring necessary data based on the determined emotional state. This enables the provision of more personalized information according to the user's emotions and enables the efficient provision of timely information. 【0732】 A "user" is an entity that uses a system to obtain information or input requests. 【0733】 A "request" is an instruction from a user to the system to obtain the information or action they need. 【0734】 "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and manipulate human language. 【0735】 "Analysis" is the process of breaking down information and understanding its meaning and intent. 【0736】 "Emotional state" refers to the emotional tone and nuances extracted from the user's request. 【0737】 "Data" refers to a collection of numbers, characters, symbols, and other elements used to represent specific information. 【0738】 "Acquisition" refers to the act of selecting necessary data and information and incorporating them into a system. 【0739】 "Generative technology" refers to the techniques that computers use to create natural-sounding text and information. 【0740】 "User's past usage history" refers to a record of information and actions recorded when a user uses the system. 【0741】 "Current data from external sources" refers to the latest data obtained from external information sources outside the system. 【0742】 This system provides information based on user input, and therefore basically operates with a configuration centered around terminals, servers, and communication networks. 【0743】 The user first uses their device to enter a request for specific information. For example, this might occur if the user types "Tell me about events where I can relax" into their device. This request is then sent to the server via the internet. 【0744】 The server is equipped with software that implements natural language processing technology. This technology analyzes requests sent by users and interprets what they mean. Furthermore, the server has an emotion engine that analyzes the user's emotional state based on the content of the request. Based on this emotional state, the server retrieves more appropriate data. 【0745】 The server quickly retrieves the necessary data through database access technologies and APIs. The retrieved data is then personalized, taking into account the user's past history and emotional state. Generative technologies are then used to organize the information into a user-friendly format. This information is finally transmitted to the terminal via the internet and presented to the user. 【0746】 As a concrete example, consider a scenario where a user requests, "Recommend some movies I can enjoy this weekend." The server analyzes this request using natural language processing technology, taking into account factors such as movie genre and time slot, and further understands the underlying emotions behind the request through an emotion engine. Based on the data obtained, the server selects the most suitable movie information for the user and returns a generated message to the device. A natural response such as, "Here's a recommended comedy movie to help you relax this weekend: XX," would be displayed. 【0747】 A concrete example of a prompt for a generative AI model might be, "If someone is on vacation and looking for a travel destination to relieve stress, what suggestions should I give them?" This prompt requests the AI ​​model to provide information based on user attributes and context. 【0748】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0749】 Step 1: 【0750】 The user enters a request for specific information into the terminal in text format. This request might be in the format of "Tell me about events that can help me relax." The entered text is sent to the server via the internet. 【0751】 Step 2: 【0752】 The server analyzes received requests using natural language processing techniques. The input is the request text from the user, and the output is structured data that captures the intent of the request. The server then performs morphological analysis and intent identification on this structured data. 【0753】 Step 3: 【0754】 The server analyzes the request content using an emotion engine to determine the user's emotional state. The input is structured data, and the output is emotional state data that includes emotional tone and nuances. The emotion engine determines emotions from keywords and phrases in the text. 【0755】 Step 4: 【0756】 The server retrieves necessary information from databases and external APIs based on emotional state data and request content. The input is emotional state and request content, and the output is information appropriate to those inputs. The server accesses databases using efficient queries and collects external information through public APIs. 【0757】 Step 5: 【0758】 The server personalizes the acquired information based on the user's past history and emotional state. The input is the acquired information, and the output is the personalized information. Here, specific patterns and trends from the user's history are taken into consideration, and the priority of the information is changed accordingly. 【0759】 Step 6: 【0760】 The server converts personalized information into an understandable format using natural language generation technology. The input is personalized information, and the output is text generated for the user. The server uses templates to create a natural sentence structure. 【0761】 Step 7: 【0762】 The server sends the final sentence to the terminal via the internet. The input is a sentence generated in natural language, and the output is a text response to the terminal. 