system

Abstract

We provide the system. [Solution] A communication means for receiving user input, A natural language processing means that analyzes the aforementioned input to understand the user's intent, An artificial intelligence means for generating proposals based on the aforementioned analysis, A display means for providing the aforementioned proposal to the user, A storage means for storing the user history, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 [[ID=二十六]] 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In career development within a company, there is a lack of an environment where employees can easily consult, and there is a problem that it is difficult to find a clear career path and growth method. Furthermore, while consultations to the personnel department are concentrated and the burden increases, there is also a problem that consistent advice cannot be provided to individual employees. There is a need for a system that improves such a situation and efficiently supports the career growth of employees. 【Means for Solving the Problems】 【0005】 This invention provides a system comprising communication means for receiving user input, natural language processing means for analyzing the input and understanding its intent, artificial intelligence means for generating suggestions based on the analysis results, display means for providing the suggestions to the user, and storage means for storing the user's history. This system enables employees to interact individually with AI and receive personalized career advice. At the same time, it supports efficient and consistent career development by providing unified support to all employees while reducing the burden on the human resources department. 【0006】 A "user" is an individual who uses the system to receive career counseling and advice. 【0007】 "Input" refers to information about questions and inquiries that users provide to the system. 【0008】 "Communication means" refers to a means of receiving user input into the system via a network. 【0009】 "Natural language processing means" refers to technologies that analyze user input and understand its meaning. 【0010】 "Artificial intelligence means" refers to algorithms and programs that generate appropriate suggestions for users based on analyzed information. 【0011】 "Display means" refers to a device or screen that visually presents the generated suggestions to the user. 【0012】 A "memory device" refers to a device or system that stores a user's consultation history and generated advice, making them accessible for future reference. 【0013】 "Resource data" refers to educational programs and other career support information available within the company. 【0014】 "Feedback" is the act of a user providing an evaluation or opinion in response to advice they have received. 【Brief Description of the Drawings】 【0015】 [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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Modes for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0019】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0029】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 In implementing this invention, first, a server equipped with an information processing device is prepared, and an AI engine and a natural language processing module are operated on it. This server communicates with a terminal operated by a user via a network to provide a career consultation service. The terminal is equipped with an interface that allows the user to input questions about their career, and the input content is sent to the server. 【0037】 The server analyzes the received input using natural language processing to understand the user's intent. This analysis includes morphological analysis and contextual recognition to identify the career information and advice the user is seeking. Based on the analysis results, the server's artificial intelligence generates suggestions tailored to the user. These suggestions refer to internal resource data and are specifically generated in the form of, for example, training information for leadership development or project opportunities related to skill enhancement. 【0038】 The generated suggestions are sent from the server to the user's terminal, which displays them on the screen. This display allows the user to review the suggestions and use them to help develop their career. Users can also provide feedback on the suggestions, and the terminal sends this feedback back to the server. This feedback is stored on the server by a memory device and used to improve the accuracy of future suggestions and manage the user's history. 【0039】 For example, if a user enters "I want to improve my project management skills," the server can suggest relevant in-house training programs and opportunities to participate in project teams. The suggestions are further customized and personalized based on the user's past history and feedback. In this way, the system of the present invention can effectively support the career development of its users. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The user logs into the career consultation system using their device. The user enters their authentication information and prepares to access the system. 【0043】 Step 2: 【0044】 The device sends login information to the server, which then authenticates the user based on that information. If authentication is successful, the server provides the device with a user interface for career counseling. 【0045】 Step 3: 【0046】 Users enter questions and inquiries about their carrier through the interface provided on their device. 【0047】 Step 4: 【0048】 The terminal sends the user's input to the server. The input data is encrypted and reaches the server while maintaining security. 【0049】 Step 5: 【0050】 The server receives the input data and passes it to the natural language processing module. The module analyzes the input data to understand the user's intent and needs. 【0051】 Step 6: 【0052】 The AI ​​system on the server generates optimal suggestions based on the analyzed intent. The AI ​​refers to the company's internal resource database and selects information that meets the user's request. 【0053】 Step 7: 【0054】 The server sends the generated proposal to the terminal. The proposal content is converted into a user-friendly format and prepared. 【0055】 Step 8: 【0056】 The device receives the suggestions and displays them on the user interface. Users can then use the provided information to consider their own career strategy. 【0057】 Step 9: 【0058】 Users can provide feedback on the proposal. They can also add questions about anything unclear or provide evaluation comments on the proposal. 【0059】 Step 10: 【0060】 The device sends feedback to the server. The feedback information is stored using a memory device to improve the accuracy of future suggestions. 【0061】 (Example 1) 【0062】 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." 【0063】 Conventional career counseling service systems have the problem of not being able to adequately adapt to the diverse needs of users. Furthermore, the suggestions offered are general, making it difficult to provide specific support tailored to individual circumstances. In addition, there is a lack of mechanisms for continuously improving service quality by not adequately utilizing user feedback. 【0064】 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. 【0065】 In this invention, the server includes an information exchange means for receiving user questions, a natural language processing device for analyzing the questions and understanding the user's intent, and a machine learning device for generating suggestions based on the analysis. This makes it possible to provide each user with specific career suggestions tailored to their individual circumstances. Furthermore, it allows for continuous improvement of service quality by utilizing user feedback. 【0066】 An "information exchange tool" is a communication interface for receiving user questions and information. 【0067】 A "natural language processing system" is a data analysis system that analyzes received questions and understands the user's intent. 【0068】 A "machine learning device" is a computer system that generates suggestions for users based on analysis results obtained through natural language processing. 【0069】 An "information display device" is an output interface for displaying the generated proposals on the user's terminal. 【0070】 An "information storage device" is a data storage device used to collect and store user interactions and feedback. 【0071】 In carrying out the present invention, a server equipped with an information processing device is used. This server communicates with a terminal operated by a user via a network to provide a career consultation service. First, the server receives the user's questions. For this purpose, an information exchange means connected to the server is used. All input transmitted from the user's terminal is delivered to the server through this communication interface. 【0072】 Next, the server uses a natural language processing unit (NLP) to analyze the received question. This analysis employs NLP techniques such as morphological analysis and contextual recognition, allowing the user's intent to be identified. Furthermore, a machine learning system generates suggestions based on the analysis results. This machine learning system refers to an internal database to provide career development suggestions tailored to the user. For example, it extracts training information for leadership development and project participation opportunities from internal resource data. 【0073】 The generated suggestions are transmitted to the user's terminal via an information display device. The terminal displays the received information on the user's screen. This process allows the user to review the suggestions and use the individually customized information to help them in their career development. Users can also provide feedback on the suggestions. This feedback is stored on a server by an information storage device and used later to improve the accuracy of the suggestions. 【0074】 As a concrete example, consider a scenario where a user inputs "I want to improve my project management skills." The server can then suggest relevant internal training programs and opportunities to participate in project teams. Furthermore, these suggestions are further customized based on the user's past history and feedback. Below is an example of a prompt to input into the generating AI model: "The user is seeking to improve their project management skills. Based on this user's past history and feedback, please generate specific suggestions including relevant training programs and project participation opportunities." 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The user enters career-related questions into the device's interface. Specifically, the user enters something like "I want to improve my project management skills" into a text box. The user's input is sent from the device to the server when the submit button is pressed. The input data is natural language text information. 【0078】 Step 2: 【0079】 The server runs a natural language processing unit to analyze the received input data. Specifically, it performs morphological analysis to divide the text information into words. Next, it performs contextual recognition to analyze the user's intent. The output obtained in this process is structured data about the user's intent and desires. This data is used in the next suggestion generation step. 【0080】 Step 3: 【0081】 The server's machine learning system generates suggestions based on the structured data obtained in the previous step. First, the server queries its internal database to find relevant resources. For example, it identifies internal training programs or project participation opportunities related to improving project management skills. This data is then processed to generate specific suggestions tailored to the user. The output is a data structure containing the suggested content. 【0082】 Step 4: 【0083】 The generated suggestions are sent from the server to the terminal. The server encodes the suggestion data in an appropriate format, such as JSON, and sends it to the terminal as an HTTP response. This ensures that the suggestions reach the user's terminal. 【0084】 Step 5: 【0085】 The device visually displays the received suggestion data to the user. Specifically, it reflects the suggestion content in UI components on the screen. The user can review the suggestions on the screen and use them to help develop their career. 【0086】 Step 6: 【0087】 Users provide feedback on the proposal. This feedback is entered into a feedback form on the device and later sent to the server. The data entered includes the user's evaluation of the proposal and any additional requests. 【0088】 Step 7: 【0089】 The server receives feedback and stores it within the server using an information storage device. This feedback is stored in a database and used when generating suggestions in the future. This improves the accuracy of suggestions and enables the provision of more optimized services to users. 【0090】 (Application Example 1) 【0091】 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." 【0092】 In today's family environment, opportunities for career development counseling are limited, and there is a lack of convenient career support tools, especially for individuals aiming to improve their skills while working. This creates a challenge in that opportunities to receive optimal suggestions for individual career goals are restricted. 【0093】 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. 【0094】 In this invention, the server includes speech recognition means for receiving voice input from a user, natural language processing means for analyzing the input and understanding the user's intent, and artificial intelligence means for generating career suggestions based on the analysis. This makes it possible to facilitate career counseling within the home and provide users with optimal career development advice. 【0095】 A "speech recognition means" is a means for receiving speech input from a user and converting it into text data. 【0096】 "Natural language processing means" are methods for analyzing received text data and understanding the user's intent. 【0097】 "Artificial intelligence means" refers to means for generating suggestions suitable for the user based on analysis. 【0098】 "Voice output means" refers to a means for converting the generated proposal into voice and providing it to the user. 【0099】 A "memory device" is a means of saving a user's past history and feedback to help improve the accuracy of future suggestions. 【0100】 "Internal data" refers to the databases and resources that the system references when generating suggestions. 【0101】 A "robot" is an automated device installed in a home that can provide career counseling. 【0102】 To implement this invention, a robot installed in the home is required. This robot is equipped with speech recognition means to accurately recognize the user's voice input. Specifically, it converts the user's voice into text data using speech recognition technology such as Google® Cloud Speech-to-Text API. Furthermore, natural language processing (NLP) technology is required as a natural language processing means, which the system uses to analyze and understand the user's questions and inquiries. It is conceivable to use a combination of open-source NLP libraries and cloud-based NLP services. 