system

Abstract

We provide the system. [Solution] Means for setting up multiple information processing devices, means for acquiring a plurality of responses generated by the information processing device, Means for comparing and evaluating the aforementioned multiple responses, A means of selecting the most appropriate response based on the evaluation results, A means of providing the selected response to 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In recent years, many companies have been providing information using generative AI, but there are concerns about the reliability and accuracy of the generated responses. In particular, for companies that provide large-scale technical support, responses based on incorrect information can be a factor in reducing customer satisfaction. Also, manual response confirmation may lead to waste of time and resources. The present invention aims to solve these problems and improve the business efficiency of companies by providing information quickly and accurately. 【Means for Solving the Problems】 【0005】 This invention provides a system that uses multiple information processing devices to acquire the responses they generate, compare them with each other, and evaluate them. Based on the evaluation results, the most appropriate response is selected and provided to the user. In particular, the evaluation means is equipped with a function to evaluate the reliability by comparing it with similar responses from the past, thereby increasing the reliability of the response. Furthermore, the information processing devices use natural language processing technology to analyze inquiries from users and generate accurate and appropriate responses. This configuration enables the provision of information quickly and reliably. 【0006】 An "information processing device" is a system of hardware or software that can receive, process, and perform specific functions based on data. 【0007】 A "response" is the answer or result that an information processing device generates in response to an external inquiry. 【0008】 "Comparison" is the act of comparing two or more objects and judging their similarities and differences. 【0009】 "Evaluation" is the act of judging the value and reliability of a subject or data, and deriving results based on numerical or qualitative criteria. 【0010】 "Selection" is the act of choosing the most appropriate option from among the given choices. 【0011】 "Providing" means showing or sharing the selected results or information with other users or systems. 【0012】 "Natural language processing technology" refers to computer science-based technologies for understanding, generating, and responding to human language. 【0013】 "Trustworthiness" is a criterion for judging how reliable a piece of information or response can be. [Brief explanation of the drawing] 【0014】 [Figure 1] It 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】 【0015】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 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). 【0021】 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." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 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. 【0025】 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). 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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". 【0035】 This invention provides a highly reliable information system by using multiple information processing devices and comparing and evaluating the responses they generate. This system improves operational efficiency by receiving user inquiries and providing quick and accurate responses. Furthermore, it enhances the reliability of responses by utilizing an internal database and past response history. 【0036】 The server first receives inquiries from users. Users use their terminals to input problems or questions in natural language and send them to the server. The server analyzes the received inquiries using natural language processing techniques and extracts keywords and context. Based on these analysis results, the server forwards the inquiries to multiple information processing devices, i.e., AI agents. Each AI agent uses its own algorithm to generate a response based on the analysis results. 【0037】 The multiple responses generated are collected again by the server. The server then compares these responses with each other and evaluates them by cross-referencing them with past response history. This evaluation process is crucial for quantifying confidence and selecting the most reliable response. The selected response is returned to the user's terminal and displayed on the screen. 【0038】 For example, if a user inquires, "I'm having trouble with my network connection," the server sends this inquiry to multiple AI agents. The AI ​​agents generate responses such as "Check your cables" or "Restart your router." The server then evaluates these responses, and if it determines from past data that "Check your cables" is appropriate, it provides that response to the user. 【0039】 This system enables engineers and technical support staff to respond quickly based on reliable information, significantly improving work efficiency. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The user uses a terminal to input questions or problems in natural language. The terminal converts the input data into a structured format and sends it to the server. 【0043】 Step 2: 【0044】 The server analyzes the queries received from users. Natural language processing techniques are used for the analysis to extract important keywords and context. This clarifies the intent of the query, making subsequent processing easier. 【0045】 Step 3: 【0046】 The server forwards the query to multiple AI agents based on the analysis results. The server creates an API request for each AI agent and sends the query data. 【0047】 Step 4: 【0048】 Each AI agent processes the received query and generates a response using its own algorithm. The generated response is then sent to the server. 【0049】 Step 5: 【0050】 The server collects and evaluates the responses returned by each AI agent. This evaluation includes comparing responses with each other and matching them against past response history. This allows the confidence level of each response to be quantified. 【0051】 Step 6: 【0052】 The server selects the most reliable response based on the evaluation results. The selected response is sent to the terminal in preparation for providing it to the user. 【0053】 Step 7: 【0054】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve the problem. 【0055】 (Example 1) 【0056】 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." 【0057】 In modern information processing systems, providing quick and accurate responses to user inquiries is crucial. However, when responses from diverse sources are inconsistent, selecting the most reliable answer becomes difficult. This can lead to decreased operational efficiency and reduced user satisfaction. Therefore, there is a need for systems that can provide highly reliable responses to users. 【0058】 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. 【0059】 In this invention, the server includes means for receiving inquiries from users and analyzing them using natural language processing technology, means for forwarding inquiries to multiple information processing devices based on the analysis results, and means for obtaining multiple responses generated by the information processing devices. This makes it possible to compare and evaluate various responses to user inquiries and quickly provide the optimal response based on reliability. 【0060】 "User inquiries" refer to the act of users of an information system expressing their questions or problems in natural language and requesting a response from the system. 【0061】 "Natural language processing technology" refers to the technology that enables computers to understand and analyze natural language, which is human language, and includes functions for analyzing the meaning and context of text. 【0062】 "Analysis results" refer to keywords and contextual information obtained by analyzing the content of inquiries received from users using natural language processing technology. 【0063】 An "information processing device" refers to an electronic device or program that has the ability to perform calculations and decisions based on received data and generate a response. 【0064】 "Response reliability" refers to a numerical value or indicator that shows how accurate and helpful each generated response is in response to a user's inquiry. 【0065】 "Response history" refers to a record of responses that the system has generated and provided to users in the past, and is data used to evaluate the reliability of future responses. 【0066】 This invention provides an information processing system that accurately and quickly processes user inquiries. This system utilizes multiple information processing devices and selects the most appropriate response by comparing and evaluating the responses generated by each device. Furthermore, it aims to provide users with more reliable information by utilizing past response history. 【0067】 The server receives inquiries from users and analyzes the content of those inquiries using natural language processing technology. In this process, software such as Python's NLTK and SpaCy are used to extract keywords and context. The analyzed results are then transferred to multiple information processing devices, i.e., AI agents, each possessing a generative AI model. These AI agents generate responses based on the analysis results. A possible example of a specific AI model is the GPT series, which is a generative AI model. 【0068】 The generated responses are collected by the server, which evaluates them and compares them against past response history. This evaluation includes quantifying confidence levels using methods such as TF-IDF and clustering. The response with the highest confidence level is selected and displayed on the user's terminal. 【0069】 As a concrete example, consider a case where a user inquires, "My smartphone battery drains very quickly." In this case, the server sends the inquiry to an AI agent, which generates different responses such as "Please close background apps" or "Please lower the screen brightness." The server evaluates these responses and determines that "Please close background apps" is appropriate, providing it to the user. 【0070】 An example of a prompt message might be, "Please tell me how to improve power management on my mobile device." 【0071】 This system improves operational efficiency by enabling engineers and technical support staff to provide quick and reliable responses to user inquiries. 【0072】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0073】 Step 1: 【0074】 The user uses a terminal to input a query in natural language and sends it to the server. The input here is the query content in text format. This input is sent as a dataset for the server to receive. The specific actions involved in this process include text input on the terminal and the transmission of that text as packet data over the network. 【0075】 Step 2: 【0076】 The server receives inquiries from users. The received data is then analyzed using natural language processing techniques. This analysis uses Python's NLTK and SpaCy to extract keywords and context from the text. The output of the analysis is a dataset containing keyword and contextual information. This dataset is used in the next processing step. 【0077】 Step 3: 【0078】 The server forwards the query to multiple information processing devices, i.e., AI agents, based on the analysis results. The input is the dataset of analysis results obtained in step 2. The server generates a prompt sentence for each AI agent and instructs them to generate a response using a generative AI model. The output is the response generated by each AI agent, which is returned to the server. 【0079】 Step 4: 【0080】 The server collects multiple responses received from each AI agent as input. It compares the collected responses and performs data calculations to evaluate their reliability. This evaluation includes quantifying reliability using methods such as TF-IDF and clustering. The output of the evaluation is a confidence score for each response, which is used in the subsequent selection procedure. 【0081】 Step 5: 【0082】 The server selects the most appropriate response based on the confidence score. The inputs here are the confidence score obtained in step 4 and the corresponding response. The selected response is used as the final output to the user. After selection, the chosen response is returned to the user's terminal. 【0083】 Step 6: 【0084】 The user's device receives a selected response sent from the server. This response is displayed to the user and used as a guide for the user's next action. The output consists of specific action instructions or solutions displayed on the device screen. 【0085】 (Application Example 1) 【0086】 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." 【0087】 In modern homes, there is a need for systems that can respond quickly and accurately to the various problems and questions users face. However, existing technologies have challenges in ensuring the reliability of information and the selection of appropriate responses to address the diverse situations within the home. As a result, users spend a lot of time obtaining the necessary information and are unable to effectively utilize the appliances and devices in their homes. 【0088】 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. 【0089】 In this invention, the server includes means for setting up multiple information processing devices, means for acquiring multiple responses generated by the information processing devices, means for comparing and evaluating the multiple responses, means for receiving voice or text input from a user, and means for analyzing the received input using natural language processing and extracting keywords and context. This enables a rapid and reliable response to the diverse needs of users within the home. 【0090】 An "information processing device" is an electronic device used for inputting, processing, and outputting data, and possesses functions for performing various calculations and analyses. 【0091】 A "response" is an answer or reaction generated by an information processing device in response to a user's inquiry, and is intended to meet the user's needs. 【0092】 "Evaluation" is the process of comparing multiple responses and determining which response is the most appropriate and reliable. 【0093】 "Natural language processing" is a technology that understands natural language input by users and extracts appropriate information, analyzing the intent and context of inquiries. 【0094】 An "artificial intelligence agent" is a program or system that performs analysis and judgment based on a specific algorithm and autonomously generates responses and suggestions. 【0095】 "Past response history" refers to a database that records previously provided responses and is referenced in the evaluation process. 【0096】 "Household machinery and equipment" is a general term for machines and electronic devices used within the home, possessing a variety of functions to support daily life. 【0097】 The system implementing this invention takes the form of meeting diverse user needs within the home using household machinery and equipment. It is mainly implemented using a server, an information processing device, and a user terminal. 【0098】 The server receives voice and text input from the user using microphones and cameras built into home appliances. The received input is analyzed using natural language processing to extract keywords and context. This analysis is performed using Google® Natural Language API or similar technologies. 【0099】 Based on the analysis results, the server forwards the query to multiple artificial intelligence agents. These agents use services such as IBM Watson® and AWS® Lex to generate responses using their own algorithms. The generated responses are collected by the server and evaluated. The evaluation uses past response history, and the reliability is determined by comparing them with similar cases in the database. 【0100】 The response deemed most reliable is provided to the user's device. The device includes a speaker and display, and the response is presented in either voice or text format. For example, if the user asks, "What should I make for dinner tonight?", the system can suggest dishes like "pasta," which have been popular in many households based on past history. An example of a prompt message to use would be, "Generate a highly reliable response to my dinner suggestion. I will consider past history and suggest the best dish." 【0101】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0102】 Step 1: 【0103】 The server uses the microphones and cameras of home appliances to receive voice or text input from the user. The input data is converted into digital text using speech recognition software. This provides text data of the user's questions and problems. 【0104】 Step 2: 【0105】 The server analyzes the received text data using natural language processing techniques. It utilizes tools such as the Google Natural Language API to extract keywords and context through the analysis. The input is text data, and the output is the analyzed keywords and contextual information. 