【0763】 Step 8: 【0764】 The terminal displays the text response received from the server in the user interface. The input is the text received from the server, and the output is visually presented information. Through this, the user can verify the necessary information. 【0765】 (Application Example 2) 【0766】 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". 【0767】 For the elderly and those requiring care, it is essential to constantly monitor their emotional state and provide appropriate information and support accordingly. However, conventional information provision systems often fail to consider the user's emotions and remain uniform in their information delivery. As a result, necessary support and appropriate information are not provided in a timely manner, leading to decreased user satisfaction. 【0768】 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. 【0769】 In this invention, the server includes means for receiving a user request and analyzing it using natural language processing, means for obtaining necessary information based on the analysis results, and means for analyzing the user's emotions and personalizing the response based on those emotions. This makes it possible to provide personalized responses and information that take into account the user's emotional state. 【0770】 "Means of receiving user requests and analyzing them using natural language processing" refers to techniques that obtain inquiries and instructions from users and analyze text data in order to interpret them. 【0771】 "Means of obtaining necessary information based on the analyzed results" refers to techniques that retrieve relevant information from databases or external sources based on results obtained through natural language processing. 【0772】 "Means of organizing and presenting information in a way that is optimal for the user" refers to technologies that organize acquired information according to its purpose and situation, and present it in a way that is easy for the user to understand. 【0773】 "Means of analyzing user emotions and personalizing responses based on those emotions" refers to technology that identifies the user's emotional state and generates individually tailored responses based on the results of that identification. 【0774】 "A means of acquiring real-time data from external sources and providing users with immediate information and responses tailored to their emotions" refers to a technology that instantly incorporates the latest information from external sources and uses it to create immediate responses that match the user's emotions. 【0775】 This system has the ability to effectively analyze user requests and personalize responses by providing an interface equipped with emotion recognition technology. The system consists of a server with emotion recognition capabilities and a terminal that enables interaction with the user. The terminal receives a voice or text request from the user and initiates a series of processes. 【0776】 The server first uses a natural language processing engine to analyze the user's request. The software used here includes natural language processing libraries such as spaCy and NLTK. This allows the content of the text to be understood, and the next stage is sentiment analysis. The server's sentiment analysis engine uses tools such as the Google Cloud Natural Language API to analyze the user's emotional tone in detail. 【0777】 Once the user's emotions are identified, the server retrieves appropriate information from a database or external source based on the results. A system like MySQL can be used for database management. Next, a natural language generation engine, such as the OpenAI GPT model, is used to generate a personalized response. This response is sent to the terminal and presented to the user in an appropriate format. 【0778】 As a concrete example, if an elderly person in a nursing home says, "I want to relax today," the device sends that request to the server. If the emotion analysis determines that the person is seeking relaxation, the server can generate suggestions for calming music or simple relaxation exercises and present them to the user. 【0779】 An example of a prompt for a generative AI model is, "If the user is seeking relaxation, generate personalized relaxation content." 【0780】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0781】 Step 1: 【0782】 The device receives a voice or text request from the user. The voice data obtained as input is converted into text data using speech recognition technology. The text obtained in this process forms the basis for the next analysis. 【0783】 Step 2: 【0784】 The server analyzes the text data using a natural language processing engine (e.g., spaCy or NLTK). Here, morphological and syntactic analysis are performed to analyze the meaning of the input text and understand its content. The server receives the output, which includes the text's structure and semantic information. 【0785】 Step 3: 【0786】 The server uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to evaluate the emotional tone of the analyzed text. Emotion analysis analyzes language patterns and nuances to identify the user's emotional state (e.g., joy, sadness, stress). The result of the emotional state assessment is then output. 【0787】 Step 4: 【0788】 The server retrieves relevant information from databases and external sources based on the user's emotional state. Input includes the results of the emotional analysis and the user's profile information. Output provides relaxation content and suggestions tailored to the user. 