【0103】 Based on the analysis results, the server uses artificial intelligence to generate appropriate career suggestions. These suggestions are personalized based on internal data and the user's past history. A generative AI model based on a transformer architecture is used to provide more accurate suggestions. 【0104】 Subsequently, the generated proposals are provided to the user via voice output through a robot. Text-to-speech technology is used for speech synthesis, ensuring that the proposals are conveyed in a way that is easily understandable to the user. 【0105】 Furthermore, user feedback and responses to suggestions are accumulated through memory systems and used to improve future suggestions. Database technology is used to securely and efficiently store and manage history and feedback information. 【0106】 For example, when a user asks a career question such as "I want to improve my project management skills," the robot can suggest suitable training programs or self-study resources. A possible prompt could be voice-input such as, "Hello, I'd like to learn about project management. Do you have any recommended learning methods?" 【0107】 In this way, a system is created that allows users to easily receive expert career advice in the comfort of their own homes. 【0108】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0109】 Step 1: 【0110】 The user asks the robot a question using voice. For example, they might say, "I want to improve my project management skills." This voice input is received through the robot's microphone. 【0111】 Step 2: 【0112】 The device converts the received audio into text data using the Google Cloud Speech-to-Text API. This API analyzes the audio waveform and transcribes it based on a language model. As a result, the entered question is sent to the server in text format. 【0113】 Step 3: 【0114】 The server uses natural language processing (NLP) to analyze received text data and understand the user's intent. Specifically, it uses morphological analysis to break down words and phrases in a sentence and performs semantic analysis. It receives the user's question text as input and obtains the analysis results. 【0115】 Step 4: 【0116】 The server uses a generative AI model to generate appropriate career suggestions based on the analysis results. It references internal databases and the user's past history information to create personalized advice. It generates suggestion text as output. 【0117】 Step 5: 【0118】 The generated suggestion text is sent from the server to the terminal. The terminal uses text-to-speech technology to convert this suggestion into speech. The advice is then delivered to the user via the terminal's speaker. 【0119】 Step 6: 【0120】 The user provides feedback on the advice given by voice input into the device. This is also converted into text data through speech recognition. The text data is sent to the server and stored in a memory device as feedback. 【0121】 Step 7: 【0122】 The server analyzes the accumulated feedback to improve future career suggestions. It updates database entries, saves them as user-specific historical information, and uses them to improve the accuracy of future suggestions. 【0123】 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. 【0124】 In implementing the present invention, a system configuration is used in which a server equipped with an information processing device and a terminal operated by a user are connected via a network. The server receives input transmitted by the user from the terminal and provides a service for career counseling. 【0125】 After receiving input from the user, the server first uses natural language processing to analyze the text and understand its content and the user's intent. Simultaneously, an emotion engine recognizes the user's emotions based on the input text. This emotion analysis takes into account the user's facial expressions, tone of voice, and word choice to determine the user's emotional state. 【0126】 The analyzed data is passed to artificial intelligence tools, which generate the most suitable career advice and suggestions for the user. The emotion engine also references the user's emotional data at this stage, adjusting the suggestions according to the user's emotional state. For example, for a user feeling anxious, the suggestions are optimized to provide a sense of reassurance. 【0127】 The generated suggestions are sent from the server to the terminal and displayed on the terminal. Users can review these suggestions on their terminal and use them to develop their careers. Users can also input feedback on the provided suggestions into their terminal and continue the consultation process. 【0128】 The emotional data from this feedback and suggestions is recorded in a memory system and used to generate and improve future suggestions. For example, if a user inputs "I'm anxious about the next career step," the server, based on the emotion engine's judgment, tags the emotion as "anxiety" and uses this information to generate supportive advice. For instance, a suggestion might be made to "provide specific advice along with past success stories." This system makes it possible to provide consistent career advice that is tailored to the user's emotions. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 The user operates the device and logs into the career consultation system. The user enters the necessary authentication information and begins accessing the system. 【0132】 Step 2: 【0133】 The device sends login information to the server. The server uses this information to authenticate the user, and if successful, provides the device with an interface for career counseling. 【0134】 Step 3: 【0135】 Users input questions and inquiries about their carrier through the device's interface. 【0136】 Step 4: 【0137】 The terminal sends the entered data to the server. The data reaches the server while ensuring security. 【0138】 Step 5: 【0139】 The server passes the received input data to a natural language processing system. The natural language processing system analyzes the input and understands the user's intent. 【0140】 Step 6: 【0141】 Simultaneously, the server uses an emotion engine to analyze the user's emotional state from the input. The emotion engine infers emotions from the expression and context of the text. 【0142】 Step 7: 【0143】 The server's artificial intelligence generates career suggestions tailored to the user based on the results of natural language processing and sentiment analysis. Sentimental data is considered, and adjustments are made to reflect the user's emotions. 【0144】 Step 8: 【0145】 The server sends the generated suggestions to the terminal. The terminal then displays these suggestions in an easy-to-understand format for the user. 【0146】 Step 9: 【0147】 The user reviews the presented suggestions on their device and enters feedback or additional questions if necessary. 【0148】 Step 10: 【0149】 The device resends user feedback and questions to the server. The server records this information in its memory and uses it to inform future suggestions and responses. 【0150】 (Example 2) 【0151】 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." 【0152】 Conventional career counseling systems merely process user input text as information, making it difficult to provide appropriate suggestions tailored to the user's emotional state. Furthermore, they struggled to effectively utilize user feedback, hindering the provision of consistent and ongoing advice. This presented a challenge in providing personalized counseling support that met user needs. 【0153】 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. 【0154】 In this invention, the server includes a receiving device that receives user input, a language analysis device that analyzes the input to understand the user's intentions, and an emotion recognition device that recognizes the user's emotions based on the analyzed information. This enables the provision of personalized career suggestions that take into account the user's emotional state, and realizes high-quality consultation support. 【0155】 A "receiving device" is a component that has the function of receiving input data transmitted by the user and transferring it to a subsequent processing device. 【0156】 A "language analysis device" is a component that analyzes received input data and provides functions to understand the user's intent and meaning from the text. 【0157】 An "emotion recognition device" is a component that has the function of identifying the user's emotional state based on analyzed information and assigning an emotional tag accordingly. 【0158】 A "knowledge processing device" is a component that generates optimal suggestions based on emotion recognition results and other information, and adjusts the content as needed. 【0159】 A "presentation device" is a component that has the function of providing the generated proposal to the user visually or audibly and displaying it in a form that the user can confirm. 【0160】 A "recording device" is a component that has the function of accumulating user history information and emotional data, and storing this data so that it can be used to generate future suggestions. 【0161】 This system connects a server and a user-operated terminal via a communication network to provide a service that supports users' career consultations. The server first receives user input sent from the terminal. The input is mainly text-based and reflects the content of the user's consultation. 【0162】 The server uses natural language processing (NLP) libraries to analyze the received input. For example, it can utilize open-source tools such as SpaCy or Transformer models. This allows the server to extract the main intent and content from the user's input text. 【0163】 Next, the server utilizes an emotion recognition engine to identify the user's emotions based on the analyzed information. This involves evaluating the tone of voice and the wording of the text to classify the user's emotional state. This process typically involves using tone analyzer APIs or other emotion recognition tools. 【0164】 Based on the analysis and emotion recognition results, the server uses a generative AI model to design the most suitable career advice for the user. A typical generative AI model is a GPT-based model. The model uses prompt sentences as input and generates suggestions that are tailored to the user's experiences and emotions. For example, a possible prompt sentence might be, "I've been feeling anxious about my new job recently. Could you give me some advice on what I should do next?" 【0165】 Finally, the generated suggestions are sent from the server to the terminal. The terminal has an interface to display these suggestions in an easy-to-understand manner for the user. The user can refer to this and use it to make future career decisions. The user can also send feedback on the advice given to the server, which stores this in a recording device. This record is used for future suggestions, so the system continuously learns and improves. 【0166】 This invention is expected to allow users to receive personalized advice tailored to their individual emotional state, thereby supporting them in building a better career. 【0167】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0168】 Step 1: 【0169】 The user enters text into the device. For example, they might enter a message such as, "I'm worried about my next career step." The device then sends this text to the server over the network. The input includes the user's text data. The output is the data transferred from the device to the server. 【0170】 Step 2: 【0171】 The server analyzes the input text received from the terminal using a language analysis device. Specifically, it uses natural language processing libraries such as SpaCy to extract the intent of the text. It identifies key information and intent from the text data received as input and outputs the analysis results as internal data. 【0172】 Step 3: 【0173】 The server identifies emotions using an emotion recognition device based on the analyzed data. In this step, the tone analyzer API is used to evaluate what emotions the user's input represents. As a result, an emotional state such as "anxiety" or "relief" is output. 【0174】 Step 4: 【0175】 The server generates suggestions via a knowledge processing unit based on emotional states and analysis results. This process utilizes a generative AI model, such as a GPT-based model. The input is data tagged with user intentions and emotions, and the output is user-optimized career advice text. 【0176】 Step 5: 【0177】 The server adjusts the generated suggestions according to the user's emotional state. For example, for an anxious user, the suggestions are modified to provide reassurance. The server then references emotional data to optimize the suggestions. The adjusted suggestion text is then output. 【0178】 Step 6: 【0179】 The server sends the final proposal to the terminal. The terminal receives this proposal and displays it in an easy-to-understand format for the user. The input is the revised proposal text, and the output is the information displayed on the user's screen. 【0180】 Step 7: 【0181】 The user can review the displayed advice and enter feedback into the terminal. The terminal sends this feedback to the server. The input is the user's feedback data, and the output is the transmission of the feedback to the server. 【0182】 Step 8: 【0183】 The server stores the received feedback in a recording device. This data will be used to improve future suggestions. The input is the feedback data, and the output is the data stored in the recording device. 【0184】 (Application Example 2) 【0185】 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". 【0186】 In modern society, there are few systems that allow users to receive accurate career counseling while simultaneously experiencing their emotions. In particular, there is a lack of individualized support that takes into account emotional states, which is a challenge in providing appropriate support that meets the needs of users. 【0187】 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. 【0188】 In this invention, the server includes communication means for receiving user input, natural language processing means for analyzing the input and understanding the user's intent, and emotion analysis means for recognizing the emotional state based on the input. This makes it possible to provide appropriate career advice individually based on the user's intent and emotions. 【0189】 "Communication means for receiving user input" refers to a device or method for transmitting data or information from a user to a server via a network. 【0190】 "Natural language processing means" refers to technologies that analyze text data received from users and understand its meaning and intent. 【0191】 "Emotional analysis methods" refer to technologies that recognize and determine an emotional state from user input data, accompanying audio, facial expressions, and other information. 【0192】 "Artificial intelligence tools" refer to algorithms and systems that generate appropriate suggestions and advice based on analyzed data and emotional information. 【0193】 "Display means" refers to a display or interface used to output generated proposals and information in a format that the user can understand. 【0194】 "Memory tools" refer to storage technologies that store suggestions, user feedback, and emotional data for future reference and analysis. 【0195】 An "internal database" is a collection of information used within a system, and it is a source of information that is referenced to enhance suggestions for users. 【0196】 To implement this invention, a system configuration is required in which a server and a user's terminal are connected via a network. The server acquires data using communication means to receive input from the user and analyzes the text using natural language processing means. This analysis makes it possible to accurately understand the user's intent. 