【0106】 Step 3: 【0107】 The server sends queries to multiple artificial intelligence agents based on keywords and contextual information obtained from the analysis. AI services such as IBM Watson and AWS Lex are used, each generating responses using its own algorithm. Each AI agent receives analysis results as input and response candidates as output. 【0108】 Step 4: 【0109】 The server retrieves response candidates generated by each artificial intelligence agent and compares them against a database to track past response history. This comparison quantifies the confidence level of each response, and the server selects the response deemed most reliable. The input consists of response candidate data and past response history data, while the output is the response data with its reliability evaluated. 【0110】 Step 5: 【0111】 The server sends selected, reliable responses to the user's terminal, providing the response via voice or text through a speaker or display. This allows the user to quickly obtain solutions appropriate to their home environment. The input is reliability-rated response data, and the output is response information presented in a user-recognizable format. 【0112】 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. 【0113】 This invention is an information processing system that combines an emotion engine that recognizes user emotions, enabling not only rapid and accurate information delivery but also responses that take into account the user's emotional state. This makes it possible to more effectively meet user needs. 【0114】 Users input questions or problems in natural language through their devices and send them to the server. After receiving this query, the server first uses natural language processing techniques to analyze important keywords and context. Next, the server uses an emotion engine to detect the emotional state from the user's input text. This emotion engine is designed based on common natural language data and recognizes emotions such as joy, anger, and sadness from the text. 【0115】 The analyzed inquiry and sentiment information are sent to their respective information processing units, i.e., AI agents. The AI ​​agents generate responses based on the received information. In this process, sentiment information is also taken into consideration; for example, if the user is dissatisfied, the response may be generated using more considerate language. 【0116】 The server collects multiple responses generated by the AI ​​agent and compares and evaluates each response. This evaluation process incorporates emotional information, considering not only the accuracy of the information but also the impression the response makes on the user. The response deemed most reliable is selected and sent back to the user's device. 【0117】 For example, if a user makes an inquiry such as, "I'm unhappy because the service is delayed," the server can choose a response that includes "offering specific solutions" or "apologizing," allowing for an effective dialogue that alleviates the user's dissatisfaction. 【0118】 This system will enable engineers and technical support personnel to deliver high-quality service quickly through emotionally sensitive and reliable responses. 【0119】 The following describes the processing flow. 【0120】 Step 1: 【0121】 The user enters their inquiry into the terminal in natural language. The terminal sends this input to the server. At this stage, the user's text data enters the system. 【0122】 Step 2: 【0123】 The server analyzes the text data received from the user. It uses natural language processing techniques to extract context and keywords and structure the data. 【0124】 Step 3: 【0125】 The server sends text data to the emotion engine to identify the user's emotional state. The emotion engine analyzes emotions such as joy, anger, and sadness from the text and returns corresponding emotional information to the server. 【0126】 Step 4: 【0127】 The server sends data to multiple AI agents based on the analyzed query content and sentiment information. Each AI agent generates a response based on the data it receives. 【0128】 Step 5: 【0129】 The responses generated by each AI agent are collected by a server. The server compares and evaluates the responses against each other. This evaluation includes checking not only the accuracy of the information but also whether the response is appropriate to the user's emotional state. 【0130】 Step 6: 【0131】 The server selects the most reliable response based on the evaluation results, which best matches the user's emotions. Once the optimal response is selected, the server sends that response to the user's terminal. 【0132】 Step 7: 【0133】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve their own problem. 【0134】 (Example 2) 【0135】 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." 【0136】 Modern information processing systems are required to provide rapid and accurate responses that take user emotions into consideration. However, conventional systems have struggled to generate responses that appropriately reflect emotions. In particular, the lack of technology to accurately detect emotions from user input and select appropriate responses based on them has resulted in a failure to improve user satisfaction. 【0137】 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. 【0138】 In this invention, the server includes means for analyzing user input information, means for detecting emotional states from the analyzed text information, and means for setting up a plurality of information processing devices. This makes it possible to provide accurate and appropriate responses that take into account the user's emotions. 【0139】 "User" refers to a person who uses the system to input information or receive responses. 【0140】 "Input information" refers to text data, including questions and requests, that users provide to the system. 【0141】 "Analysis" refers to the process of processing input information to extract meaning, keywords, and context. 【0142】 "Emotional state" refers to emotional states such as joy, anger, and sadness that can be inferred from the user's input information. 【0143】 An "information processing device" refers to a computer system used to process data and generate responses. 【0144】 "Evaluation" refers to the process of comparing multiple generated responses and determining their reliability and appropriateness. 【0145】 "Selection" refers to the process of determining the most appropriate response based on the evaluation results. 【0146】 "Response" refers to the answer or message that an information processing device generates and provides to the user. 【0147】 This invention provides an information response system that takes user emotions into consideration. The system mainly consists of a user terminal, a server, and an AI agent. The user inputs inquiries in natural language using their own terminal and sends them to the server. In this system, smartphones and personal computers are often used as user terminals. 【0148】 The server analyzes incoming queries using natural language processing (NLP) techniques to extract keywords and context. Libraries such as spaCy and NLTK are used for this analysis. The server then uses an emotion engine to detect the user's emotional state from the text. This process utilizes tools such as TextBlob and the Google Cloud Natural Language API. 【0149】 After analysis and emotion detection are complete, the server sends this information to the AI ​​agent. The AI ​​agent uses a generative AI model based on the received information to generate a response that takes the user's emotions into consideration. In this response generation process, OpenAI's GPT-3® is used as the generative AI model. The AI ​​agent evaluates the input data and the multiple generated responses and selects the most appropriate one. 【0150】 The selected response is returned to the server and finally sent to the user's terminal. This allows the user to receive an emotionally sensitive response. For example, if a user makes an inquiry such as, "I'm unhappy because the service is slow," the system can detect this dissatisfaction and provide a response that includes an appropriate apology and solution. 【0151】 An example of a prompt message would be: "Identify the user's sentiment based on their text and generate a response that takes that sentiment into consideration. For example, in response to a complaint such as 'The service is slow,' generate a response that offers a sincere apology along with a relevant solution." 【0152】 This allows the system to respond quickly and accurately to user needs, thereby increasing user satisfaction. 【0153】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0154】 Step 1: 【0155】 The user uses a terminal to input and submit questions or problems in natural language. On the terminal, the user types text into the input field and presses the submit button, sending that text data to the server. The input is the user's text data, and the output is that text sent to the server. 【0156】 Step 2: 【0157】 The server retrieves text data received from the user and performs natural language processing (NLP). Specifically, it uses an NLP library to perform semantic analysis, keyword extraction, and contextual understanding of the text. The input is the user's text data, and the output is the analyzed keywords and contextual information. In this process, the server converts the information within the text into structured data. 【0158】 Step 3: 【0159】 The server uses an emotion engine to detect emotions from the parsed text data. The emotion engine categorizes the text into specific emotion categories (e.g., joy, anger, sadness). In this process, the input is the parsed text data, and the output is data indicating the user's emotional state. Specifically, it uses a statistical model to infer emotions based on words and phrases within the text. 【0160】 Step 4: 【0161】 The server sends structured keywords, contextual information, and sentiment to the AI ​​agent. The AI ​​agent uses this data to set prompts for generating appropriate responses. The input is analytical information generated from text, which is then converted into prompt data used by the AI ​​agent. 【0162】 Step 5: 【0163】 The AI ​​agent uses a generative AI model to generate responses that take into account the user's emotional state. It takes a prompt as input to the generative AI model and processes it to obtain the optimal response. The input is the prompt, and the output is the generated response text. The AI ​​agent generates responses considering the tone and wording appropriate to the user's emotions. 【0164】 Step 6: 【0165】 When the server receives multiple responses from the AI ​​agent, it compares and evaluates them. Here, data evaluation is performed to select the most appropriate response from the generated responses. The input is the multiple generated responses, and the output is the optimal response selected based on the evaluation results. This step considers the accuracy of the response and the emotionally appropriate tone of speech. 【0166】 Step 7: 【0167】 The server sends the selected optimal response to the user's terminal, where it is displayed. The input is the selected response text, and the output is the response displayed on the user's terminal. This allows the user to receive emotionally sensitive information and obtain a satisfactory response. 【0168】 (Application Example 2) 【0169】 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". 【0170】 Conventional information processing systems often provide uniform responses regardless of the user's emotional state, making it challenging to improve user satisfaction and trust. Furthermore, while there is a need to generate quick and emotionally sensitive responses to user inquiries, achieving this remains difficult. 【0171】 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. 【0172】 In this invention, the server includes means for setting up multiple data processing devices, means for analyzing user input information using an emotion recognition engine that determines the user's emotional state, and means for generating an emotion-sensitive response using a generative AI model based on the user's emotional state. This makes it possible to provide a more appropriate and satisfying response that is in line with the user's emotions. 【0173】 A "data processing device" is a device that has the function of analyzing information and generating a response based on that analysis. 【0174】 An "emotion recognition engine" is a technology that analyzes the emotional state from information input by the user and generates data accordingly. 【0175】 A "generative AI model" is an algorithm that learns from a large amount of data and generates an appropriate response based on the input information. 【0176】 "Natural language processing technology" is a computer technology used to understand and analyze human language, including extracting context and keywords. 【0177】 "Confidence level" is an index used to evaluate the accuracy and appropriateness of the generated response. 【0178】 This invention provides a system for accurately analyzing user input information and providing responses that take emotions into consideration. The specific implementation method is described below. 【0179】 First, the user makes a query using natural language on a device such as a smartphone. This device sends the query to the server. The server receives this query and begins processing the data using natural language processing technology. The software used here is spaCy, a natural language processing library. spaCy is used to analyze the context and keywords of the input data. 【0180】 Next, the server uses an emotion recognition engine to determine the user's emotional state. Hugging Face's Transformers are used as the emotion recognition engine. Transformers are used to detect emotions such as joy, anger, and sadness from text. Based on this emotional information, the server generates a response using a generative AI model. OpenAI's GPT is suitable as the generative AI model. A system using this model outputs a considerate response appropriate to the user's emotional state. 【0181】 Ultimately, the server compares the multiple responses it generates and selects the most appropriate one based on criteria such as reliability and emotional consideration. The selected response is then sent back to the user's device. As a result, the user receives an emotionally appropriate response. 【0182】 For example, if a user inquires that their ordered item is late and causing them inconvenience, the system will first investigate the cause of the delay and then generate a response such as, "We apologize for the inconvenience. We will address the issue as soon as possible. We will also send you a discount coupon for your next purchase." In this way, the present invention goes beyond mere information provision to realize a service that gives users peace of mind. 【0183】 Examples of prompt statements include: 【0184】 "Recognize the user's emotions and generate an appropriate response: If the user says '___', how should you respond?" 【0185】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0186】 Step 1: 【0187】 The user uses a terminal to input a query in natural language and sends it from the terminal to the server. The input is the user's text data, and the output is the query data sent to the server. This data is then subjected to further analysis and processing. 【0188】 Step 2: 【0189】 The server parses the received query data using spaCy, a natural language processing library. The input is the user's query data, and the output is parsed data including context and keywords. Based on this data, the server prepares for sentiment recognition in the next step. 【0190】 Step 3: 【0191】 The server uses the analyzed data to run the Hugging Face Transformers emotion recognition engine and determine the user's emotions. The input is analyzed text data, and the output is emotion data, including emotion identification. Based on the emotion data obtained here, the server identifies the emotional state to be used for response generation. 【0192】 Step 4: 【0193】 The server utilizes OpenAI's GPT generative AI model to generate responses that take sentiment data into account. The input consists of sentiment data and parsed text, and the output is the generated response text. This generated response is designed to reflect the user's emotional state. 【0194】 Step 5: 【0195】 The server collects multiple generated responses, compares them, and evaluates them. The input is the multiple generated response texts, and the output is the optimal response selected based on the evaluation. Here, the server uses indicators such as trustworthiness and consideration of emotions to determine the most appropriate response. 【0196】 Step 6: 【0197】 The server sends the selected optimal response to the user's terminal. The input is the optimal response text, and the output is the response message displayed on the user's terminal. By receiving this response, the user can receive emotionally sensitive support. 【0198】 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. 【0199】 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. 【0200】 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. 