【0789】 Step 5: 【0790】 The server uses a generative AI model (e.g., the OpenAI GPT model) to generate personalized responses. The input consists of acquired information and sentiment analysis results. The generative AI model uses this to create response text in natural language, providing specific suggestions and information as output. 【0791】 Step 6: 【0792】 The device presents the response sent from the server to the user. Presentation methods include text display, text-to-speech, and playback of visual content. This allows the user to receive information and suggestions optimized for their emotions. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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." 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 The following is further disclosed regarding the embodiments described above. 【0815】 (Claim 1) 【0816】 A means of receiving user requests and analyzing them using natural language processing, 【0817】 Means for obtaining necessary information based on the analyzed results, 【0818】 A means of organizing and presenting information in a way that is optimal for the user, 【0819】 A system that includes this. 【0820】 (Claim 2) 【0821】 The system according to claim 1, further comprising means for referencing the user's past history and personalizing the information. 【0822】 (Claim 3) 【0823】 The system according to claim 1, further comprising means for acquiring real-time data from an external source and providing immediate information to the user. 【0824】 "Example 1" 【0825】 (Claim 1) 【0826】 A means for receiving user requests and analyzing them using language processing techniques, 【0827】 Means for obtaining necessary information based on the analyzed results, 【0828】 A means of organizing and presenting information in a way that is suitable for the user, 【0829】 A means of sending information to a server via a terminal and providing personalized information to each user, 【0830】 A means of selecting and reconstructing information using algorithms, 【0831】 A system that includes this. 【0832】 (Claim 2) 【0833】 The system according to claim 1, further comprising means for referring to a user's past activity history and personalizing the information. 【0834】 (Claim 3) 【0835】 The system according to claim 1, further comprising means for acquiring real-world data from an external source and providing the information to the user immediately. 【0836】 "Application Example 1" 【0837】 (Claim 1) 【0838】 A means of receiving user requests and analyzing them using natural language processing, 【0839】 Means for obtaining necessary information based on the analyzed results, 【0840】 A means of organizing and presenting information in a way that is optimal for the user, 【0841】 A means of receiving information through voice input and converting it into text using speech recognition technology, 【0842】 A means of providing acquired information in real time, either by voice or visually, 【0843】 A system that includes this. 【0844】 (Claim 2) 【0845】 The system according to claim 1, further comprising means for referencing the user's past history and personalizing the information. 【0846】 (Claim 3) 【0847】 The system according to claim 1, further comprising means for acquiring real-time data from an external source and providing immediate information to the user. 【0848】 "Example 2 of combining an emotion engine" 【0849】 (Claim 1) 【0850】 A means of receiving user requests and analyzing them using natural language processing technology, 【0851】 A means of determining the user's emotional state based on the analyzed results, 【0852】 A means of obtaining necessary data based on the determined emotional state, 【0853】 A means of organizing data according to the user's emotions and presenting it using generation technology, 【0854】 An information-providing device that includes [this]. 【0855】 (Claim 2) 【0856】 The information providing device according to claim 1, further comprising means for referring to a user's past usage history and personalizing the information. 【0857】 (Claim 3) 【0858】 The information providing device according to claim 1, further comprising means for acquiring current data from an external source and providing timely information to the user. 【0859】 "Application example 2 when combining with an emotional engine" 【0860】 (Claim 1) 【0861】 A means of receiving user requests and analyzing them using natural language processing, 【0862】 Means for obtaining necessary information based on the analyzed results, 【0863】 A means of analyzing user emotions and personalizing responses based on those emotions, 【0864】 A means of organizing and presenting information in a way that is optimal for the user, 【0865】 A system that includes this. 【0866】 (Claim 2) 【0867】 The system according to claim 1, further comprising means for referencing the user's past history and personalizing responses based on information and emotion. 【0868】 (Claim 3) 【0869】 The system according to claim 1, further comprising means for acquiring real-time data from an external source and providing the user with immediate information and a response appropriate to their emotions. [Explanation of symbols] 【0870】 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 means of receiving user requests and analyzing them using natural language processing, Means for obtaining necessary information based on the analyzed results, A means of organizing and presenting information in a way that is optimal for the user, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for referencing the user's past history and personalizing the information. [Claim 3] The system according to claim 1, further comprising means for acquiring real-time data from an external source and providing immediate information to the user.

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