【0197】 Next, the server uses emotion analysis tools to detect the user's emotional state. This emotion analysis uses information such as expressions and tone of voice contained in the user's input. For example, by analyzing the user's tone of voice through speech recognition and adding emotional information from voice and facial expressions to text analysis, the quality of suggestions can be improved. 【0198】 The analysis results are passed to artificial intelligence (AI) tools, which then generate optimized career advice and suggestions. The AI ​​consults its internal database and, based on the analysis results and sentiment data, derives the most beneficial information for the user. 【0199】 The generated suggestions are sent to the user's terminal via a display device. The user can review the information presented on their terminal and use it to develop their career. At this stage, feedback from the user is sent to the server and stored in a memory device. This feedback will be used to improve future suggestions. 【0200】 The software components to be used include Google Cloud's NLP service for natural language processing and Microsoft Azure's sentiment recognition API for sentiment analysis. Additionally, OpenAI's AI model will be used for artificial intelligence-based suggestion generation. 【0201】 For example, when a user inputs "I'm anxious about my next career step," the emotion analysis recognizes this anxiety. In response, the AI ​​model can generate suggestions that "provide specific advice along with past success stories." 【0202】 Examples of prompt statements are as follows: 【0203】 "Generate encouraging words and specific advice for a user who is feeling anxious about their first day at a new workplace, referencing their past successes." 【0204】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0205】 Step 1: 【0206】 The server receives input from the user. This input is received as text or audio data. The server receives this data using a communication protocol and temporarily stores it for the next processing step. 【0207】 Step 2: 【0208】 The server passes the received input data to a natural language processing system. Here, an NLP engine (e.g., Google Cloud NLP service) is used to perform grammatical and semantic analysis on the text data, and output data to understand the user's intent. At this stage, the input text is converted into a structured data format. 【0209】 Step 3: 【0210】 The server passes the text analysis results to the sentiment analysis tool. The sentiment analysis tool generates sentiment data based on the characteristics of the input text and pronunciation. Specifically, it uses the Microsoft Azure sentiment recognition API to output labels such as "anxiety" and "joy" through a sentiment evaluation model. These labels indicate the user's emotional state. 【0211】 Step 4: 【0212】 The server sends text analysis results and sentiment data to an artificial intelligence (AI) model. The AI ​​model (e.g., OpenAI's AI model) uses this data to generate the most suitable suggestions for the user. The AI ​​refers to an internal database and outputs advice tailored to the user's situation. 【0213】 Step 5: 【0214】 The generated suggestions are sent from the server to the user's terminal. The terminal displays the received suggestions on its screen. The user reviews them and uses them to develop their career path as needed. 【0215】 Step 6: 【0216】 Users send feedback on their suggestions to the server via their device. The feedback is sent as text data, which the server stores in a memory device. The stored data is used as reference data for future suggestions. 【0217】 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. 【0218】 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. 【0219】 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. 【0220】 [Second Embodiment] 【0221】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0222】 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. 【0223】 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). 【0224】 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. 【0225】 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. 【0226】 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). 【0227】 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. 【0228】 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. 【0229】 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. 【0230】 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. 【0231】 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. 【0232】 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". 【0233】 In implementing this invention, first, a server equipped with an information processing device is prepared, and an AI engine and a natural language processing module are operated on it. This server communicates with a terminal operated by a user via a network to provide a career consultation service. The terminal is equipped with an interface that allows the user to input questions about their career, and the input content is sent to the server. 【0234】 The server analyzes the received input using natural language processing to understand the user's intent. This analysis includes morphological analysis and contextual recognition to identify the career information and advice the user is seeking. Based on the analysis results, the server's artificial intelligence generates suggestions tailored to the user. These suggestions refer to internal resource data and are specifically generated in the form of, for example, training information for leadership development or project opportunities related to skill enhancement. 【0235】 The generated suggestions are sent from the server to the user's terminal, which displays them on the screen. This display allows the user to review the suggestions and use them to help develop their career. Users can also provide feedback on the suggestions, and the terminal sends this feedback back to the server. This feedback is stored on the server by a memory device and used to improve the accuracy of future suggestions and manage the user's history. 【0236】 For example, if a user enters "I want to improve my project management skills," the server can suggest relevant in-house training programs and opportunities to participate in project teams. The suggestions are further customized and personalized based on the user's past history and feedback. In this way, the system of the present invention can effectively support the career development of its users. 【0237】 The following describes the processing flow. 【0238】 Step 1: 【0239】 The user logs into the career consultation system using their device. The user enters their authentication information and prepares to access the system. 【0240】 Step 2: 【0241】 The device sends login information to the server, which then authenticates the user based on that information. If authentication is successful, the server provides the device with a user interface for career counseling. 【0242】 Step 3: 【0243】 Users enter questions and inquiries about their carrier through the interface provided on their device. 【0244】 Step 4: 【0245】 The terminal sends the user's input to the server. The input data is encrypted and reaches the server while maintaining security. 【0246】 Step 5: 【0247】 The server receives the input data and passes it to the natural language processing module. The module analyzes the input data to understand the user's intent and needs. 【0248】 Step 6: 【0249】 The AI ​​system on the server generates optimal suggestions based on the analyzed intent. The AI ​​refers to the company's internal resource database and selects information that meets the user's request. 【0250】 Step 7: 【0251】 The server sends the generated proposal to the terminal. The proposal content is converted into a user-friendly format and prepared. 【0252】 Step 8: 【0253】 The device receives the suggestions and displays them on the user interface. Users can then use the provided information to consider their own career strategy. 【0254】 Step 9: 【0255】 Users can provide feedback on the proposal. They can also add questions about anything unclear or provide evaluation comments on the proposal. 【0256】 Step 10: 【0257】 The device sends feedback to the server. The feedback information is stored using a memory device to improve the accuracy of future suggestions. 【0258】 (Example 1) 【0259】 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." 【0260】 Conventional career counseling service systems have the problem of not being able to adequately adapt to the diverse needs of users. Furthermore, the suggestions offered are general, making it difficult to provide specific support tailored to individual circumstances. In addition, there is a lack of mechanisms for continuously improving service quality by not adequately utilizing user feedback. 【0261】 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. 【0262】 In this invention, the server includes an information exchange means for receiving user questions, a natural language processing device for analyzing the questions and understanding the user's intent, and a machine learning device for generating suggestions based on the analysis. This makes it possible to provide each user with specific career suggestions tailored to their individual circumstances. Furthermore, it allows for continuous improvement of service quality by utilizing user feedback. 【0263】 An "information exchange tool" is a communication interface for receiving user questions and information. 【0264】 A "natural language processing system" is a data analysis system that analyzes received questions and understands the user's intent. 【0265】 A "machine learning device" is a computer system that generates suggestions for users based on analysis results obtained through natural language processing. 【0266】 An "information display device" is an output interface for displaying the generated proposals on the user's terminal. 【0267】 An "information storage device" is a data storage device used to collect and store user interactions and feedback. 【0268】 In carrying out the present invention, a server equipped with an information processing device is used. This server communicates with a terminal operated by a user via a network to provide a career consultation service. First, the server receives the user's questions. For this purpose, an information exchange means connected to the server is used. All input transmitted from the user's terminal is delivered to the server through this communication interface. 【0269】 Next, the server uses a natural language processing unit (NLP) to analyze the received question. This analysis employs NLP techniques such as morphological analysis and contextual recognition, allowing the user's intent to be identified. Furthermore, a machine learning system generates suggestions based on the analysis results. This machine learning system refers to an internal database to provide career development suggestions tailored to the user. For example, it extracts training information for leadership development and project participation opportunities from internal resource data. 【0270】 The generated suggestions are transmitted to the user's terminal via an information display device. The terminal displays the received information on the user's screen. This process allows the user to review the suggestions and use the individually customized information to help them in their career development. Users can also provide feedback on the suggestions. This feedback is stored on a server by an information storage device and used later to improve the accuracy of the suggestions. 【0271】 As a concrete example, consider a scenario where a user inputs "I want to improve my project management skills." The server can then suggest relevant internal training programs and opportunities to participate in project teams. Furthermore, these suggestions are further customized based on the user's past history and feedback. Below is an example of a prompt to input into the generating AI model: "The user is seeking to improve their project management skills. Based on this user's past history and feedback, please generate specific suggestions including relevant training programs and project participation opportunities." 【0272】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0273】 Step 1: 【0274】 The user enters career-related questions into the device's interface. Specifically, the user enters something like "I want to improve my project management skills" into a text box. The user's input is sent from the device to the server when the submit button is pressed. The input data is natural language text information. 【0275】 Step 2: 【0276】 The server executes a natural language processing device to analyze the received input data. As specific operations, it performs morphological analysis and splits the text information into words. Next, it performs context recognition to analyze the user's intention. The output obtained in this process is structured data regarding the user's intention and desire. This data is used in the following proposal generation step. 【0277】 Step 3: 【0278】 The machine learning device of the server generates proposals based on the structured data obtained in the previous step. First, the server executes a query on its internal database to search for relevant resources. For example, it identifies in-house training programs related to improving project management skills and opportunities to participate in projects. This data is processed to generate specific proposals suitable for the user. The output is a data structure containing the proposal content. 【0279】 Step 4: 【0280】 The generated proposals are sent from the server to the terminal. The server encodes the proposal data in an appropriate format such as JSON and sends it to the terminal as an HTTP response. As a result, the proposal content reaches the user's terminal. 【0281】 Step 5: 【0282】 The terminal visually displays the received proposal data to the user. As specific operations, it reflects the proposal content in UI components on the screen. The user can view the proposal on the screen and utilize it for their career development. 【0283】 Step 6: 【0284】 The user inputs feedback on the proposal. The feedback is input into the feedback form of the terminal and later sent to the server. The data input is the user's evaluation of the proposal and additional requests. 【0285】 Step 7: 【0286】 The server receives the feedback and stores it in the server by the information storage device. The feedback is stored in the database and used when generating proposals from the next time onwards. As a result, the accuracy of the proposals is improved, and it becomes possible to provide services optimized by the user. 【0287】 (Application Example 1) 【0288】 Next, Application Example 1 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". 【0289】 In a modern home environment, there are limited places to consult on career formation, and there is a lack of convenient career support means, especially for individuals who aim to improve their skills while working. As a result, there is a problem that the opportunity to receive optimal proposals for individual career goals is limited. 【0290】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means. 【0291】 In this invention, the server includes voice recognition means for receiving the voice input of the user, natural language processing means for analyzing the input to understand the user's intention, and artificial intelligence means for generating a career proposal based on the analysis. As a result, it becomes possible to facilitate career consultations within the home and provide optimal career formation advice to the user. 