【0201】 [Second Embodiment] 【0202】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0203】 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. 【0204】 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). 【0205】 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. 【0206】 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. 【0207】 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). 【0208】 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. 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 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". 【0214】 This invention provides a highly reliable information system by using multiple information processing devices and comparing and evaluating the responses they generate. This system improves operational efficiency by receiving user inquiries and providing quick and accurate responses. Furthermore, it enhances the reliability of responses by utilizing an internal database and past response history. 【0215】 The server first receives inquiries from users. Users use their terminals to input problems or questions in natural language and send them to the server. The server analyzes the received inquiries using natural language processing techniques and extracts keywords and context. Based on these analysis results, the server forwards the inquiries to multiple information processing devices, i.e., AI agents. Each AI agent uses its own algorithm to generate a response based on the analysis results. 【0216】 The multiple responses generated are collected again by the server. The server then compares these responses with each other and evaluates them by cross-referencing them with past response history. This evaluation process is crucial for quantifying confidence and selecting the most reliable response. The selected response is returned to the user's terminal and displayed on the screen. 【0217】 For example, if a user inquires, "I'm having trouble with my network connection," the server sends this inquiry to multiple AI agents. The AI ​​agents generate responses such as "Check your cables" or "Restart your router." The server then evaluates these responses, and if it determines from past data that "Check your cables" is appropriate, it provides that response to the user. 【0218】 This system enables engineers and technical support staff to respond quickly based on reliable information, significantly improving work efficiency. 【0219】 The following describes the processing flow. 【0220】 Step 1: 【0221】 The user uses a terminal to input questions or problems in natural language. The terminal converts the input data into a structured format and sends it to the server. 【0222】 Step 2: 【0223】 The server analyzes the queries received from users. Natural language processing techniques are used for the analysis to extract important keywords and context. This clarifies the intent of the query, making subsequent processing easier. 【0224】 Step 3: 【0225】 The server forwards the query to multiple AI agents based on the analysis results. The server creates an API request for each AI agent and sends the query data. 【0226】 Step 4: 【0227】 Each AI agent processes the received query and generates a response using its own algorithm. The generated response is then sent to the server. 【0228】 Step 5: 【0229】 The server collects and evaluates the responses returned by each AI agent. This evaluation includes comparing responses with each other and matching them against past response history. This allows the confidence level of each response to be quantified. 【0230】 Step 6: 【0231】 The server selects the most reliable response based on the evaluation results. The selected response is sent to the terminal in preparation for providing it to the user. 【0232】 Step 7: 【0233】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve the problem. 【0234】 (Example 1) 【0235】 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." 【0236】 In modern information processing systems, providing quick and accurate responses to user inquiries is crucial. However, when responses from diverse sources are inconsistent, selecting the most reliable answer becomes difficult. This can lead to decreased operational efficiency and reduced user satisfaction. Therefore, there is a need for systems that can provide highly reliable responses to users. 【0237】 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. 【0238】 In this invention, the server includes means for receiving inquiries from users and analyzing them using natural language processing technology, means for forwarding inquiries to multiple information processing devices based on the analysis results, and means for obtaining multiple responses generated by the information processing devices. This makes it possible to compare and evaluate various responses to user inquiries and quickly provide the optimal response based on reliability. 【0239】 "User inquiries" refer to the act of users of an information system expressing their questions or problems in natural language and requesting a response from the system. 【0240】 "Natural language processing technology" refers to the technology that enables computers to understand and analyze natural language, which is human language, and includes functions for analyzing the meaning and context of text. 【0241】 "Analysis results" refer to keywords and contextual information obtained by analyzing the content of inquiries received from users using natural language processing technology. 【0242】 An "information processing device" refers to an electronic device or program that has the ability to perform calculations and decisions based on received data and generate a response. 【0243】 "Response reliability" refers to a numerical value or indicator that shows how accurate and helpful each generated response is in response to a user's inquiry. 【0244】 "Response history" refers to a record of responses that the system has generated and provided to users in the past, and is data used to evaluate the reliability of future responses. 【0245】 This invention provides an information processing system that accurately and quickly processes user inquiries. This system utilizes multiple information processing devices and selects the most appropriate response by comparing and evaluating the responses generated by each device. Furthermore, it aims to provide users with more reliable information by utilizing past response history. 【0246】 The server receives inquiries from users and analyzes the content of those inquiries using natural language processing technology. In this process, software such as Python's NLTK and SpaCy are used to extract keywords and context. The analyzed results are then transferred to multiple information processing devices, i.e., AI agents, each possessing a generative AI model. These AI agents generate responses based on the analysis results. A possible example of a specific AI model is the GPT series, which is a generative AI model. 【0247】 The generated responses are collected by the server, which evaluates them and compares them against past response history. This evaluation includes quantifying confidence levels using methods such as TF-IDF and clustering. The response with the highest confidence level is selected and displayed on the user's terminal. 【0248】 As a concrete example, consider a case where a user inquires, "My smartphone battery drains very quickly." In this case, the server sends the inquiry to an AI agent, which generates different responses such as "Please close background apps" or "Please lower the screen brightness." The server evaluates these responses and determines that "Please close background apps" is appropriate, providing it to the user. 【0249】 An example of a prompt message might be, "Please tell me how to improve power management on my mobile device." 【0250】 This system improves operational efficiency by enabling engineers and technical support staff to provide quick and reliable responses to user inquiries. 【0251】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0252】 Step 1: 【0253】 The user uses a terminal to input a query in natural language and sends it to the server. The input here is the query content in text format. This input is sent as a dataset for the server to receive. The specific actions involved in this process include text input on the terminal and the transmission of that text as packet data over the network. 【0254】 Step 2: 【0255】 The server receives inquiries from users. The received data is then analyzed using natural language processing techniques. This analysis uses Python's NLTK and SpaCy to extract keywords and context from the text. The output of the analysis is a dataset containing keyword and contextual information. This dataset is used in the next processing step. 【0256】 Step 3: 【0257】 The server forwards the query to multiple information processing devices, i.e., AI agents, based on the analysis results. The input is the dataset of analysis results obtained in step 2. The server generates a prompt sentence for each AI agent and instructs them to generate a response using a generative AI model. The output is the response generated by each AI agent, which is returned to the server. 【0258】 Step 4: 【0259】 The server collects multiple responses received from each AI agent as input. It compares the collected responses and performs data calculations to evaluate their reliability. This evaluation includes quantifying reliability using methods such as TF-IDF and clustering. The output of the evaluation is a confidence score for each response, which is used in the subsequent selection procedure. 【0260】 Step 5: 【0261】 The server selects the most appropriate response based on the confidence score. The inputs here are the confidence score obtained in step 4 and the corresponding response. The selected response is used as the final output to the user. After selection, the chosen response is returned to the user's terminal. 【0262】 Step 6: 【0263】 The user's device receives a selected response sent from the server. This response is displayed to the user and used as a guide for the user's next action. The output consists of specific action instructions or solutions displayed on the device screen. 【0264】 (Application Example 1) 【0265】 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." 【0266】 In modern homes, there is a need for systems that can respond quickly and accurately to the various problems and questions users face. However, existing technologies have challenges in ensuring the reliability of information and the selection of appropriate responses to address the diverse situations within the home. As a result, users spend a lot of time obtaining the necessary information and are unable to effectively utilize the appliances and devices in their homes. 【0267】 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. 【0268】 In this invention, the server includes means for setting up multiple information processing devices, means for acquiring multiple responses generated by the information processing devices, means for comparing and evaluating the multiple responses, means for receiving voice or text input from a user, and means for analyzing the received input using natural language processing and extracting keywords and context. This enables a rapid and reliable response to the diverse needs of users within the home. 【0269】 An "information processing device" is an electronic device used for inputting, processing, and outputting data, and possesses functions for performing various calculations and analyses. 【0270】 A "response" is an answer or reaction generated by an information processing device in response to a user's inquiry, and is intended to meet the user's needs. 【0271】 "Evaluation" is the process of comparing multiple responses and determining which response is the most appropriate and reliable. 【0272】 "Natural language processing" is a technology that understands natural language input by users and extracts appropriate information, analyzing the intent and context of inquiries. 【0273】 An "artificial intelligence agent" is a program or system that performs analysis and judgment based on a specific algorithm and autonomously generates responses and suggestions. 【0274】 "Past response history" refers to a database that records previously provided responses and is referenced in the evaluation process. 【0275】 "Household machinery and equipment" is a general term for machines and electronic devices used within the home, possessing a variety of functions to support daily life. 【0276】 The system implementing this invention takes the form of meeting diverse user needs within the home using household machinery and equipment. It is mainly implemented using a server, an information processing device, and a user terminal. 【0277】 The server receives voice and text input from the user using microphones and cameras built into home appliances. The received input is analyzed using natural language processing to extract keywords and context. This analysis is performed using Google Natural Language API or similar technologies. 【0278】 Based on the analysis results, the server forwards the query to multiple artificial intelligence agents. These agents use services such as IBM Watson and AWS Lex to generate responses using their own algorithms. The generated responses are collected by the server and evaluated. The evaluation uses past response history, and the reliability is determined by comparing them with similar cases in the database. 【0279】 The response evaluated as the most reliable is provided to the user's terminal. The terminal includes a speaker and a display, and the response is presented in the form of voice or text. As a specific example, when the user asks, "What should I cook for dinner today?", the system can propose a dish such as "pasta", which has received favorable reviews in many households based on past history. An example of the prompt sentence used is, "Please generate a highly reliable response to the dinner proposal. Considering the past history, we will propose the most suitable dish." 【0280】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0281】 Step 1: 【0282】 The server uses the microphone and camera of the household mechanical device to receive voice or text input from the user. The input data is converted into digital text using speech recognition software. As a result, text data of questions or problems from the user is obtained. 【0283】 Step 2: 【0284】 The server analyzes the received text data using natural language processing technology. By leveraging Google Natural Language API or the like, keywords and context are extracted through the analysis. The input is text data, and the output is the analyzed keywords and context information. 【0285】 Step 3: 【0286】 Based on the keywords and context information obtained from the analysis, the server sends inquiries to multiple artificial intelligence agents. Here, AI services such as IBM Watson and AWS Lex are used, and each generates a response using its own algorithm. The input received by each AI agent is the data of the analysis result, and the output is the data of the response candidates. 【0287】 Step 4: 【0288】 The server retrieves response candidates generated by each artificial intelligence agent and compares them against a database to track past response history. This comparison quantifies the confidence level of each response, and the server selects the response deemed most reliable. The input consists of response candidate data and past response history data, while the output is the response data with its reliability evaluated. 【0289】 Step 5: 【0290】 The server sends selected, reliable responses to the user's terminal, providing the response via voice or text through a speaker or display. This allows the user to quickly obtain solutions appropriate to their home environment. The input is reliability-rated response data, and the output is response information presented in a user-recognizable format. 【0291】 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. 【0292】 This invention is an information processing system that combines an emotion engine that recognizes user emotions, enabling not only rapid and accurate information delivery but also responses that take into account the user's emotional state. This makes it possible to more effectively meet user needs. 【0293】 Users input questions or problems in natural language through their devices and send them to the server. After receiving this query, the server first uses natural language processing techniques to analyze important keywords and context. Next, the server uses an emotion engine to detect the emotional state from the user's input text. This emotion engine is designed based on common natural language data and recognizes emotions such as joy, anger, and sadness from the text. 【0294】 The analyzed inquiry and sentiment information are sent to their respective information processing units, i.e., AI agents. The AI ​​agents generate responses based on the received information. In this process, sentiment information is also taken into consideration; for example, if the user is dissatisfied, the response may be generated using more considerate language. 【0295】 The server collects multiple responses generated by the AI ​​agent and compares and evaluates each response. This evaluation process incorporates emotional information, considering not only the accuracy of the information but also the impression the response makes on the user. The response deemed most reliable is selected and sent back to the user's device. 