【0292】 The "voice recognition means" is means for receiving the voice input from the user and converting it into text data. 【0293】 The "natural language processing means" is means for analyzing the received text data and understanding the user's intention. 【0294】 "Artificial intelligence means" refers to means for generating suggestions suitable for the user based on analysis. 【0295】 "Voice output means" refers to a means for converting the generated proposal into voice and providing it to the user. 【0296】 A "memory device" is a means of saving a user's past history and feedback to help improve the accuracy of future suggestions. 【0297】 "Internal data" refers to the databases and resources that the system references when generating suggestions. 【0298】 A "robot" is an automated device installed in a home that can provide career counseling. 【0299】 To implement this invention, a robot installed in the home is required. This robot is equipped with speech recognition means to accurately recognize the user's voice input. Specifically, it converts the user's voice into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. Furthermore, natural language processing (NLP) technology is required as a means of natural language processing, which the system uses to analyze and understand the user's questions and inquiries. It is conceivable to use a combination of open-source NLP libraries and cloud-based NLP services. 【0300】 Based on the analysis results, the server uses artificial intelligence to generate appropriate career suggestions. These suggestions are personalized based on internal data and the user's past history. A generative AI model based on a transformer architecture is used to provide more accurate suggestions. 【0301】 Subsequently, the generated proposals are provided to the user via voice output through a robot. Text-to-speech technology is used for speech synthesis, ensuring that the proposals are conveyed in a way that is easily understandable to the user. 【0302】 Furthermore, through the memory means, the feedback from the user and the responses to the suggestions are accumulated and used to improve the subsequent suggestions. Using database technology, the history and feedback information are stored and managed safely and efficiently. 【0303】 As a specific example, when a user asks a career question such as "I want to improve my project management skills", the robot can propose suitable training programs and self-learning resources. As an example of a prompt sentence, it is possible to input it by voice like "Hello, I want to learn about project management. Is there any recommended learning method?" 【0304】 In this way, a system is realized in which users can easily receive professional career advice within the home. 【0305】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0306】 Step 1: 【0307】 The user asks the robot a question by voice. For example, say "I want to improve my project management skills". This voice input is received through the robot's microphone. 【0308】 Step 2: 【0309】 The terminal converts the received voice into text data by the Google Cloud Speech-to-Text API. This API analyzes the voice waveform and performs text conversion based on the language model. As a result, the input question is sent to the server in text format. ) 【0310】 Step 3: 【0311】 The server uses natural language processing (NLP) to analyze received text data and understand the user's intent. Specifically, it uses morphological analysis to break down words and phrases in a sentence and performs semantic analysis. It receives the user's question text as input and obtains the analysis results. 【0312】 Step 4: 【0313】 The server uses a generative AI model to generate appropriate career suggestions based on the analysis results. It references internal databases and the user's past history information to create personalized advice. It generates suggestion text as output. 【0314】 Step 5: 【0315】 The generated suggestion text is sent from the server to the terminal. The terminal uses text-to-speech technology to convert this suggestion into speech. The advice is then delivered to the user via the terminal's speaker. 【0316】 Step 6: 【0317】 The user provides feedback on the advice given by voice input into the device. This is also converted into text data through speech recognition. The text data is sent to the server and stored in a memory device as feedback. 【0318】 Step 7: 【0319】 The server analyzes the accumulated feedback to improve future career suggestions. It updates database entries, saves them as user-specific historical information, and uses them to improve the accuracy of future suggestions. 【0320】 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. 【0321】 In implementing the present invention, a system configuration is used in which a server equipped with an information processing device and a terminal operated by a user are connected via a network. The server receives input transmitted by the user from the terminal and provides a service for career counseling. 【0322】 After receiving input from the user, the server first uses natural language processing to analyze the text and understand its content and the user's intent. Simultaneously, an emotion engine recognizes the user's emotions based on the input text. This emotion analysis takes into account the user's facial expressions, tone of voice, and word choice to determine the user's emotional state. 【0323】 The analyzed data is passed to artificial intelligence tools, which generate the most suitable career advice and suggestions for the user. The emotion engine also references the user's emotional data at this stage, adjusting the suggestions according to the user's emotional state. For example, for a user feeling anxious, the suggestions are optimized to provide a sense of reassurance. 【0324】 The generated suggestions are sent from the server to the terminal and displayed on the terminal. Users can review these suggestions on their terminal and use them to develop their careers. Users can also input feedback on the provided suggestions into their terminal and continue the consultation process. 【0325】 The emotional data from this feedback and suggestions is recorded in a memory system and used to generate and improve future suggestions. For example, if a user inputs "I'm anxious about the next career step," the server, based on the emotion engine's judgment, tags the emotion as "anxiety" and uses this information to generate supportive advice. For instance, a suggestion might be made to "provide specific advice along with past success stories." This system makes it possible to provide consistent career advice that is tailored to the user's emotions. 【0326】 The following describes the processing flow. 【0327】 Step 1: 【0328】 The user operates the device and logs into the career consultation system. The user enters the necessary authentication information and begins accessing the system. 【0329】 Step 2: 【0330】 The device sends login information to the server. The server uses this information to authenticate the user, and if successful, provides the device with an interface for career counseling. 【0331】 Step 3: 【0332】 Users input questions and inquiries about their carrier through the device's interface. 【0333】 Step 4: 【0334】 The terminal sends the entered data to the server. The data reaches the server while ensuring security. 【0335】 Step 5: 【0336】 The server passes the received input data to a natural language processing system. The natural language processing system analyzes the input and understands the user's intent. 【0337】 Step 6: 【0338】 Simultaneously, the server uses an emotion engine to analyze the user's emotional state from the input. The emotion engine infers emotions from the expression and context of the text. 【0339】 Step 7: 【0340】 The server's artificial intelligence generates career suggestions tailored to the user based on the results of natural language processing and sentiment analysis. Sentimental data is considered, and adjustments are made to reflect the user's emotions. 【0341】 Step 8: 【0342】 The server sends the generated suggestions to the terminal. The terminal then displays these suggestions in an easy-to-understand format for the user. 【0343】 Step 9: 【0344】 The user reviews the presented suggestions on their device and enters feedback or additional questions if necessary. 【0345】 Step 10: 【0346】 The device resends user feedback and questions to the server. The server records this information in its memory and uses it to inform future suggestions and responses. 【0347】 (Example 2) 【0348】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0349】 Conventional career counseling systems merely process user input text as information, making it difficult to provide appropriate suggestions tailored to the user's emotional state. Furthermore, they struggled to effectively utilize user feedback, hindering the provision of consistent and ongoing advice. This presented a challenge in providing personalized counseling support that met user needs. 【0350】 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. 【0351】 In this invention, the server includes a receiving device that receives user input, a language analysis device that analyzes the input to understand the user's intentions, and an emotion recognition device that recognizes the user's emotions based on the analyzed information. This enables the provision of personalized career suggestions that take into account the user's emotional state, and realizes high-quality consultation support. 【0352】 A "receiving device" is a component that has the function of receiving input data transmitted by the user and transferring it to a subsequent processing device. 【0353】 A "language analysis device" is a component that analyzes received input data and provides functions to understand the user's intent and meaning from the text. 【0354】 An "emotion recognition device" is a component that has the function of identifying the user's emotional state based on analyzed information and assigning an emotional tag accordingly. 【0355】 A "knowledge processing device" is a component that generates optimal suggestions based on emotion recognition results and other information, and adjusts the content as needed. 【0356】 A "presentation device" is a component that has the function of providing the generated proposal to the user visually or audibly and displaying it in a form that the user can confirm. 【0357】 A "recording device" is a component that has the function of accumulating user history information and emotional data, and storing this data so that it can be used to generate future suggestions. 【0358】 This system connects a server and a user-operated terminal via a communication network to provide a service that supports users' career consultations. The server first receives user input sent from the terminal. The input is mainly text-based and reflects the content of the user's consultation. 【0359】 The server uses natural language processing (NLP) libraries to analyze the received input. For example, it can utilize open-source tools such as SpaCy or Transformer models. This allows the server to extract the main intent and content from the user's input text. 【0360】 Next, the server utilizes an emotion recognition engine to identify the user's emotions based on the analyzed information. This involves evaluating the tone of voice and the wording of the text to classify the user's emotional state. This process typically involves using tone analyzer APIs or other emotion recognition tools. 【0361】 Based on the analysis and emotion recognition results, the server uses a generative AI model to design the most suitable career advice for the user. A typical generative AI model is a GPT-based model. The model uses prompt sentences as input and generates suggestions that are tailored to the user's experiences and emotions. For example, a possible prompt sentence might be, "I've been feeling anxious about my new job recently. Could you give me some advice on what I should do next?" 【0362】 Finally, the generated suggestions are sent from the server to the terminal. The terminal has an interface to display these suggestions in an easy-to-understand manner for the user. The user can refer to this and use it to make future career decisions. The user can also send feedback on the advice given to the server, which stores this in a recording device. This record is used for future suggestions, so the system continuously learns and improves. 【0363】 This invention is expected to allow users to receive personalized advice tailored to their individual emotional state, thereby supporting them in building a better career. 【0364】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0365】 Step 1: 【0366】 The user enters text into the device. For example, they might enter a message such as, "I'm worried about my next career step." The device then sends this text to the server over the network. The input includes the user's text data. The output is the data transferred from the device to the server. 【0367】 Step 2: 【0368】 The server analyzes the input text received from the terminal using a language analysis device. Specifically, it uses natural language processing libraries such as SpaCy to extract the intent of the text. It identifies key information and intent from the text data received as input and outputs the analysis results as internal data. 【0369】 Step 3: 【0370】 The server identifies emotions using an emotion recognition device based on the analyzed data. In this step, the tone analyzer API is used to evaluate what emotions the user's input represents. As a result, an emotional state such as "anxiety" or "relief" is output. 【0371】 Step 4: 【0372】 The server generates suggestions via a knowledge processing unit based on emotional states and analysis results. This process utilizes a generative AI model, such as a GPT-based model. The input is data tagged with user intentions and emotions, and the output is user-optimized career advice text. 【0373】 Step 5: 【0374】 The server adjusts the generated suggestions according to the user's emotional state. For example, for an anxious user, the suggestions are modified to provide reassurance. The server then references emotional data to optimize the suggestions. The adjusted suggestion text is then output. 【0375】 Step 6: 【0376】 The server sends the final proposal to the terminal. The terminal receives this proposal and displays it in an easy-to-understand format for the user. The input is the revised proposal text, and the output is the information displayed on the user's screen. 【0377】 Step 7: 【0378】 The user can review the displayed advice and enter feedback into the terminal. The terminal sends this feedback to the server. The input is the user's feedback data, and the output is the transmission of the feedback to the server. 【0379】 Step 8: 【0380】 The server stores the received feedback in a recording device. This data will be used to improve future suggestions. The input is the feedback data, and the output is the data stored in the recording device. 【0381】 (Application Example 2) 【0382】 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." 【0383】 In modern society, there are few systems that allow users to receive accurate career counseling while simultaneously experiencing their emotions. In particular, there is a lack of individualized support that takes into account emotional states, which is a challenge in providing appropriate support that meets the needs of users. 【0384】 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. 【0385】 In this invention, the server includes communication means for receiving user input, natural language processing means for analyzing the input and understanding the user's intent, and emotion analysis means for recognizing the emotional state based on the input. This makes it possible to provide appropriate career advice individually based on the user's intent and emotions. 【0386】 "Communication means for receiving user input" refers to a device or method for transmitting data or information from a user to a server via a network. 【0387】 "Natural language processing means" refers to technologies that analyze text data received from users and understand its meaning and intent. 【0388】 "Emotional analysis methods" refer to technologies that recognize and determine an emotional state from user input data, accompanying audio, facial expressions, and other information. 【0389】 "Artificial intelligence tools" refer to algorithms and systems that generate appropriate suggestions and advice based on analyzed data and emotional information. 【0390】 "Display means" refers to a display or interface used to output generated proposals and information in a format that the user can understand. 【0391】 "Memory tools" refer to storage technologies that store suggestions, user feedback, and emotional data for future reference and analysis. 【0392】 An "internal database" is a collection of information used within a system, and it is a source of information that is referenced to enhance suggestions for users. 【0393】 To implement this invention, a system configuration is required in which a server and a user's terminal are connected via a network. The server acquires data using communication means to receive input from the user and analyzes the text using natural language processing means. This analysis makes it possible to accurately understand the user's intent. 【0394】 Next, the server uses emotion analysis tools to detect the user's emotional state. This emotion analysis uses information such as expressions and tone of voice contained in the user's input. For example, by analyzing the user's tone of voice through speech recognition and adding emotional information from voice and facial expressions to text analysis, the quality of suggestions can be improved. 【0395】 The analysis results are passed to artificial intelligence (AI) tools, which then generate optimized career advice and suggestions. The AI ​​consults its internal database and, based on the analysis results and sentiment data, derives the most beneficial information for the user. 【0396】 The generated suggestions are sent to the user's terminal via a display device. The user can review the information presented on their terminal and use it to develop their career. At this stage, feedback from the user is sent to the server and stored in a memory device. This feedback will be used to improve future suggestions. 【0397】 The software components to be used include Google Cloud's NLP service for natural language processing and Microsoft Azure's sentiment recognition API for sentiment analysis. Additionally, OpenAI's AI model will be used for suggestion generation using artificial intelligence. 【0398】 For example, when a user inputs "I'm anxious about my next career step," the emotion analysis recognizes this anxiety. In response, the AI ​​model can generate suggestions that "provide specific advice along with past success stories." 【0399】 Examples of prompt statements are as follows: 【0400】 "Generate encouraging words and specific advice for a user who is feeling anxious about their first day at a new workplace, referencing their past successes." 【0401】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0402】 Step 1: 【0403】 The server receives input from the user. This input is received as text or audio data. The server receives this data using a communication protocol and temporarily stores it for the next processing step. 【0404】 Step 2: 【0405】 The server passes the received input data to a natural language processing system. Here, an NLP engine (e.g., Google Cloud NLP service) is used to perform grammatical and semantic analysis on the text data, and output data to understand the user's intent. At this stage, the input text is converted into a structured data format. 【0406】 Step 3: 【0407】 The server passes the text analysis results to the sentiment analysis tool. The sentiment analysis tool generates sentiment data based on the characteristics of the input text and pronunciation. Specifically, it uses the Microsoft Azure sentiment recognition API to output labels such as "anxiety" and "joy" through a sentiment evaluation model. These labels indicate the user's emotional state. 【0408】 Step 4: 【0409】 The server sends text analysis results and sentiment data to an artificial intelligence (AI) model. The AI ​​model (e.g., OpenAI's AI model) uses this data to generate the most suitable suggestions for the user. The AI ​​refers to an internal database and outputs advice tailored to the user's situation. 【0410】 Step 5: 【0411】 The generated suggestions are sent from the server to the user's terminal. The terminal displays the received suggestions on its screen. The user reviews them and uses them to develop their career path as needed. 【0412】 Step 6: 【0413】 Users send feedback on their suggestions to the server via their device. The feedback is sent as text data, which the server stores in a memory device. The stored data is used as reference data for future suggestions. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 [Third Embodiment] 【0418】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0419】 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. 【0420】 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). 【0421】 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. 【0422】 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. 【0423】 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). 【0424】 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. 【0425】 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. 【0426】 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. 【0427】 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. 【0428】 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. 【0429】 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". 【0430】 In implementing this invention, first, a server equipped with an information processing device is prepared, and an AI engine and a natural language processing module are operated on it. This server communicates with a terminal operated by a user via a network to provide a career consultation service. The terminal is equipped with an interface that allows the user to input questions about their career, and the input content is sent to the server. 【0431】 The server analyzes the received input using natural language processing to understand the user's intent. This analysis includes morphological analysis and contextual recognition to identify the career information and advice the user is seeking. Based on the analysis results, the server's artificial intelligence generates suggestions tailored to the user. These suggestions refer to internal resource data and are specifically generated in the form of, for example, training information for leadership development or project opportunities related to skill enhancement. 【0432】 The generated suggestions are sent from the server to the user's terminal, which displays them on the screen. This display allows the user to review the suggestions and use them to help develop their career. Users can also provide feedback on the suggestions, and the terminal sends this feedback back to the server. This feedback is stored on the server by a memory device and used to improve the accuracy of future suggestions and manage the user's history. 【0433】 For example, if a user enters "I want to improve my project management skills," the server can suggest relevant in-house training programs and opportunities to participate in project teams. The suggestions are further customized and personalized based on the user's past history and feedback. In this way, the system of the present invention can effectively support the career development of its users. 【0434】 The following describes the processing flow. 【0435】 Step 1: 【0436】 The user logs into the career consultation system using their device. The user enters their authentication information and prepares to access the system. 【0437】 Step 2: 【0438】 The device sends login information to the server, which then authenticates the user based on that information. If authentication is successful, the server provides the device with a user interface for career counseling. 【0439】 Step 3: 【0440】 Users enter questions and inquiries about their carrier through the interface provided on their device. 【0441】 Step 4: 【0442】 The terminal sends the user's input to the server. The input data is encrypted and reaches the server while maintaining security. 【0443】 Step 5: 【0444】 The server receives the input data and passes it to the natural language processing module. The module analyzes the input data to understand the user's intent and needs. 【0445】 Step 6: 【0446】 The AI ​​system on the server generates optimal suggestions based on the analyzed intent. The AI ​​refers to the company's internal resource database and selects information that meets the user's request. 【0447】 Step 7: 【0448】 The server sends the generated proposal to the terminal. The proposal content is converted into a user-friendly format and prepared. 【0449】 Step 8: 【0450】 The device receives the suggestions and displays them on the user interface. Users can then use the provided information to consider their own career strategy. 【0451】 Step 9: 【0452】 Users can provide feedback on the proposal. They can also add questions about anything unclear or provide evaluation comments on the proposal. 【0453】 Step 10: 【0454】 The device sends feedback to the server. The feedback information is stored using a memory device to improve the accuracy of future suggestions. 【0455】 (Example 1) 【0456】 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." 【0457】 Conventional career counseling service systems have the problem of not being able to adequately adapt to the diverse needs of users. Furthermore, the suggestions offered are general, making it difficult to provide specific support tailored to individual circumstances. In addition, there is a lack of mechanisms for continuously improving service quality by not adequately utilizing user feedback. 【0458】 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. 【0459】 In this invention, the server includes an information exchange means for receiving user questions, a natural language processing device for analyzing the questions and understanding the user's intent, and a machine learning device for generating suggestions based on the analysis. This makes it possible to provide each user with specific career suggestions tailored to their individual circumstances. Furthermore, it allows for continuous improvement of service quality by utilizing user feedback. 【0460】 An "information exchange tool" is a communication interface for receiving user questions and information. 【0461】 A "natural language processing system" is a data analysis system that analyzes received questions and understands the user's intent. 【0462】 A "machine learning device" is a computer system that generates suggestions for users based on analysis results obtained through natural language processing. 【0463】 An "information display device" is an output interface for displaying the generated proposals on the user's terminal. 【0464】 An "information storage device" is a data storage device used to collect and store user interactions and feedback. 【0465】 In carrying out the present invention, a server equipped with an information processing device is used. This server communicates with a terminal operated by a user via a network to provide a career consultation service. First, the server receives the user's questions. For this purpose, an information exchange means connected to the server is used. All input transmitted from the user's terminal is delivered to the server through this communication interface. 【0466】 Next, the server uses a natural language processing unit (NLP) to analyze the received question. This analysis employs NLP techniques such as morphological analysis and contextual recognition, allowing the user's intent to be identified. Furthermore, a machine learning system generates suggestions based on the analysis results. This machine learning system refers to an internal database to provide career development suggestions tailored to the user. For example, it extracts training information for leadership development and project participation opportunities from internal resource data. 【0467】 The generated suggestions are transmitted to the user's terminal via an information display device. The terminal displays the received information on the user's screen. This process allows the user to review the suggestions and use the individually customized information to help them in their career development. Users can also provide feedback on the suggestions. This feedback is stored on a server by an information storage device and used later to improve the accuracy of the suggestions. 【0468】 As a concrete example, consider a scenario where a user inputs "I want to improve my project management skills." The server can then suggest relevant internal training programs and opportunities to participate in project teams. Furthermore, these suggestions are further customized based on the user's past history and feedback. Below is an example of a prompt to input into the generating AI model: "The user is seeking to improve their project management skills. Based on this user's past history and feedback, please generate specific suggestions including relevant training programs and project participation opportunities." 【0469】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0470】 Step 1: 【0471】 The user enters career-related questions into the device's interface. Specifically, the user enters something like "I want to improve my project management skills" into a text box. The user's input is sent from the device to the server when the submit button is pressed. The input data is natural language text information. 【0472】 Step 2: 【0473】 The server runs a natural language processing unit to analyze the received input data. Specifically, it performs morphological analysis to divide the text information into words. Next, it performs contextual recognition to analyze the user's intent. The output obtained in this process is structured data about the user's intent and desires. This data is used in the next suggestion generation step. 【0474】 Step 3: 【0475】 The server's machine learning system generates suggestions based on the structured data obtained in the previous step. First, the server queries its internal database to find relevant resources. For example, it identifies internal training programs or project participation opportunities related to improving project management skills. This data is then processed to generate specific suggestions tailored to the user. The output is a data structure containing the suggested content. 【0476】 Step 4: 【0477】 The generated suggestions are sent from the server to the terminal. The server encodes the suggestion data in an appropriate format, such as JSON, and sends it to the terminal as an HTTP response. This ensures that the suggestions reach the user's terminal. 