【0296】 For example, if a user makes an inquiry such as, "I'm unhappy because the service is delayed," the server can choose a response that includes "offering specific solutions" or "apologizing," allowing for an effective dialogue that alleviates the user's dissatisfaction. 【0297】 This system will enable engineers and technical support personnel to deliver high-quality service quickly through emotionally sensitive and reliable responses. 【0298】 The following describes the processing flow. 【0299】 Step 1: 【0300】 The user enters their inquiry into the terminal in natural language. The terminal sends this input to the server. At this stage, the user's text data enters the system. 【0301】 Step 2: 【0302】 The server analyzes the text data received from the user. It uses natural language processing techniques to extract context and keywords and structure the data. 【0303】 Step 3: 【0304】 The server sends text data to the emotion engine to identify the user's emotional state. The emotion engine analyzes emotions such as joy, anger, and sadness from the text and returns corresponding emotion information to the server. 【0305】 Step 4: 【0306】 Based on the analyzed inquiry content and emotion information, the server sends data to multiple AI agents. Each AI agent generates a response based on the received data. 【0307】 Step 5: 【0308】 Responses generated from each AI agent are collected by the server. The server compares the responses with each other and conducts an evaluation. This evaluation includes a process of confirming whether the response is suitable for the user's emotional state in addition to the accuracy of the information. 【0309】 Step 6: 【0310】 The server selects the most reliable response that matches the user's emotion from the evaluation results. Once the optimal response is selected, the server sends that response to the user's terminal. 【0311】 Step 7: 【0312】 The terminal displays the selected response received from the server to the user. The user can attempt to solve their own problem by referring to the presented information. 【0313】 (Example 2) 【0314】 Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0315】 Modern information processing systems are required to provide rapid and accurate responses that take user emotions into consideration. However, conventional systems have struggled to generate responses that appropriately reflect emotions. In particular, the lack of technology to accurately detect emotions from user input and select appropriate responses based on them has resulted in a failure to improve user satisfaction. 【0316】 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. 【0317】 In this invention, the server includes means for analyzing user input information, means for detecting emotional states from the analyzed text information, and means for setting up a plurality of information processing devices. This makes it possible to provide accurate and appropriate responses that take into account the user's emotions. 【0318】 "User" refers to a person who uses the system to input information or receive responses. 【0319】 "Input information" refers to text data, including questions and requests, that users provide to the system. 【0320】 "Analysis" refers to the process of processing input information to extract meaning, keywords, and context. 【0321】 "Emotional state" refers to emotional states such as joy, anger, and sadness that can be inferred from the user's input information. 【0322】 An "information processing device" refers to a computer system used to process data and generate responses. 【0323】 "Evaluation" refers to the process of comparing multiple generated responses and determining their reliability and appropriateness. 【0324】 "Selection" refers to the process of determining the most appropriate response based on the evaluation results. 【0325】 "Response" refers to the answer or message that an information processing device generates and provides to the user. 【0326】 This invention provides an information response system that takes user emotions into consideration. The system mainly consists of a user terminal, a server, and an AI agent. The user inputs inquiries in natural language using their own terminal and sends them to the server. In this system, smartphones and personal computers are often used as user terminals. 【0327】 The server analyzes incoming queries using natural language processing (NLP) techniques to extract keywords and context. Libraries such as spaCy and NLTK are used for this analysis. The server then uses an emotion engine to detect the user's emotional state from the text. This process utilizes tools such as TextBlob and the Google Cloud Natural Language API. 【0328】 After analysis and emotion detection are complete, the server sends this information to the AI ​​agent. The AI ​​agent uses a generative AI model based on the received information to generate a response that takes the user's emotions into consideration. In this response generation process, OpenAI's GPT-3 is used as the generative AI model. The AI ​​agent evaluates the input data and the multiple generated responses and selects the most appropriate one. 【0329】 The selected response is returned to the server and finally sent to the user's terminal. This allows the user to receive an emotionally sensitive response. For example, if a user makes an inquiry such as, "I'm unhappy because the service is slow," the system can detect this dissatisfaction and provide a response that includes an appropriate apology and solution. 【0330】 An example of a prompt message would be: "Identify the user's sentiment based on their text and generate a response that takes that sentiment into consideration. For example, in response to a complaint such as 'The service is slow,' generate a response that offers a sincere apology along with a relevant solution." 【0331】 This allows the system to respond quickly and accurately to user needs, thereby increasing user satisfaction. 【0332】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0333】 Step 1: 【0334】 The user uses a terminal to input and submit questions or problems in natural language. On the terminal, the user types text into the input field and presses the submit button, sending that text data to the server. The input is the user's text data, and the output is that text sent to the server. 【0335】 Step 2: 【0336】 The server retrieves text data received from the user and performs natural language processing (NLP). Specifically, it uses an NLP library to perform semantic analysis, keyword extraction, and contextual understanding of the text. The input is the user's text data, and the output is the analyzed keywords and contextual information. In this process, the server converts the information within the text into structured data. 【0337】 Step 3: 【0338】 The server uses an emotion engine to detect emotions from the parsed text data. The emotion engine categorizes the text into specific emotion categories (e.g., joy, anger, sadness). In this process, the input is the parsed text data, and the output is data indicating the user's emotional state. Specifically, it uses a statistical model to infer emotions based on words and phrases within the text. 【0339】 Step 4: 【0340】 The server sends structured keywords, contextual information, and sentiment to the AI ​​agent. The AI ​​agent uses this data to set prompts for generating appropriate responses. The input is analytical information generated from text, which is then converted into prompt data used by the AI ​​agent. 【0341】 Step 5: 【0342】 The AI ​​agent uses a generative AI model to generate responses that take into account the user's emotional state. It takes a prompt as input to the generative AI model and processes it to obtain the optimal response. The input is the prompt, and the output is the generated response text. The AI ​​agent generates responses considering the tone and wording appropriate to the user's emotions. 【0343】 Step 6: 【0344】 When the server receives multiple responses from the AI ​​agent, it compares and evaluates them. Here, data evaluation is performed to select the most appropriate response from the generated responses. The input is the multiple generated responses, and the output is the optimal response selected based on the evaluation results. This step considers the accuracy of the response and the emotionally appropriate tone of speech. 【0345】 Step 7: 【0346】 The server sends the selected optimal response to the user's terminal, where it is displayed. The input is the selected response text, and the output is the response displayed on the user's terminal. This allows the user to receive emotionally sensitive information and obtain a satisfactory response. 【0347】 (Application Example 2) 【0348】 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." 【0349】 Conventional information processing systems often provide uniform responses regardless of the user's emotional state, making it challenging to improve user satisfaction and trust. Furthermore, while there is a need to generate quick and emotionally sensitive responses to user inquiries, achieving this remains difficult. 【0350】 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. 【0351】 In this invention, the server includes means for setting up multiple data processing devices, means for analyzing user input information using an emotion recognition engine that determines the user's emotional state, and means for generating an emotion-sensitive response using a generative AI model based on the user's emotional state. This makes it possible to provide a more appropriate and satisfying response that is in line with the user's emotions. 【0352】 A "data processing device" is a device that has the function of analyzing information and generating a response based on that analysis. 【0353】 An "emotion recognition engine" is a technology that analyzes the emotional state from information input by the user and generates data accordingly. 【0354】 A "generative AI model" is an algorithm that learns from a large amount of data and generates an appropriate response based on the input information. 【0355】 "Natural language processing technology" is a computer technology used to understand and analyze human language, including extracting context and keywords. 【0356】 "Confidence level" is an index used to evaluate the accuracy and appropriateness of the generated response. 【0357】 This invention provides a system for accurately analyzing user input information and providing responses that take emotions into consideration. The specific implementation method is described below. 【0358】 First, the user makes a query using natural language on a device such as a smartphone. This device sends the query to the server. The server receives this query and begins processing the data using natural language processing technology. The software used here is spaCy, a natural language processing library. spaCy is used to analyze the context and keywords of the input data. 【0359】 Next, the server uses an emotion recognition engine to determine the user's emotional state. Hugging Face's Transformers are used as the emotion recognition engine. Transformers are used to detect emotions such as joy, anger, and sadness from text. Based on this emotional information, the server generates a response using a generative AI model. OpenAI's GPT is suitable as the generative AI model. A system using this model outputs a considerate response appropriate to the user's emotional state. 【0360】 Ultimately, the server compares the multiple responses it generates and selects the most appropriate one based on criteria such as reliability and emotional consideration. The selected response is then sent back to the user's device. As a result, the user receives an emotionally appropriate response. 【0361】 For example, if a user inquires that their ordered item is late and causing them inconvenience, the system will first investigate the cause of the delay and then generate a response such as, "We apologize for the inconvenience. We will address the issue as soon as possible. We will also send you a discount coupon for your next purchase." In this way, the present invention goes beyond mere information provision to realize a service that gives users peace of mind. 【0362】 Examples of prompt statements include: 【0363】 "Recognize the user's emotions and generate an appropriate response: If the user says '___', how should you respond?" 【0364】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0365】 Step 1: 【0366】 The user uses a terminal to input a query in natural language and sends it from the terminal to the server. The input is the user's text data, and the output is the query data sent to the server. This data is then subjected to further analysis and processing. 【0367】 Step 2: 【0368】 The server parses the received query data using spaCy, a natural language processing library. The input is the user's query data, and the output is parsed data including context and keywords. Based on this data, the server prepares for sentiment recognition in the next step. 【0369】 Step 3: 【0370】 The server uses the analyzed data to run the Hugging Face Transformers emotion recognition engine and determine the user's emotions. The input is analyzed text data, and the output is emotion data, including emotion identification. Based on the emotion data obtained here, the server identifies the emotional state to be used for response generation. 【0371】 Step 4: 【0372】 The server utilizes OpenAI's GPT generative AI model to generate responses that take sentiment data into account. The input consists of sentiment data and parsed text, and the output is the generated response text. This generated response is designed to reflect the user's emotional state. 【0373】 Step 5: 【0374】 The server collects multiple generated responses, compares them, and evaluates them. The input is the multiple generated response texts, and the output is the optimal response selected based on the evaluation. Here, the server uses indicators such as trustworthiness and consideration of emotions to determine the most appropriate response. 【0375】 Step 6: 【0376】 The server sends the selected optimal response to the user's terminal. The input is the optimal response text, and the output is the response message displayed on the user's terminal. By receiving this response, the user can receive emotionally sensitive support. 【0377】 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. 【0378】 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. 【0379】 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. 【0380】 [Third Embodiment] 【0381】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0382】 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. 【0383】 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). 【0384】 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. 【0385】 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. 【0386】 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). 【0387】 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. 【0388】 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. 【0389】 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. 【0390】 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. 【0391】 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. 【0392】 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". 【0393】 This invention provides a highly reliable information system by using multiple information processing devices and comparing and evaluating the responses they generate. This system improves operational efficiency by receiving user inquiries and providing quick and accurate responses. Furthermore, it enhances the reliability of responses by utilizing an internal database and past response history. 【0394】 The server first receives inquiries from users. Users use their terminals to input problems or questions in natural language and send them to the server. The server analyzes the received inquiries using natural language processing techniques and extracts keywords and context. Based on these analysis results, the server forwards the inquiries to multiple information processing devices, i.e., AI agents. Each AI agent uses its own algorithm to generate a response based on the analysis results. 【0395】 The multiple responses generated are collected again by the server. The server then compares these responses with each other and evaluates them by cross-referencing them with past response history. This evaluation process is crucial for quantifying confidence and selecting the most reliable response. The selected response is returned to the user's terminal and displayed on the screen. 【0396】 For example, if a user inquires, "I'm having trouble with my network connection," the server sends this inquiry to multiple AI agents. The AI ​​agents generate responses such as "Check your cables" or "Restart your router." The server then evaluates these responses, and if it determines from past data that "Check your cables" is appropriate, it provides that response to the user. 【0397】 This system enables engineers and technical support staff to respond quickly based on reliable information, significantly improving work efficiency. 