【0478】 Step 5: 【0479】 The device visually displays the received suggestion data to the user. Specifically, it reflects the suggestion content in UI components on the screen. The user can review the suggestions on the screen and use them to help develop their career. 【0480】 Step 6: 【0481】 Users provide feedback on the proposal. This feedback is entered into a feedback form on the device and later sent to the server. The data entered includes the user's evaluation of the proposal and any additional requests. 【0482】 Step 7: 【0483】 The server receives feedback and stores it within the server using an information storage device. This feedback is stored in a database and used when generating suggestions in the future. This improves the accuracy of suggestions and enables the provision of more optimized services to users. 【0484】 (Application Example 1) 【0485】 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." 【0486】 In today's family environment, opportunities for career development counseling are limited, and there is a lack of convenient career support tools, especially for individuals aiming to improve their skills while working. This creates a challenge in that opportunities to receive optimal suggestions for individual career goals are restricted. 【0487】 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. 【0488】 In this invention, the server includes speech recognition means for receiving voice input from a user, natural language processing means for analyzing the input and understanding the user's intent, and artificial intelligence means for generating career suggestions based on the analysis. This makes it possible to facilitate career counseling within the home and provide users with optimal career development advice. 【0489】 A "speech recognition means" is a means for receiving speech input from a user and converting it into text data. 【0490】 "Natural language processing means" are methods for analyzing received text data and understanding the user's intent. 【0491】 "Artificial intelligence means" refers to means for generating suggestions suitable for the user based on analysis. 【0492】 "Voice output means" refers to a means for converting the generated proposal into voice and providing it to the user. 【0493】 A "memory device" is a means of saving a user's past history and feedback to help improve the accuracy of future suggestions. 【0494】 "Internal data" refers to the databases and resources that the system references when generating suggestions. 【0495】 A "robot" is an automated device installed in a home that can provide career counseling. 【0496】 To implement this invention, a robot installed in the home is required. This robot is equipped with speech recognition means to accurately recognize the user's voice input. Specifically, it converts the user's voice into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. Furthermore, natural language processing (NLP) technology is required as a means of natural language processing, which the system uses to analyze and understand the user's questions and inquiries. It is conceivable to use a combination of open-source NLP libraries and cloud-based NLP services. 【0497】 Based on the analysis results, the server uses artificial intelligence to generate appropriate career suggestions. These suggestions are personalized based on internal data and the user's past history. A generative AI model based on a transformer architecture is used to provide more accurate suggestions. 【0498】 Subsequently, the generated proposals are provided to the user via voice output through a robot. Text-to-speech technology is used for speech synthesis, ensuring that the proposals are conveyed in a way that is easily understandable to the user. 【0499】 Furthermore, user feedback and responses to suggestions are accumulated through memory systems and used to improve future suggestions. Database technology is used to securely and efficiently store and manage history and feedback information. 【0500】 For example, when a user asks a career question such as "I want to improve my project management skills," the robot can suggest suitable training programs or self-study resources. A possible prompt could be voice-input such as, "Hello, I'd like to learn about project management. Do you have any recommended learning methods?" 【0501】 In this way, a system is created that allows users to easily receive expert career advice in the comfort of their own homes. 【0502】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0503】 Step 1: 【0504】 The user asks the robot a question using voice. For example, they might say, "I want to improve my project management skills." This voice input is received through the robot's microphone. 【0505】 Step 2: 【0506】 The device converts the received audio into text data using the Google Cloud Speech-to-Text API. This API analyzes the audio waveform and transcribes it based on a language model. As a result, the entered question is sent to the server in text format. 【0507】 Step 3: 【0508】 The server uses natural language processing (NLP) to analyze received text data and understand the user's intent. Specifically, it uses morphological analysis to break down words and phrases in a sentence and performs semantic analysis. It receives the user's question text as input and obtains the analysis results. 【0509】 Step 4: 【0510】 The server uses a generative AI model to generate appropriate career suggestions based on the analysis results. It references internal databases and the user's past history information to create personalized advice. It generates suggestion text as output. 【0511】 Step 5: 【0512】 The generated suggestion text is sent from the server to the terminal. The terminal uses text-to-speech technology to convert this suggestion into speech. The advice is then delivered to the user via the terminal's speaker. 【0513】 Step 6: 【0514】 The user provides feedback on the advice given by voice input into the device. This is also converted into text data through speech recognition. The text data is sent to the server and stored in a memory device as feedback. 【0515】 Step 7: 【0516】 The server analyzes the accumulated feedback to improve future career suggestions. It updates database entries, saves them as user-specific historical information, and uses them to improve the accuracy of future suggestions. 【0517】 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. 【0518】 In implementing the present invention, a system configuration is used in which a server equipped with an information processing device and a terminal operated by a user are connected via a network. The server receives input transmitted by the user from the terminal and provides a service for career counseling. 【0519】 After receiving input from the user, the server first uses natural language processing to analyze the text and understand its content and the user's intent. Simultaneously, an emotion engine recognizes the user's emotions based on the input text. This emotion analysis takes into account the user's facial expressions, tone of voice, and word choice to determine the user's emotional state. 【0520】 The analyzed data is passed to artificial intelligence tools, which generate the most suitable career advice and suggestions for the user. The emotion engine also references the user's emotional data at this stage, adjusting the suggestions according to the user's emotional state. For example, for a user feeling anxious, the suggestions are optimized to provide a sense of reassurance. 【0521】 The generated suggestions are sent from the server to the terminal and displayed on the terminal. Users can review these suggestions on their terminal and use them to develop their careers. Users can also input feedback on the provided suggestions into their terminal and continue the consultation process. 【0522】 The emotional data from this feedback and suggestions is recorded in a memory system and used to generate and improve future suggestions. For example, if a user inputs "I'm anxious about the next career step," the server, based on the emotion engine's judgment, tags the emotion as "anxiety" and uses this information to generate supportive advice. For instance, a suggestion might be made to "provide specific advice along with past success stories." This system makes it possible to provide consistent career advice that is tailored to the user's emotions. 【0523】 The following describes the processing flow. 【0524】 Step 1: 【0525】 The user operates the device and logs into the career consultation system. The user enters the necessary authentication information and begins accessing the system. 【0526】 Step 2: 【0527】 The device sends login information to the server. The server uses this information to authenticate the user, and if successful, provides the device with an interface for career counseling. 【0528】 Step 3: 【0529】 Users input questions and inquiries about their carrier through the device's interface. 【0530】 Step 4: 【0531】 The terminal sends the entered data to the server. The data reaches the server while ensuring security. 【0532】 Step 5: 【0533】 The server passes the received input data to a natural language processing system. The natural language processing system analyzes the input and understands the user's intent. 【0534】 Step 6: 【0535】 Simultaneously, the server uses an emotion engine to analyze the user's emotional state from the input. The emotion engine infers emotions from the expression and context of the text. 【0536】 Step 7: 【0537】 The server's artificial intelligence generates career suggestions tailored to the user based on the results of natural language processing and sentiment analysis. Sentimental data is considered, and adjustments are made to reflect the user's emotions. 【0538】 Step 8: 【0539】 The server sends the generated suggestions to the terminal. The terminal then displays these suggestions in an easy-to-understand format for the user. 【0540】 Step 9: 【0541】 The user reviews the presented suggestions on their device and enters feedback or additional questions if necessary. 【0542】 Step 10: 【0543】 The device resends user feedback and questions to the server. The server records this information in its memory and uses it to inform future suggestions and responses. 【0544】 (Example 2) 【0545】 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." 【0546】 Conventional career counseling systems merely process user input text as information, making it difficult to provide appropriate suggestions tailored to the user's emotional state. Furthermore, they struggled to effectively utilize user feedback, hindering the provision of consistent and ongoing advice. This presented a challenge in providing personalized counseling support that met user needs. 【0547】 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. 【0548】 In this invention, the server includes a receiving device that receives user input, a language analysis device that analyzes the input to understand the user's intentions, and an emotion recognition device that recognizes the user's emotions based on the analyzed information. This enables the provision of personalized career suggestions that take into account the user's emotional state, and realizes high-quality consultation support. 【0549】 A "receiving device" is a component that has the function of receiving input data transmitted by the user and transferring it to a subsequent processing device. 【0550】 A "language analysis device" is a component that analyzes received input data and provides functions to understand the user's intent and meaning from the text. 【0551】 An "emotion recognition device" is a component that has the function of identifying the user's emotional state based on analyzed information and assigning an emotional tag accordingly. 【0552】 A "knowledge processing device" is a component that generates optimal suggestions based on emotion recognition results and other information, and adjusts the content as needed. 【0553】 A "presentation device" is a component that has the function of providing the generated proposal to the user visually or audibly and displaying it in a form that the user can confirm. 【0554】 A "recording device" is a component that has the function of accumulating user history information and emotional data, and storing this data so that it can be used to generate future suggestions. 【0555】 This system connects a server and a user-operated terminal via a communication network to provide a service that supports users' career consultations. The server first receives user input sent from the terminal. The input is mainly text-based and reflects the content of the user's consultation. 【0556】 The server uses natural language processing (NLP) libraries to analyze the received input. For example, it can utilize open-source tools such as SpaCy or Transformer models. This allows the server to extract the main intent and content from the user's input text. 【0557】 Next, the server utilizes an emotion recognition engine to identify the user's emotions based on the analyzed information. This involves evaluating the tone of voice and the wording of the text to classify the user's emotional state. This process typically involves using tone analyzer APIs or other emotion recognition tools. 【0558】 Based on the analysis and emotion recognition results, the server uses a generative AI model to design the most suitable career advice for the user. A typical generative AI model is a GPT-based model. The model uses prompt sentences as input and generates suggestions that are tailored to the user's experiences and emotions. For example, a possible prompt sentence might be, "I've been feeling anxious about my new job recently. Could you give me some advice on what I should do next?" 【0559】 Finally, the generated suggestions are sent from the server to the terminal. The terminal has an interface to display these suggestions in an easy-to-understand manner for the user. The user can refer to this and use it to make future career decisions. The user can also send feedback on the advice given to the server, which stores this in a recording device. This record is used for future suggestions, so the system continuously learns and improves. 【0560】 This invention is expected to allow users to receive personalized advice tailored to their individual emotional state, thereby supporting them in building a better career. 【0561】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0562】 Step 1: 【0563】 The user enters text into the device. For example, they might enter a message such as, "I'm worried about my next career step." The device then sends this text to the server over the network. The input includes the user's text data. The output is the data transferred from the device to the server. 【0564】 Step 2: 【0565】 The server analyzes the input text received from the terminal using a language analysis device. Specifically, it uses natural language processing libraries such as SpaCy to extract the intent of the text. It identifies key information and intent from the text data received as input and outputs the analysis results as internal data. 【0566】 Step 3: 【0567】 The server identifies emotions using an emotion recognition device based on the analyzed data. In this step, the tone analyzer API is used to evaluate what emotions the user's input represents. As a result, an emotional state such as "anxiety" or "relief" is output. 【0568】 Step 4: 【0569】 The server generates suggestions via a knowledge processing unit based on emotional states and analysis results. This process utilizes a generative AI model, such as a GPT-based model. The input is data tagged with user intentions and emotions, and the output is user-optimized career advice text. 【0570】 Step 5: 【0571】 The server adjusts the generated suggestions according to the user's emotional state. For example, for an anxious user, the suggestions are modified to provide reassurance. The server then references emotional data to optimize the suggestions. The adjusted suggestion text is then output. 【0572】 Step 6: 【0573】 The server sends the final proposal to the terminal. The terminal receives this proposal and displays it in an easy-to-understand format for the user. The input is the revised proposal text, and the output is the information displayed on the user's screen. 【0574】 Step 7: 【0575】 The user can review the displayed advice and enter feedback into the terminal. The terminal sends this feedback to the server. The input is the user's feedback data, and the output is the transmission of the feedback to the server. 【0576】 Step 8: 【0577】 The server stores the received feedback in a recording device. This data will be used to improve future suggestions. The input is the feedback data, and the output is the data stored in the recording device. 【0578】 (Application Example 2) 【0579】 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." 【0580】 In modern society, there are few systems that allow users to receive accurate career counseling while simultaneously experiencing their emotions. In particular, there is a lack of individualized support that takes into account emotional states, which is a challenge in providing appropriate support that meets the needs of users. 【0581】 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. 【0582】 In this invention, the server includes communication means for receiving user input, natural language processing means for analyzing the input and understanding the user's intent, and emotion analysis means for recognizing the emotional state based on the input. This makes it possible to provide appropriate career advice individually based on the user's intent and emotions. 【0583】 "Communication means for receiving user input" refers to a device or method for transmitting data or information from a user to a server via a network. 【0584】 "Natural language processing means" refers to technologies that analyze text data received from users and understand its meaning and intent. 【0585】 "Emotional analysis methods" refer to technologies that recognize and determine an emotional state from user input data, accompanying audio, facial expressions, and other information. 【0586】 "Artificial intelligence tools" refer to algorithms and systems that generate appropriate suggestions and advice based on analyzed data and emotional information. 【0587】 "Display means" refers to a display or interface used to output generated proposals and information in a format that the user can understand. 【0588】 "Memory tools" refer to storage technologies that store suggestions, user feedback, and emotional data for future reference and analysis. 【0589】 An "internal database" is a collection of information used within a system, and it is a source of information that is referenced to enhance suggestions for users. 【0590】 To implement this invention, a system configuration is required in which a server and a user's terminal are connected via a network. The server acquires data using communication means to receive input from the user and analyzes the text using natural language processing means. This analysis makes it possible to accurately understand the user's intent. 【0591】 Next, the server uses emotion analysis tools to detect the user's emotional state. This emotion analysis uses information such as expressions and tone of voice contained in the user's input. For example, by analyzing the user's tone of voice through speech recognition and adding emotional information from voice and facial expressions to text analysis, the quality of suggestions can be improved. 【0592】 The analysis results are passed to artificial intelligence (AI) tools, which then generate optimized career advice and suggestions. The AI ​​consults its internal database and, based on the analysis results and sentiment data, derives the most beneficial information for the user. 【0593】 The generated suggestions are sent to the user's terminal via a display device. The user can review the information presented on their terminal and use it to develop their career. At this stage, feedback from the user is sent to the server and stored in a memory device. This feedback will be used to improve future suggestions. 【0594】 The software components to be used include Google Cloud's NLP service for natural language processing and Microsoft Azure's sentiment recognition API for sentiment analysis. Additionally, OpenAI's AI model will be used for suggestion generation using artificial intelligence. 【0595】 For example, when a user inputs "I'm anxious about my next career step," the emotion analysis recognizes this anxiety. In response, the AI ​​model can generate suggestions that "provide specific advice along with past success stories." 【0596】 Examples of prompt statements are as follows: 【0597】 "Generate encouraging words and specific advice for a user who is feeling anxious about their first day at a new workplace, referencing their past successes." 【0598】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0599】 Step 1: 【0600】 The server receives input from the user. This input is received as text or audio data. The server receives this data using a communication protocol and temporarily stores it for the next processing step. 【0601】 Step 2: 【0602】 The server passes the received input data to a natural language processing system. Here, an NLP engine (e.g., Google Cloud NLP service) is used to perform grammatical and semantic analysis on the text data, and output data to understand the user's intent. At this stage, the input text is converted into a structured data format. 【0603】 Step 3: 【0604】 The server passes the text analysis results to the sentiment analysis tool. The sentiment analysis tool generates sentiment data based on the characteristics of the input text and pronunciation. Specifically, it uses the Microsoft Azure sentiment recognition API to output labels such as "anxiety" and "joy" through a sentiment evaluation model. These labels indicate the user's emotional state. 【0605】 Step 4: 【0606】 The server sends text analysis results and sentiment data to an artificial intelligence (AI) model. The AI ​​model (e.g., OpenAI's AI model) uses this data to generate the most suitable suggestions for the user. The AI ​​refers to an internal database and outputs advice tailored to the user's situation. 【0607】 Step 5: 【0608】 The generated suggestions are sent from the server to the user's terminal. The terminal displays the received suggestions on its screen. The user reviews them and uses them to develop their career path as needed. 【0609】 Step 6: 【0610】 Users send feedback on their suggestions to the server via their device. The feedback is sent as text data, which the server stores in a memory device. The stored data is used as reference data for future suggestions. 【0611】 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. 【0612】 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. 【0613】 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. 【0614】 [Fourth Embodiment] 【0615】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0616】 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. 【0617】 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). 【0618】 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. 【0619】 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. 【0620】 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). 【0621】 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. 【0622】 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. 【0623】 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. 【0624】 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. 【0625】 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. 【0626】 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. 【0627】 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". 【0628】 In implementing this invention, first, a server equipped with an information processing device is prepared, and an AI engine and a natural language processing module are operated on it. This server communicates with a terminal operated by a user via a network to provide a career consultation service. The terminal is equipped with an interface that allows the user to input questions about their career, and the input content is sent to the server. 【0629】 The server analyzes the received input using natural language processing to understand the user's intent. This analysis includes morphological analysis and contextual recognition to identify the career information and advice the user is seeking. Based on the analysis results, the server's artificial intelligence generates suggestions tailored to the user. These suggestions refer to internal resource data and are specifically generated in the form of, for example, training information for leadership development or project opportunities related to skill enhancement. 【0630】 The generated suggestions are sent from the server to the user's terminal, which displays them on the screen. This display allows the user to review the suggestions and use them to help develop their career. Users can also provide feedback on the suggestions, and the terminal sends this feedback back to the server. This feedback is stored on the server by a memory device and used to improve the accuracy of future suggestions and manage the user's history. 【0631】 For example, if a user enters "I want to improve my project management skills," the server can suggest relevant in-house training programs and opportunities to participate in project teams. The suggestions are further customized and personalized based on the user's past history and feedback. In this way, the system of the present invention can effectively support the career development of its users. 【0632】 The following describes the processing flow. 【0633】 Step 1: 【0634】 The user logs into the career consultation system using their device. The user enters their authentication information and prepares to access the system. 【0635】 Step 2: 【0636】 The device sends login information to the server, which then authenticates the user based on that information. If authentication is successful, the server provides the device with a user interface for career counseling. 【0637】 Step 3: 【0638】 Users enter questions and inquiries about their carrier through the interface provided on their device. 【0639】 Step 4: 【0640】 The terminal sends the user's input to the server. The input data is encrypted and reaches the server while maintaining security. 【0641】 Step 5: 【0642】 The server receives the input data and passes it to the natural language processing module. The module analyzes the input data to understand the user's intent and needs. 【0643】 Step 6: 【0644】 The AI ​​system on the server generates optimal suggestions based on the analyzed intent. The AI ​​refers to the company's internal resource database and selects information that meets the user's request. 【0645】 Step 7: 【0646】 The server sends the generated proposal to the terminal. The proposal content is converted into a user-friendly format and prepared. 【0647】 Step 8: 【0648】 The device receives the suggestions and displays them on the user interface. Users can then use the provided information to consider their own career strategy. 【0649】 Step 9: 【0650】 Users can provide feedback on the proposal. They can also add questions about anything unclear or provide evaluation comments on the proposal. 【0651】 Step 10: 【0652】 The device sends feedback to the server. The feedback information is stored using a memory device to improve the accuracy of future suggestions. 【0653】 (Example 1) 【0654】 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". 【0655】 Conventional career counseling service systems have the problem of not being able to adequately adapt to the diverse needs of users. Furthermore, the suggestions offered are general, making it difficult to provide specific support tailored to individual circumstances. In addition, there is a lack of mechanisms for continuously improving service quality by not adequately utilizing user feedback. 【0656】 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. 【0657】 In this invention, the server includes an information exchange means for receiving user questions, a natural language processing device for analyzing the questions and understanding the user's intent, and a machine learning device for generating suggestions based on the analysis. This makes it possible to provide each user with specific career suggestions tailored to their individual circumstances. Furthermore, it allows for continuous improvement of service quality by utilizing user feedback. 【0658】 An "information exchange tool" is a communication interface for receiving user questions and information. 【0659】 A "natural language processing system" is a data analysis system that analyzes received questions and understands the user's intent. 【0660】 A "machine learning device" is a computer system that generates suggestions for users based on analysis results obtained through natural language processing. 【0661】 An "information display device" is an output interface for displaying the generated proposals on the user's terminal. 【0662】 An "information storage device" is a data storage device used to collect and store user interactions and feedback. 【0663】 In carrying out the present invention, a server equipped with an information processing device is used. This server communicates with a terminal operated by a user via a network to provide a career consultation service. First, the server receives the user's questions. For this purpose, an information exchange means connected to the server is used. All input transmitted from the user's terminal is delivered to the server through this communication interface. 【0664】 Next, the server uses a natural language processing unit (NLP) to analyze the received question. This analysis employs NLP techniques such as morphological analysis and contextual recognition, allowing the user's intent to be identified. Furthermore, a machine learning system generates suggestions based on the analysis results. This machine learning system refers to an internal database to provide career development suggestions tailored to the user. For example, it extracts training information for leadership development and project participation opportunities from internal resource data. 【0665】 The generated suggestions are transmitted to the user's terminal via an information display device. The terminal displays the received information on the user's screen. This process allows the user to review the suggestions and use the individually customized information to help them in their career development. Users can also provide feedback on the suggestions. This feedback is stored on a server by an information storage device and used later to improve the accuracy of the suggestions. 【0666】 As a concrete example, consider a scenario where a user inputs "I want to improve my project management skills." The server can then suggest relevant internal training programs and opportunities to participate in project teams. Furthermore, these suggestions are further customized based on the user's past history and feedback. Below is an example of a prompt to input into the generating AI model: "The user is seeking to improve their project management skills. Based on this user's past history and feedback, please generate specific suggestions including relevant training programs and project participation opportunities." 