【0398】 The following describes the processing flow. 【0399】 Step 1: 【0400】 The user uses a terminal to input questions or problems in natural language. The terminal converts the input data into a structured format and sends it to the server. 【0401】 Step 2: 【0402】 The server analyzes the queries received from users. Natural language processing techniques are used for the analysis to extract important keywords and context. This clarifies the intent of the query, making subsequent processing easier. 【0403】 Step 3: 【0404】 The server forwards the query to multiple AI agents based on the analysis results. The server creates an API request for each AI agent and sends the query data. 【0405】 Step 4: 【0406】 Each AI agent processes the received query and generates a response using its own algorithm. The generated response is then sent to the server. 【0407】 Step 5: 【0408】 The server collects and evaluates the responses returned by each AI agent. This evaluation includes comparing responses with each other and matching them against past response history. This allows the confidence level of each response to be quantified. 【0409】 Step 6: 【0410】 The server selects the most reliable response based on the evaluation results. The selected response is sent to the terminal in preparation for providing it to the user. 【0411】 Step 7: 【0412】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve the problem. 【0413】 (Example 1) 【0414】 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." 【0415】 In modern information processing systems, providing quick and accurate responses to user inquiries is crucial. However, when responses from diverse sources are inconsistent, selecting the most reliable answer becomes difficult. This can lead to decreased operational efficiency and reduced user satisfaction. Therefore, there is a need for systems that can provide highly reliable responses to users. 【0416】 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. 【0417】 In this invention, the server includes means for receiving inquiries from users and analyzing them using natural language processing technology, means for forwarding inquiries to multiple information processing devices based on the analysis results, and means for obtaining multiple responses generated by the information processing devices. This makes it possible to compare and evaluate various responses to user inquiries and quickly provide the optimal response based on reliability. 【0418】 "User inquiries" refer to the act of users of an information system expressing their questions or problems in natural language and requesting a response from the system. 【0419】 "Natural language processing technology" refers to the technology that enables computers to understand and analyze natural language, which is human language, and includes functions for analyzing the meaning and context of text. 【0420】 "Analysis results" refer to keywords and contextual information obtained by analyzing the content of inquiries received from users using natural language processing technology. 【0421】 An "information processing device" refers to an electronic device or program that has the ability to perform calculations and decisions based on received data and generate a response. 【0422】 "Response reliability" refers to a numerical value or indicator that shows how accurate and helpful each generated response is in response to a user's inquiry. 【0423】 "Response history" refers to a record of responses that the system has generated and provided to users in the past, and is data used to evaluate the reliability of future responses. 【0424】 This invention provides an information processing system that accurately and quickly processes user inquiries. This system utilizes multiple information processing devices and selects the most appropriate response by comparing and evaluating the responses generated by each device. Furthermore, it aims to provide users with more reliable information by utilizing past response history. 【0425】 The server receives inquiries from users and analyzes the content of those inquiries using natural language processing technology. In this process, software such as Python's NLTK and SpaCy are used to extract keywords and context. The analyzed results are then transferred to multiple information processing devices, i.e., AI agents, each possessing a generative AI model. These AI agents generate responses based on the analysis results. A possible example of a specific AI model is the GPT series, which is a generative AI model. 【0426】 The generated responses are collected by the server, which evaluates them and compares them against past response history. This evaluation includes quantifying confidence levels using methods such as TF-IDF and clustering. The response with the highest confidence level is selected and displayed on the user's terminal. 【0427】 As a concrete example, consider a case where a user inquires, "My smartphone battery drains very quickly." In this case, the server sends the inquiry to an AI agent, which generates different responses such as "Please close background apps" or "Please lower the screen brightness." The server evaluates these responses and determines that "Please close background apps" is appropriate, providing it to the user. 【0428】 An example of a prompt message might be, "Please tell me how to improve power management on my mobile device." 【0429】 This system improves operational efficiency by enabling engineers and technical support staff to provide quick and reliable responses to user inquiries. 【0430】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0431】 Step 1: 【0432】 The user uses a terminal to input a query in natural language and sends it to the server. The input here is the query content in text format. This input is sent as a dataset for the server to receive. The specific actions involved in this process include text input on the terminal and the transmission of that text as packet data over the network. 【0433】 Step 2: 【0434】 The server receives inquiries from users. The received data is then analyzed using natural language processing techniques. This analysis uses Python's NLTK and SpaCy to extract keywords and context from the text. The output of the analysis is a dataset containing keyword and contextual information. This dataset is used in the next processing step. 【0435】 Step 3: 【0436】 The server forwards the query to multiple information processing devices, i.e., AI agents, based on the analysis results. The input is the dataset of analysis results obtained in step 2. The server generates a prompt sentence for each AI agent and instructs them to generate a response using a generative AI model. The output is the response generated by each AI agent, which is returned to the server. 【0437】 Step 4: 【0438】 The server collects multiple responses received from each AI agent as input. It compares the collected responses and performs data calculations to evaluate their reliability. This evaluation includes quantifying reliability using methods such as TF-IDF and clustering. The output of the evaluation is a confidence score for each response, which is used in the subsequent selection procedure. 【0439】 Step 5: 【0440】 The server selects the most appropriate response based on the confidence score. The inputs here are the confidence score obtained in step 4 and the corresponding response. The selected response is used as the final output to the user. After selection, the chosen response is returned to the user's terminal. 【0441】 Step 6: 【0442】 The user's device receives a selected response sent from the server. This response is displayed to the user and used as a guide for the user's next action. The output consists of specific action instructions or solutions displayed on the device screen. 【0443】 (Application Example 1) 【0444】 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." 【0445】 In modern homes, there is a need for systems that can respond quickly and accurately to the various problems and questions users face. However, existing technologies have challenges in ensuring the reliability of information and the selection of appropriate responses to address the diverse situations within the home. As a result, users spend a lot of time obtaining the necessary information and are unable to effectively utilize the appliances and devices in their homes. 【0446】 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. 【0447】 In this invention, the server includes means for setting up multiple information processing devices, means for acquiring multiple responses generated by the information processing devices, means for comparing and evaluating the multiple responses, means for receiving voice or text input from a user, and means for analyzing the received input using natural language processing and extracting keywords and context. This enables a rapid and reliable response to the diverse needs of users within the home. 【0448】 An "information processing device" is an electronic device used for inputting, processing, and outputting data, and possesses functions for performing various calculations and analyses. 【0449】 A "response" is an answer or reaction generated by an information processing device in response to a user's inquiry, and is intended to meet the user's needs. 【0450】 "Evaluation" is the process of comparing multiple responses and determining which response is the most appropriate and reliable. 【0451】 "Natural language processing" is a technology that understands natural language input by users and extracts appropriate information, analyzing the intent and context of inquiries. 【0452】 An "artificial intelligence agent" is a program or system that performs analysis and judgment based on a specific algorithm and autonomously generates responses and suggestions. 【0453】 "Past response history" refers to a database that records previously provided responses and is referenced in the evaluation process. 【0454】 "Household machinery and equipment" is a general term for machines and electronic devices used within the home, possessing a variety of functions to support daily life. 【0455】 The system implementing this invention takes the form of meeting diverse user needs within the home using household machinery and equipment. It is mainly implemented using a server, an information processing device, and a user terminal. 【0456】 The server receives voice and text input from the user using microphones and cameras built into home appliances. The received input is analyzed using natural language processing to extract keywords and context. This analysis is performed using Google Natural Language API or similar technologies. 【0457】 Based on the analysis results, the server forwards the query to multiple artificial intelligence agents. These agents use services such as IBM Watson and AWS Lex to generate responses using their own algorithms. The generated responses are collected by the server and evaluated. The evaluation uses past response history, and the reliability is determined by comparing them with similar cases in the database. 【0458】 The response deemed most reliable is provided to the user's device. The device includes a speaker and display, and the response is presented in either voice or text format. For example, if the user asks, "What should I make for dinner tonight?", the system can suggest dishes like "pasta," which have been popular in many households based on past history. An example of a prompt message to use would be, "Generate a highly reliable response to my dinner suggestion. I will consider past history and suggest the best dish." 【0459】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0460】 Step 1: 【0461】 The server uses the microphones and cameras of home appliances to receive voice or text input from the user. The input data is converted into digital text using speech recognition software. This provides text data of the user's questions and problems. 【0462】 Step 2: 【0463】 The server analyzes the received text data using natural language processing techniques. It utilizes tools such as the Google Natural Language API to extract keywords and context through the analysis. The input is text data, and the output is the analyzed keywords and contextual information. 【0464】 Step 3: 【0465】 The server sends queries to multiple artificial intelligence agents based on keywords and contextual information obtained from the analysis. AI services such as IBM Watson and AWS Lex are used, each generating responses using its own algorithm. Each AI agent receives analysis results as input and response candidates as output. 【0466】 Step 4: 【0467】 The server retrieves response candidates generated by each artificial intelligence agent and compares them against a database to track past response history. This comparison quantifies the confidence level of each response, and the server selects the response deemed most reliable. The input consists of response candidate data and past response history data, while the output is the response data with its reliability evaluated. 【0468】 Step 5: 【0469】 The server sends selected, reliable responses to the user's terminal, providing the response via voice or text through a speaker or display. This allows the user to quickly obtain solutions appropriate to their home environment. The input is reliability-rated response data, and the output is response information presented in a user-recognizable format. 【0470】 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. 【0471】 This invention is an information processing system that combines an emotion engine that recognizes user emotions, enabling not only rapid and accurate information delivery but also responses that take into account the user's emotional state. This makes it possible to more effectively meet user needs. 【0472】 Users input questions or problems in natural language through their devices and send them to the server. After receiving this query, the server first uses natural language processing techniques to analyze important keywords and context. Next, the server uses an emotion engine to detect the emotional state from the user's input text. This emotion engine is designed based on common natural language data and recognizes emotions such as joy, anger, and sadness from the text. 【0473】 The analyzed inquiry and sentiment information are sent to their respective information processing units, i.e., AI agents. The AI ​​agents generate responses based on the received information. In this process, sentiment information is also taken into consideration; for example, if the user is dissatisfied, the response may be generated using more considerate language. 【0474】 The server collects multiple responses generated by the AI ​​agent and compares and evaluates each response. This evaluation process incorporates emotional information, considering not only the accuracy of the information but also the impression the response makes on the user. The response deemed most reliable is selected and sent back to the user's device. 【0475】 For example, if a user makes an inquiry such as, "I'm unhappy because the service is delayed," the server can choose a response that includes "offering specific solutions" or "apologizing," allowing for an effective dialogue that alleviates the user's dissatisfaction. 【0476】 This system will enable engineers and technical support personnel to deliver high-quality service quickly through emotionally sensitive and reliable responses. 【0477】 The following describes the processing flow. 【0478】 Step 1: 【0479】 The user enters their inquiry into the terminal in natural language. The terminal sends this input to the server. At this stage, the user's text data enters the system. 【0480】 Step 2: 【0481】 The server analyzes the text data received from the user. It uses natural language processing techniques to extract context and keywords and structure the data. 【0482】 Step 3: 【0483】 The server sends text data to the emotion engine to identify the user's emotional state. The emotion engine analyzes emotions such as joy, anger, and sadness from the text and returns corresponding emotional information to the server. 【0484】 Step 4: 【0485】 The server sends data to multiple AI agents based on the analyzed query content and sentiment information. Each AI agent generates a response based on the data it receives. 【0486】 Step 5: 【0487】 The responses generated by each AI agent are collected by a server. The server compares and evaluates the responses against each other. This evaluation includes checking not only the accuracy of the information but also whether the response is appropriate to the user's emotional state. 【0488】 Step 6: 【0489】 The server selects the most reliable response based on the evaluation results, which best matches the user's emotions. Once the optimal response is selected, the server sends that response to the user's terminal. 【0490】 Step 7: 【0491】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve their own problem. 【0492】 (Example 2) 【0493】 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." 【0494】 Modern information processing systems are required to provide rapid and accurate responses that take user emotions into consideration. However, conventional systems have struggled to generate responses that appropriately reflect emotions. In particular, the lack of technology to accurately detect emotions from user input and select appropriate responses based on them has resulted in a failure to improve user satisfaction. 【0495】 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. 【0496】 In this invention, the server includes means for analyzing user input information, means for detecting emotional states from the analyzed text information, and means for setting up a plurality of information processing devices. This makes it possible to provide accurate and appropriate responses that take into account the user's emotions. 【0497】 "User" refers to a person who uses the system to input information or receive responses. 【0498】 "Input information" refers to text data, including questions and requests, that users provide to the system. 【0499】 "Analysis" refers to the process of processing input information to extract meaning, keywords, and context. 【0500】 "Emotional state" refers to emotional states such as joy, anger, and sadness that can be inferred from the user's input information. 【0501】 An "information processing device" refers to a computer system used to process data and generate responses. 【0502】 "Evaluation" refers to the process of comparing multiple generated responses and determining their reliability and appropriateness. 【0503】 "Selection" refers to the process of determining the most appropriate response based on the evaluation results. 【0504】 "Response" refers to the answer or message that an information processing device generates and provides to the user. 【0505】 This invention provides an information response system that takes user emotions into consideration. The system mainly consists of a user terminal, a server, and an AI agent. The user inputs inquiries in natural language using their own terminal and sends them to the server. In this system, smartphones and personal computers are often used as user terminals. 【0506】 The server analyzes incoming queries using natural language processing (NLP) techniques to extract keywords and context. Libraries such as spaCy and NLTK are used for this analysis. The server then uses an emotion engine to detect the user's emotional state from the text. This process utilizes tools such as TextBlob and the Google Cloud Natural Language API. 【0507】 After analysis and emotion detection are complete, the server sends this information to the AI ​​agent. The AI ​​agent uses a generative AI model based on the received information to generate a response that takes the user's emotions into consideration. In this response generation process, OpenAI's GPT-3 is used as the generative AI model. The AI ​​agent evaluates the input data and the multiple generated responses and selects the most appropriate one. 【0508】 The selected response is returned to the server and finally sent to the user's terminal. This allows the user to receive an emotionally sensitive response. For example, if a user makes an inquiry such as, "I'm unhappy because the service is slow," the system can detect this dissatisfaction and provide a response that includes an appropriate apology and solution. 【0509】 An example of a prompt message would be: "Identify the user's sentiment based on their text and generate a response that takes that sentiment into consideration. For example, in response to a complaint such as 'The service is slow,' generate a response that offers a sincere apology along with a relevant solution." 【0510】 This allows the system to respond quickly and accurately to user needs, thereby increasing user satisfaction. 【0511】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0512】 Step 1: 【0513】 The user uses a terminal to input and submit questions or problems in natural language. On the terminal, the user types text into the input field and presses the submit button, sending that text data to the server. The input is the user's text data, and the output is that text sent to the server. 【0514】 Step 2: 【0515】 The server retrieves text data received from the user and performs natural language processing (NLP). Specifically, it uses an NLP library to perform semantic analysis, keyword extraction, and contextual understanding of the text. The input is the user's text data, and the output is the analyzed keywords and contextual information. In this process, the server converts the information within the text into structured data. 【0516】 Step 3: 【0517】 The server uses an emotion engine to detect emotions from the parsed text data. The emotion engine categorizes the text into specific emotion categories (e.g., joy, anger, sadness). In this process, the input is the parsed text data, and the output is data indicating the user's emotional state. Specifically, it uses a statistical model to infer emotions based on words and phrases within the text. 【0518】 Step 4: 【0519】 The server sends structured keywords, contextual information, and sentiment to the AI ​​agent. The AI ​​agent uses this data to set prompts for generating appropriate responses. The input is analytical information generated from text, which is then converted into prompt data used by the AI ​​agent. 【0520】 Step 5: 【0521】 The AI ​​agent uses a generative AI model to generate responses that take into account the user's emotional state. It takes a prompt as input to the generative AI model and processes it to obtain the optimal response. The input is the prompt, and the output is the generated response text. The AI ​​agent generates responses considering the tone and wording appropriate to the user's emotions. 【0522】 Step 6: 【0523】 When the server receives multiple responses from the AI ​​agent, it compares and evaluates them. Here, data evaluation is performed to select the most appropriate response from the generated responses. The input is the multiple generated responses, and the output is the optimal response selected based on the evaluation results. This step considers the accuracy of the response and the emotionally appropriate tone of speech. 【0524】 Step 7: 【0525】 The server sends the selected optimal response to the user's terminal, where it is displayed. The input is the selected response text, and the output is the response displayed on the user's terminal. This allows the user to receive emotionally sensitive information and obtain a satisfactory response. 【0526】 (Application Example 2) 【0527】 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." 【0528】 Conventional information processing systems often provide uniform responses regardless of the user's emotional state, making it challenging to improve user satisfaction and trust. Furthermore, while there is a need to generate quick and emotionally sensitive responses to user inquiries, achieving this remains difficult. 【0529】 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. 【0530】 In this invention, the server includes means for setting up multiple data processing devices, means for analyzing user input information using an emotion recognition engine that determines the user's emotional state, and means for generating an emotion-sensitive response using a generative AI model based on the user's emotional state. This makes it possible to provide a more appropriate and satisfying response that is in line with the user's emotions. 【0531】 A "data processing device" is a device that has the function of analyzing information and generating a response based on that analysis. 【0532】 An "emotion recognition engine" is a technology that analyzes the emotional state from information input by the user and generates data accordingly. 【0533】 A "generative AI model" is an algorithm that learns from a large amount of data and generates an appropriate response based on the input information. 【0534】 "Natural language processing technology" is a computer technology used to understand and analyze human language, including extracting context and keywords. 【0535】 "Confidence level" is an index used to evaluate the accuracy and appropriateness of the generated response. 【0536】 This invention provides a system for accurately analyzing user input information and providing responses that take emotions into consideration. The specific implementation method is described below. 【0537】 First, the user makes a query using natural language on a device such as a smartphone. This device sends the query to the server. The server receives this query and begins processing the data using natural language processing technology. The software used here is spaCy, a natural language processing library. spaCy is used to analyze the context and keywords of the input data. 【0538】 Next, the server uses an emotion recognition engine to determine the user's emotional state. Hugging Face's Transformers are used as the emotion recognition engine. Transformers are used to detect emotions such as joy, anger, and sadness from text. Based on this emotional information, the server generates a response using a generative AI model. OpenAI's GPT is suitable as the generative AI model. A system using this model outputs a considerate response appropriate to the user's emotional state. 【0539】 Ultimately, the server compares the multiple responses it generates and selects the most appropriate one based on criteria such as reliability and emotional consideration. The selected response is then sent back to the user's device. As a result, the user receives an emotionally appropriate response. 【0540】 For example, if a user inquires that their ordered item is late and causing them inconvenience, the system will first investigate the cause of the delay and then generate a response such as, "We apologize for the inconvenience. We will address the issue as soon as possible. We will also send you a discount coupon for your next purchase." In this way, the present invention goes beyond mere information provision to realize a service that gives users peace of mind. 【0541】 Examples of prompt statements include: 【0542】 "Recognize the user's emotions and generate an appropriate response: If the user says '___', how should you respond?" 【0543】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0544】 Step 1: 【0545】 The user uses a terminal to input a query in natural language and sends it from the terminal to the server. The input is the user's text data, and the output is the query data sent to the server. This data is then subjected to further analysis and processing. 【0546】 Step 2: 【0547】 The server parses the received query data using spaCy, a natural language processing library. The input is the user's query data, and the output is parsed data including context and keywords. Based on this data, the server prepares for sentiment recognition in the next step. 【0548】 Step 3: 【0549】 The server uses the analyzed data to run the Hugging Face Transformers emotion recognition engine and determine the user's emotions. The input is analyzed text data, and the output is emotion data, including emotion identification. Based on the emotion data obtained here, the server identifies the emotional state to be used for response generation. 【0550】 Step 4: 【0551】 The server utilizes OpenAI's GPT generative AI model to generate responses that take sentiment data into account. The input consists of sentiment data and parsed text, and the output is the generated response text. This generated response is designed to reflect the user's emotional state. 【0552】 Step 5: 【0553】 The server collects multiple generated responses, compares them, and evaluates them. The input is the multiple generated response texts, and the output is the optimal response selected based on the evaluation. Here, the server uses indicators such as trustworthiness and consideration of emotions to determine the most appropriate response. 【0554】 Step 6: 【0555】 The server sends the selected optimal response to the user's terminal. The input is the optimal response text, and the output is the response message displayed on the user's terminal. By receiving this response, the user can receive emotionally sensitive support. 【0556】 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. 【0557】 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. 【0558】 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. 【0559】 [Fourth Embodiment] 【0560】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0561】 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. 【0562】 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). 【0563】 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. 【0564】 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. 【0565】 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). 【0566】 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. 【0567】 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. 【0568】 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. 【0569】 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. 【0570】 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. 【0571】 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. 【0572】 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". 【0573】 This invention provides a highly reliable information system by using multiple information processing devices and comparing and evaluating the responses they generate. This system improves operational efficiency by receiving user inquiries and providing quick and accurate responses. Furthermore, it enhances the reliability of responses by utilizing an internal database and past response history. 【0574】 The server first receives inquiries from users. Users use their terminals to input problems or questions in natural language and send them to the server. The server analyzes the received inquiries using natural language processing techniques and extracts keywords and context. Based on these analysis results, the server forwards the inquiries to multiple information processing devices, i.e., AI agents. Each AI agent uses its own algorithm to generate a response based on the analysis results. 【0575】 The multiple responses generated are collected again by the server. The server then compares these responses with each other and evaluates them by cross-referencing them with past response history. This evaluation process is crucial for quantifying confidence and selecting the most reliable response. The selected response is returned to the user's terminal and displayed on the screen. 【0576】 For example, if a user inquires, "I'm having trouble with my network connection," the server sends this inquiry to multiple AI agents. The AI ​​agents generate responses such as "Check your cables" or "Restart your router." The server then evaluates these responses, and if it determines from past data that "Check your cables" is appropriate, it provides that response to the user. 【0577】 This system enables engineers and technical support staff to respond quickly based on reliable information, significantly improving work efficiency. 【0578】 The following describes the processing flow. 【0579】 Step 1: 【0580】 The user uses a terminal to input questions or problems in natural language. The terminal converts the input data into a structured format and sends it to the server. 【0581】 Step 2: 【0582】 The server analyzes the queries received from users. Natural language processing techniques are used for the analysis to extract important keywords and context. This clarifies the intent of the query, making subsequent processing easier. 【0583】 Step 3: 【0584】 The server forwards the query to multiple AI agents based on the analysis results. The server creates an API request for each AI agent and sends the query data. 【0585】 Step 4: 【0586】 Each AI agent processes the received query and generates a response using its own algorithm. The generated response is then sent to the server. 【0587】 Step 5: 【0588】 The server collects and evaluates the responses returned by each AI agent. This evaluation includes comparing responses with each other and matching them against past response history. This allows the confidence level of each response to be quantified. 【0589】 Step 6: 【0590】 The server selects the most reliable response based on the evaluation results. The selected response is sent to the terminal in preparation for providing it to the user. 【0591】 Step 7: 【0592】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve the problem. 【0593】 (Example 1) 【0594】 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". 【0595】 In modern information processing systems, providing quick and accurate responses to user inquiries is crucial. However, when responses from diverse sources are inconsistent, selecting the most reliable answer becomes difficult. This can lead to decreased operational efficiency and reduced user satisfaction. Therefore, there is a need for systems that can provide highly reliable responses to users. 【0596】 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. 【0597】 In this invention, the server includes means for receiving inquiries from users and analyzing them using natural language processing technology, means for forwarding inquiries to multiple information processing devices based on the analysis results, and means for obtaining multiple responses generated by the information processing devices. This makes it possible to compare and evaluate various responses to user inquiries and quickly provide the optimal response based on reliability. 【0598】 "User inquiries" refer to the act of users of an information system expressing their questions or problems in natural language and requesting a response from the system. 【0599】 "Natural language processing technology" refers to the technology that enables computers to understand and analyze natural language, which is human language, and includes functions for analyzing the meaning and context of text. 【0600】 "Analysis results" refer to keywords and contextual information obtained by analyzing the content of inquiries received from users using natural language processing technology. 【0601】 An "information processing device" refers to an electronic device or program that has the ability to perform calculations and decisions based on received data and generate a response. 【0602】 "Response reliability" refers to a numerical value or indicator that shows how accurate and helpful each generated response is in response to a user's inquiry. 【0603】 "Response history" refers to a record of responses that the system has generated and provided to users in the past, and is data used to evaluate the reliability of future responses. 【0604】 This invention provides an information processing system that accurately and quickly processes user inquiries. This system utilizes multiple information processing devices and selects the most appropriate response by comparing and evaluating the responses generated by each device. Furthermore, it aims to provide users with more reliable information by utilizing past response history. 【0605】 The server receives inquiries from users and analyzes the content of those inquiries using natural language processing technology. In this process, software such as Python's NLTK and SpaCy are used to extract keywords and context. The analyzed results are then transferred to multiple information processing devices, i.e., AI agents, each possessing a generative AI model. These AI agents generate responses based on the analysis results. A possible example of a specific AI model is the GPT series, which is a generative AI model. 【0606】 The generated responses are collected by the server, which evaluates them and compares them against past response history. This evaluation includes quantifying confidence levels using methods such as TF-IDF and clustering. The response with the highest confidence level is selected and displayed on the user's terminal. 【0607】 As a concrete example, consider a case where a user inquires, "My smartphone battery drains very quickly." In this case, the server sends the inquiry to an AI agent, which generates different responses such as "Please close background apps" or "Please lower the screen brightness." The server evaluates these responses and determines that "Please close background apps" is appropriate, providing it to the user. 【0608】 An example of a prompt message might be, "Please tell me how to improve power management on my mobile device." 【0609】 This system improves operational efficiency by enabling engineers and technical support staff to provide quick and reliable responses to user inquiries. 【0610】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0611】 Step 1: 【0612】 The user uses a terminal to input a query in natural language and sends it to the server. The input here is the query content in text format. This input is sent as a dataset for the server to receive. The specific actions involved in this process include text input on the terminal and the transmission of that text as packet data over the network. 【0613】 Step 2: 【0614】 The server receives inquiries from users. The received data is then analyzed using natural language processing techniques. This analysis uses Python's NLTK and SpaCy to extract keywords and context from the text. The output of the analysis is a dataset containing keyword and contextual information. This dataset is used in the next processing step. 【0615】 Step 3: 【0616】 The server forwards the query to multiple information processing devices, i.e., AI agents, based on the analysis results. The input is the dataset of analysis results obtained in step 2. The server generates a prompt sentence for each AI agent and instructs them to generate a response using a generative AI model. The output is the response generated by each AI agent, which is returned to the server. 【0617】 Step 4: 【0618】 The server collects multiple responses received from each AI agent as input. It compares the collected responses and performs data calculations to evaluate their reliability. This evaluation includes quantifying reliability using methods such as TF-IDF and clustering. The output of the evaluation is a confidence score for each response, which is used in the subsequent selection procedure. 【0619】 Step 5: 【0620】 The server selects the most appropriate response based on the confidence score. The inputs here are the confidence score obtained in step 4 and the corresponding response. The selected response is used as the final output to the user. After selection, the chosen response is returned to the user's terminal. 【0621】 Step 6: 【0622】 The user's device receives a selected response sent from the server. This response is displayed to the user and used as a guide for the user's next action. The output consists of specific action instructions or solutions displayed on the device screen. 【0623】 (Application Example 1) 【0624】 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". 【0625】 In modern homes, there is a need for systems that can respond quickly and accurately to the various problems and questions users face. However, existing technologies have challenges in ensuring the reliability of information and the selection of appropriate responses to address the diverse situations within the home. As a result, users spend a lot of time obtaining the necessary information and are unable to effectively utilize the appliances and devices in their homes. 【0626】 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. 【0627】 In this invention, the server includes means for setting up multiple information processing devices, means for acquiring multiple responses generated by the information processing devices, means for comparing and evaluating the multiple responses, means for receiving voice or text input from a user, and means for analyzing the received input using natural language processing and extracting keywords and context. This enables a rapid and reliable response to the diverse needs of users within the home. 【0628】 An "information processing device" is an electronic device used for inputting, processing, and outputting data, and possesses functions for performing various calculations and analyses. 【0629】 A "response" is an answer or reaction generated by an information processing device in response to a user's inquiry, and is intended to meet the user's needs. 【0630】 "Evaluation" is the process of comparing multiple responses and determining which response is the most appropriate and reliable. 【0631】 "Natural language processing" is a technology that understands natural language input by users and extracts appropriate information, analyzing the intent and context of inquiries. 【0632】 An "artificial intelligence agent" is a program or system that performs analysis and judgment based on a specific algorithm and autonomously generates responses and suggestions. 【0633】 "Past response history" refers to a database that records previously provided responses and is referenced in the evaluation process. 【0634】 "Household machinery and equipment" is a general term for machines and electronic devices used within the home, possessing a variety of functions to support daily life. 【0635】 The system implementing this invention takes the form of meeting diverse user needs within the home using household machinery and equipment. It is mainly implemented using a server, an information processing device, and a user terminal. 【0636】 The server receives voice and text input from the user using microphones and cameras built into home appliances. The received input is analyzed using natural language processing to extract keywords and context. This analysis is performed using Google Natural Language API or similar technologies. 【0637】 Based on the analysis results, the server forwards the query to multiple artificial intelligence agents. These agents use services such as IBM Watson and AWS Lex to generate responses using their own algorithms. The generated responses are collected by the server and evaluated. The evaluation uses past response history, and the reliability is determined by comparing them with similar cases in the database. 【0638】 The response deemed most reliable is provided to the user's device. The device includes a speaker and display, and the response is presented in either voice or text format. For example, if the user asks, "What should I make for dinner tonight?", the system can suggest dishes like "pasta," which have been popular in many households based on past history. An example of a prompt message to use would be, "Generate a highly reliable response to my dinner suggestion. I will consider past history and suggest the best dish." 【0639】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0640】 Step 1: 【0641】 The server uses the microphones and cameras of home appliances to receive voice or text input from the user. The input data is converted into digital text using speech recognition software. This provides text data of the user's questions and problems. 【0642】 Step 2: 【0643】 The server analyzes the received text data using natural language processing techniques. It utilizes tools such as the Google Natural Language API to extract keywords and context through the analysis. The input is text data, and the output is the analyzed keywords and contextual information. 【0644】 Step 3: 【0645】 The server sends queries to multiple artificial intelligence agents based on keywords and contextual information obtained from the analysis. AI services such as IBM Watson and AWS Lex are used, each generating responses using its own algorithm. Each AI agent receives analysis results as input and response candidates as output. 【0646】 Step 4: 【0647】 The server retrieves response candidates generated by each artificial intelligence agent and compares them against a database to track past response history. This comparison quantifies the confidence level of each response, and the server selects the response deemed most reliable. The input consists of response candidate data and past response history data, while the output is the response data with its reliability evaluated. 【0648】 Step 5: 【0649】 The server sends selected, reliable responses to the user's terminal, providing the response via voice or text through a speaker or display. This allows the user to quickly obtain solutions appropriate to their home environment. The input is reliability-rated response data, and the output is response information presented in a user-recognizable format. 【0650】 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. 【0651】 This invention is an information processing system that combines an emotion engine that recognizes user emotions, enabling not only rapid and accurate information delivery but also responses that take into account the user's emotional state. This makes it possible to more effectively meet user needs. 【0652】 Users input questions or problems in natural language through their devices and send them to the server. After receiving this query, the server first uses natural language processing techniques to analyze important keywords and context. Next, the server uses an emotion engine to detect the emotional state from the user's input text. This emotion engine is designed based on common natural language data and recognizes emotions such as joy, anger, and sadness from the text. 【0653】 The analyzed inquiry and sentiment information are sent to their respective information processing units, i.e., AI agents. The AI ​​agents generate responses based on the received information. In this process, sentiment information is also taken into consideration; for example, if the user is dissatisfied, the response may be generated using more considerate language. 【0654】 The server collects multiple responses generated by the AI ​​agent and compares and evaluates each response. This evaluation process incorporates emotional information, considering not only the accuracy of the information but also the impression the response makes on the user. The response deemed most reliable is selected and sent back to the user's device. 【0655】 For example, if a user makes an inquiry such as, "I'm unhappy because the service is delayed," the server can choose a response that includes "offering specific solutions" or "apologizing," allowing for an effective dialogue that alleviates the user's dissatisfaction. 【0656】 This system will enable engineers and technical support personnel to deliver high-quality service quickly through emotionally sensitive and reliable responses. 【0657】 The following describes the processing flow. 【0658】 Step 1: 【0659】 The user enters their inquiry into the terminal in natural language. The terminal sends this input to the server. At this stage, the user's text data enters the system. 【0660】 Step 2: 【0661】 The server analyzes the text data received from the user. It uses natural language processing techniques to extract context and keywords and structure the data. 【0662】 Step 3: 【0663】 The server sends text data to the emotion engine to identify the user's emotional state. The emotion engine analyzes emotions such as joy, anger, and sadness from the text and returns corresponding emotional information to the server. 【0664】 Step 4: 【0665】 The server sends data to multiple AI agents based on the analyzed query content and sentiment information. Each AI agent generates a response based on the data it receives. 【0666】 Step 5: 【0667】 The responses generated by each AI agent are collected by a server. The server compares and evaluates the responses against each other. This evaluation includes checking not only the accuracy of the information but also whether the response is appropriate to the user's emotional state. 【0668】 Step 6: 【0669】 The server selects the most reliable response based on the evaluation results, which best matches the user's emotions. Once the optimal response is selected, the server sends that response to the user's terminal. 【0670】 Step 7: 【0671】 The terminal displays selected responses received from the server to the user. The user can then use the presented information to resolve their own problem. 【0672】 (Example 2) 【0673】 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". 【0674】 Modern information processing systems are required to provide rapid and accurate responses that take user emotions into consideration. However, conventional systems have struggled to generate responses that appropriately reflect emotions. In particular, the lack of technology to accurately detect emotions from user input and select appropriate responses based on them has resulted in a failure to improve user satisfaction. 【0675】 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. 【0676】 In this invention, the server includes means for analyzing user input information, means for detecting emotional states from the analyzed text information, and means for setting up a plurality of information processing devices. This makes it possible to provide accurate and appropriate responses that take into account the user's emotions. 【0677】 "User" refers to a person who uses the system to input information or receive responses. 【0678】 "Input information" refers to text data, including questions and requests, that users provide to the system. 【0679】 "Analysis" refers to the process of processing input information to extract meaning, keywords, and context. 【0680】 "Emotional state" refers to emotional states such as joy, anger, and sadness that can be inferred from the user's input information. 【0681】 An "information processing device" refers to a computer system used to process data and generate responses. 【0682】 "Evaluation" refers to the process of comparing multiple generated responses and determining their reliability and appropriateness. 【0683】 "Selection" refers to the process of determining the most appropriate response based on the evaluation results. 【0684】 "Response" refers to the answer or message that an information processing device generates and provides to the user. 【0685】 This invention provides an information response system that takes user emotions into consideration. The system mainly consists of a user terminal, a server, and an AI agent. The user inputs inquiries in natural language using their own terminal and sends them to the server. In this system, smartphones and personal computers are often used as user terminals. 【0686】 The server analyzes incoming queries using natural language processing (NLP) techniques to extract keywords and context. Libraries such as spaCy and NLTK are used for this analysis. The server then uses an emotion engine to detect the user's emotional state from the text. This process utilizes tools such as TextBlob and the Google Cloud Natural Language API. 【0687】 After analysis and emotion detection are complete, the server sends this information to the AI ​​agent. The AI ​​agent uses a generative AI model based on the received information to generate a response that takes the user's emotions into consideration. In this response generation process, OpenAI's GPT-3 is used as the generative AI model. The AI ​​agent evaluates the input data and the multiple generated responses and selects the most appropriate one. 【0688】 The selected response is returned to the server and finally sent to the user's terminal. This allows the user to receive an emotionally sensitive response. For example, if a user makes an inquiry such as, "I'm unhappy because the service is slow," the system can detect this dissatisfaction and provide a response that includes an appropriate apology and solution. 【0689】 An example of a prompt message would be: "Identify the user's sentiment based on their text and generate a response that takes that sentiment into consideration. For example, in response to a complaint such as 'The service is slow,' generate a response that offers a sincere apology along with a relevant solution." 【0690】 This allows the system to respond quickly and accurately to user needs, thereby increasing user satisfaction. 【0691】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0692】 Step 1: 【0693】 The user uses a terminal to input and submit questions or problems in natural language. On the terminal, the user types text into the input field and presses the submit button, sending that text data to the server. The input is the user's text data, and the output is that text sent to the server. 【0694】 Step 2: 【0695】 The server retrieves text data received from the user and performs natural language processing (NLP). Specifically, it uses an NLP library to perform semantic analysis, keyword extraction, and contextual understanding of the text. The input is the user's text data, and the output is the analyzed keywords and contextual information. In this process, the server converts the information within the text into structured data. 【0696】 Step 3: 【0697】 The server uses an emotion engine to detect emotions from the parsed text data. The emotion engine categorizes the text into specific emotion categories (e.g., joy, anger, sadness). In this process, the input is the parsed text data, and the output is data indicating the user's emotional state. Specifically, it uses a statistical model to infer emotions based on words and phrases within the text. 【0698】 Step 4: 【0699】 The server sends structured keywords, contextual information, and sentiment to the AI ​​agent. The AI ​​agent uses this data to set prompts for generating appropriate responses. The input is analytical information generated from text, which is then converted into prompt data used by the AI ​​agent. 【0700】 Step 5: 【0701】 The AI ​​agent uses a generative AI model to generate responses that take into account the user's emotional state. It takes a prompt as input to the generative AI model and processes it to obtain the optimal response. The input is the prompt, and the output is the generated response text. The AI ​​agent generates responses considering the tone and wording appropriate to the user's emotions. 【0702】 Step 6: 【0703】 When the server receives multiple responses from the AI ​​agent, it compares and evaluates them. Here, data evaluation is performed to select the most appropriate response from the generated responses. The input is the multiple generated responses, and the output is the optimal response selected based on the evaluation results. This step considers the accuracy of the response and the emotionally appropriate tone of speech. 【0704】 Step 7: 【0705】 The server sends the selected optimal response to the user's terminal, where it is displayed. The input is the selected response text, and the output is the response displayed on the user's terminal. This allows the user to receive emotionally sensitive information and obtain a satisfactory response. 【0706】 (Application Example 2) 【0707】 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". 【0708】 Conventional information processing systems often provide uniform responses regardless of the user's emotional state, making it challenging to improve user satisfaction and trust. Furthermore, while there is a need to generate quick and emotionally sensitive responses to user inquiries, achieving this remains difficult. 【0709】 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. 【0710】 In this invention, the server includes means for setting up multiple data processing devices, means for analyzing user input information using an emotion recognition engine that determines the user's emotional state, and means for generating an emotion-sensitive response using a generative AI model based on the user's emotional state. This makes it possible to provide a more appropriate and satisfying response that is in line with the user's emotions. 【0711】 A "data processing device" is a device that has the function of analyzing information and generating a response based on that analysis. 【0712】 An "emotion recognition engine" is a technology that analyzes the emotional state from information input by the user and generates data accordingly. 【0713】 A "generative AI model" is an algorithm that learns from a large amount of data and generates an appropriate response based on the input information. 【0714】 "Natural language processing technology" is a computer technology used to understand and analyze human language, including extracting context and keywords. 【0715】 "Confidence level" is an index used to evaluate the accuracy and appropriateness of the generated response. 【0716】 This invention provides a system for accurately analyzing user input information and providing responses that take emotions into consideration. The specific implementation method is described below. 【0717】 First, the user makes a query using natural language on a device such as a smartphone. This device sends the query to the server. The server receives this query and begins processing the data using natural language processing technology. The software used here is spaCy, a natural language processing library. spaCy is used to analyze the context and keywords of the input data. 【0718】 Next, the server uses an emotion recognition engine to determine the user's emotional state. Hugging Face's Transformers are used as the emotion recognition engine. Transformers are used to detect emotions such as joy, anger, and sadness from text. Based on this emotional information, the server generates a response using a generative AI model. OpenAI's GPT is suitable as the generative AI model. A system using this model outputs a considerate response appropriate to the user's emotional state. 【0719】 Ultimately, the server compares the multiple responses it generates and selects the most appropriate one based on criteria such as reliability and emotional consideration. The selected response is then sent back to the user's device. As a result, the user receives an emotionally appropriate response. 【0720】 For example, if a user inquires that their ordered item is late and causing them inconvenience, the system will first investigate the cause of the delay and then generate a response such as, "We apologize for the inconvenience. We will address the issue as soon as possible. We will also send you a discount coupon for your next purchase." In this way, the present invention goes beyond mere information provision to realize a service that gives users peace of mind. 【0721】 Examples of prompt statements include: 【0722】 "Recognize the user's emotions and generate an appropriate response: If the user says '___', how should you respond?" 【0723】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0724】 Step 1: 【0725】 The user uses a terminal to input a query in natural language and sends it from the terminal to the server. The input is the user's text data, and the output is the query data sent to the server. This data is then subjected to further analysis and processing. 【0726】 Step 2: 【0727】 The server parses the received query data using spaCy, a natural language processing library. The input is the user's query data, and the output is parsed data including context and keywords. Based on this data, the server prepares for sentiment recognition in the next step. 【0728】 Step 3: 【0729】 The server uses the analyzed data to run the Hugging Face Transformers emotion recognition engine and determine the user's emotions. The input is analyzed text data, and the output is emotion data, including emotion identification. Based on the emotion data obtained here, the server identifies the emotional state to be used for response generation. 【0730】 Step 4: 【0731】 The server utilizes OpenAI's GPT generative AI model to generate responses that take sentiment data into account. The input consists of sentiment data and parsed text, and the output is the generated response text. This generated response is designed to reflect the user's emotional state. 【0732】 Step 5: 【0733】 The server collects multiple generated responses, compares them, and evaluates them. The input is the multiple generated response texts, and the output is the optimal response selected based on the evaluation. Here, the server uses indicators such as trustworthiness and consideration of emotions to determine the most appropriate response. 【0734】 Step 6: 【0735】 The server sends the selected optimal response to the user's terminal. The input is the optimal response text, and the output is the response message displayed on the user's terminal. By receiving this response, the user can receive emotionally sensitive support. 【0736】 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. 【0737】 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. 【0738】 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. 【0739】 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. 【0740】 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. 【0741】 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. 【0742】 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. 【0743】 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. 【0744】 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." 【0745】 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. 【0746】 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. 【0747】 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. 【0748】 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. 【0749】 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. 【0750】 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. 【0751】 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. 【0752】 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. 【0753】 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. 【0754】 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. 【0755】 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. 【0756】 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. 【0757】 The following is further disclosed regarding the embodiments described above. 【0758】 (Claim 1) 【0759】 Means for setting up multiple information processing devices, 【0760】 means for acquiring a plurality of responses generated by the information processing device, 【0761】 Means for comparing and evaluating the aforementioned multiple responses, 【0762】 A means of selecting the most appropriate response based on the evaluation results, 【0763】 A means of providing the selected response to the user, 【0764】 A system that includes this. 【0765】 (Claim 2) 【0766】 The system according to claim 1, characterized in that the evaluation means evaluates the reliability by comparing it with similar responses in the past. 【0767】 (Claim 3) 【0768】 The system according to claim 1, characterized in that the information processing device analyzes inquiries from users using natural language processing technology. 【0769】 "Example 1" 【0770】 (Claim 1) 【0771】 A means of receiving inquiries from users and analyzing them using natural language processing technology, 【0772】 A means for forwarding queries to multiple information processing devices based on the analysis results, 【0773】 means for acquiring a plurality of responses generated by the information processing device, 【0774】 A means for comparing the aforementioned multiple responses with past response history and evaluating their reliability numerically, 【0775】 A means of selecting the most appropriate response based on the evaluation results, 【0776】 A means of providing the selected response to the user, 【0777】 A system that includes this. 【0778】 (Claim 2) 【0779】 The system according to claim 1, characterized in that the evaluation means evaluates the reliability by comparing it with past response history. 【0780】 (Claim 3) 【0781】 The system according to claim 1, characterized in that the information processing device analyzes inquiries from users using natural language processing technology and utilizes different generative AI technologies when generating responses based on the analysis results. 【0782】 "Application Example 1" 【0783】 (Claim 1) 【0784】 Means for setting up multiple information processing devices, 【0785】 means for acquiring a plurality of responses generated by the information processing device, 【0786】 Means for comparing and evaluating the aforementioned multiple responses, 【0787】 A means of selecting the most appropriate response based on the evaluation results, 【0788】 A means of providing the selected response to the user, 【0789】 A means of receiving voice or text input from the user, 【0790】 A means of analyzing the received input using natural language processing and extracting keywords and context, 【0791】 A means of sending inquiries to multiple artificial intelligence agents based on the extracted information, 【0792】 A means for evaluating the generated response by comparing it with past response history, 【0793】 A system that includes this. 【0794】 (Claim 2) 【0795】 The system according to claim 1, characterized in that the evaluation means evaluates the reliability by comparing it with similar responses in the past. 【0796】 (Claim 3) 【0797】 The system according to claim 1, characterized in that the information processing device analyzes inquiries from users using natural language processing technology and provides responses through an application installed on a home appliance. 【0798】 "Example 2 of combining an emotion engine" 【0799】 (Claim 1) 【0800】 A means of analyzing user input information, 【0801】 A means for detecting emotional states from analyzed text information, 【0802】 Means for setting up multiple information processing devices, 【0803】 means for acquiring a plurality of responses generated by the information processing device, 【0804】 Means for comparing and evaluating the aforementioned multiple responses, 【0805】 A means for selecting the most appropriate response, taking into account the aforementioned emotional state, based on the evaluation results, 【0806】 A means of providing the selected response to the user, 【0807】 A system that includes this. 【0808】 (Claim 2) 【0809】 The system according to claim 1, characterized in that the evaluation means evaluates the reliability by comparing past similar responses with emotional information. 【0810】 (Claim 3) 【0811】 The system according to claim 1, characterized in that the information processing device analyzes user inquiries and emotions using natural language processing technology. 【0812】 "Application example 2 when combining with an emotional engine" 【0813】 (Claim 1) 【0814】 Means for setting up multiple data processing devices, 【0815】 means for acquiring a plurality of responses generated by the data processing device, 【0816】 Means for comparing and evaluating the aforementioned multiple responses, 【0817】 A means of selecting the most appropriate response based on the evaluation results, 【0818】 A means of providing the selected response to the user, 【0819】 A means for analyzing user input information using an emotion recognition engine that determines the user's emotional state, 【0820】 A means for generating an emotionally sensitive response using a generative AI model based on the user's emotional state, 【0821】 A system that includes this. 【0822】 (Claim 2) 【0823】 The system according to claim 1, characterized in that the evaluation means evaluates reliability by comparing it with similar responses in the past, and further incorporates an evaluation based on the user's emotions. 【0824】 (Claim 3) 【0825】 The system according to claim 1, characterized in that the data processing device analyzes inquiries from users using natural language processing technology and generates responses using a generative AI model. [Explanation of symbols] 【0826】 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] Means for setting up multiple information processing devices, means for acquiring a plurality of responses generated by the information processing device, Means for comparing and evaluating the aforementioned multiple responses, A means of selecting the most appropriate response based on the evaluation results, A means of providing the selected response to the user, A system that includes this. [Claim 2] The system according to claim 1, characterized in that the evaluation means evaluates the reliability by comparing it with similar responses in the past. [Claim 3] The system according to claim 1, characterized in that the information processing device analyzes inquiries from users using natural language processing technology.

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