【0667】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0668】 Step 1: 【0669】 The user enters career-related questions into the device's interface. Specifically, the user enters something like "I want to improve my project management skills" into a text box. The user's input is sent from the device to the server when the submit button is pressed. The input data is natural language text information. 【0670】 Step 2: 【0671】 The server runs a natural language processing unit to analyze the received input data. Specifically, it performs morphological analysis to divide the text information into words. Next, it performs contextual recognition to analyze the user's intent. The output obtained in this process is structured data about the user's intent and desires. This data is used in the next suggestion generation step. 【0672】 Step 3: 【0673】 The server's machine learning system generates suggestions based on the structured data obtained in the previous step. First, the server queries its internal database to find relevant resources. For example, it identifies internal training programs or project participation opportunities related to improving project management skills. This data is then processed to generate specific suggestions tailored to the user. The output is a data structure containing the suggested content. 【0674】 Step 4: 【0675】 The generated suggestions are sent from the server to the terminal. The server encodes the suggestion data in an appropriate format, such as JSON, and sends it to the terminal as an HTTP response. This ensures that the suggestions reach the user's terminal. 【0676】 Step 5: 【0677】 The device visually displays the received suggestion data to the user. Specifically, it reflects the suggestion content in UI components on the screen. The user can review the suggestions on the screen and use them to help develop their career. 【0678】 Step 6: 【0679】 Users provide feedback on the proposal. This feedback is entered into a feedback form on the device and later sent to the server. The data entered includes the user's evaluation of the proposal and any additional requests. 【0680】 Step 7: 【0681】 The server receives feedback and stores it within the server using an information storage device. This feedback is stored in a database and used when generating suggestions in the future. This improves the accuracy of suggestions and enables the provision of more optimized services to users. 【0682】 (Application Example 1) 【0683】 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". 【0684】 In today's family environment, opportunities for career development counseling are limited, and there is a lack of convenient career support tools, especially for individuals aiming to improve their skills while working. This creates a challenge in that opportunities to receive optimal suggestions for individual career goals are restricted. 【0685】 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. 【0686】 In this invention, the server includes speech recognition means for receiving voice input from a user, natural language processing means for analyzing the input and understanding the user's intent, and artificial intelligence means for generating career suggestions based on the analysis. This makes it possible to facilitate career counseling within the home and provide users with optimal career development advice. 【0687】 A "speech recognition means" is a means for receiving speech input from a user and converting it into text data. 【0688】 "Natural language processing means" are methods for analyzing received text data and understanding the user's intent. 【0689】 "Artificial intelligence means" refers to means for generating suggestions suitable for the user based on analysis. 【0690】 "Voice output means" refers to a means for converting the generated proposal into voice and providing it to the user. 【0691】 A "memory device" is a means of saving a user's past history and feedback to help improve the accuracy of future suggestions. 【0692】 "Internal data" refers to the databases and resources that the system references when generating suggestions. 【0693】 A "robot" is an automated device installed in a home that can provide career counseling. 【0694】 To implement this invention, a robot installed in the home is required. This robot is equipped with speech recognition means to accurately recognize the user's voice input. Specifically, it converts the user's voice into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. Furthermore, natural language processing (NLP) technology is required as a means of natural language processing, which the system uses to analyze and understand the user's questions and inquiries. It is conceivable to use a combination of open-source NLP libraries and cloud-based NLP services. 【0695】 Based on the analysis results, the server uses artificial intelligence to generate appropriate career suggestions. These suggestions are personalized based on internal data and the user's past history. A generative AI model based on a transformer architecture is used to provide more accurate suggestions. 【0696】 Subsequently, the generated proposals are provided to the user via voice output through a robot. Text-to-speech technology is used for speech synthesis, ensuring that the proposals are conveyed in a way that is easily understandable to the user. 【0697】 Furthermore, user feedback and responses to suggestions are accumulated through memory systems and used to improve future suggestions. Database technology is used to securely and efficiently store and manage history and feedback information. 【0698】 For example, when a user asks a career question such as "I want to improve my project management skills," the robot can suggest suitable training programs or self-study resources. A possible prompt could be voice-input such as, "Hello, I'd like to learn about project management. Do you have any recommended learning methods?" 【0699】 In this way, a system is created that allows users to easily receive expert career advice in the comfort of their own homes. 【0700】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0701】 Step 1: 【0702】 The user asks the robot a question using voice. For example, they might say, "I want to improve my project management skills." This voice input is received through the robot's microphone. 【0703】 Step 2: 【0704】 The device converts the received audio into text data using the Google Cloud Speech-to-Text API. This API analyzes the audio waveform and transcribes it based on a language model. As a result, the entered question is sent to the server in text format. 【0705】 Step 3: 【0706】 The server uses natural language processing (NLP) to analyze received text data and understand the user's intent. Specifically, it uses morphological analysis to break down words and phrases in a sentence and performs semantic analysis. It receives the user's question text as input and obtains the analysis results. 【0707】 Step 4: 【0708】 The server uses a generative AI model to generate appropriate career suggestions based on the analysis results. It references internal databases and the user's past history information to create personalized advice. It generates suggestion text as output. 【0709】 Step 5: 【0710】 The generated suggestion text is sent from the server to the terminal. The terminal uses text-to-speech technology to convert this suggestion into speech. The advice is then delivered to the user via the terminal's speaker. 【0711】 Step 6: 【0712】 The user provides feedback on the advice given by voice input into the device. This is also converted into text data through speech recognition. The text data is sent to the server and stored in a memory device as feedback. 【0713】 Step 7: 【0714】 The server analyzes the accumulated feedback to improve future career suggestions. It updates database entries, saves them as user-specific historical information, and uses them to improve the accuracy of future suggestions. 【0715】 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. 【0716】 In implementing the present invention, a system configuration is used in which a server equipped with an information processing device and a terminal operated by a user are connected via a network. The server receives input transmitted by the user from the terminal and provides a service for career counseling. 【0717】 After receiving input from the user, the server first uses natural language processing to analyze the text and understand its content and the user's intent. Simultaneously, an emotion engine recognizes the user's emotions based on the input text. This emotion analysis takes into account the user's facial expressions, tone of voice, and word choice to determine the user's emotional state. 【0718】 The analyzed data is passed to artificial intelligence tools, which generate the most suitable career advice and suggestions for the user. The emotion engine also references the user's emotional data at this stage, adjusting the suggestions according to the user's emotional state. For example, for a user feeling anxious, the suggestions are optimized to provide a sense of reassurance. 【0719】 The generated suggestions are sent from the server to the terminal and displayed on the terminal. Users can review these suggestions on their terminal and use them to develop their careers. Users can also input feedback on the provided suggestions into their terminal and continue the consultation process. 【0720】 The emotional data from this feedback and suggestions is recorded in a memory system and used to generate and improve future suggestions. For example, if a user inputs "I'm anxious about the next career step," the server, based on the emotion engine's judgment, tags the emotion as "anxiety" and uses this information to generate supportive advice. For instance, a suggestion might be made to "provide specific advice along with past success stories." This system makes it possible to provide consistent career advice that is tailored to the user's emotions. 【0721】 The following describes the processing flow. 【0722】 Step 1: 【0723】 The user operates the device and logs into the career consultation system. The user enters the necessary authentication information and begins accessing the system. 【0724】 Step 2: 【0725】 The device sends login information to the server. The server uses this information to authenticate the user, and if successful, provides the device with an interface for career counseling. 【0726】 Step 3: 【0727】 Users input questions and inquiries about their carrier through the device's interface. 【0728】 Step 4: 【0729】 The terminal sends the entered data to the server. The data reaches the server while ensuring security. 【0730】 Step 5: 【0731】 The server passes the received input data to a natural language processing system. The natural language processing system analyzes the input and understands the user's intent. 【0732】 Step 6: 【0733】 Simultaneously, the server uses an emotion engine to analyze the user's emotional state from the input. The emotion engine infers emotions from the expression and context of the text. 【0734】 Step 7: 【0735】 The server's artificial intelligence generates career suggestions tailored to the user based on the results of natural language processing and sentiment analysis. Sentimental data is considered, and adjustments are made to reflect the user's emotions. 【0736】 Step 8: 【0737】 The server sends the generated suggestions to the terminal. The terminal then displays these suggestions in an easy-to-understand format for the user. 【0738】 Step 9: 【0739】 The user reviews the presented suggestions on their device and enters feedback or additional questions if necessary. 【0740】 Step 10: 【0741】 The device resends user feedback and questions to the server. The server records this information in its memory and uses it to inform future suggestions and responses. 【0742】 (Example 2) 【0743】 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". 【0744】 Conventional career counseling systems merely process user input text as information, making it difficult to provide appropriate suggestions tailored to the user's emotional state. Furthermore, they struggled to effectively utilize user feedback, hindering the provision of consistent and ongoing advice. This presented a challenge in providing personalized counseling support that met user needs. 【0745】 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. 【0746】 In this invention, the server includes a receiving device that receives user input, a language analysis device that analyzes the input to understand the user's intentions, and an emotion recognition device that recognizes the user's emotions based on the analyzed information. This enables the provision of personalized career suggestions that take into account the user's emotional state, and realizes high-quality consultation support. 【0747】 A "receiving device" is a component that has the function of receiving input data transmitted by the user and transferring it to a subsequent processing device. 【0748】 A "language analysis device" is a component that analyzes received input data and provides functions to understand the user's intent and meaning from the text. 【0749】 An "emotion recognition device" is a component that has the function of identifying the user's emotional state based on analyzed information and assigning an emotional tag accordingly. 【0750】 A "knowledge processing device" is a component that generates optimal suggestions based on emotion recognition results and other information, and adjusts the content as needed. 【0751】 A "presentation device" is a component that has the function of providing the generated proposal to the user visually or audibly and displaying it in a form that the user can confirm. 【0752】 A "recording device" is a component that has the function of accumulating user history information and emotional data, and storing this data so that it can be used to generate future suggestions. 【0753】 This system connects a server and a user-operated terminal via a communication network to provide a service that supports users' career consultations. The server first receives user input sent from the terminal. The input is mainly text-based and reflects the content of the user's consultation. 【0754】 The server uses natural language processing (NLP) libraries to analyze the received input. For example, it can utilize open-source tools such as SpaCy or Transformer models. This allows the server to extract the main intent and content from the user's input text. 【0755】 Next, the server utilizes an emotion recognition engine to identify the user's emotions based on the analyzed information. This involves evaluating the tone of voice and the wording of the text to classify the user's emotional state. This process typically involves using tone analyzer APIs or other emotion recognition tools. 【0756】 Based on the analysis and emotion recognition results, the server uses a generative AI model to design the most suitable career advice for the user. A typical generative AI model is a GPT-based model. The model uses prompt sentences as input and generates suggestions that are tailored to the user's experiences and emotions. For example, a possible prompt sentence might be, "I've been feeling anxious about my new job recently. Could you give me some advice on what I should do next?" 【0757】 Finally, the generated suggestions are sent from the server to the terminal. The terminal has an interface to display these suggestions in an easy-to-understand manner for the user. The user can refer to this and use it to make future career decisions. The user can also send feedback on the advice given to the server, which stores this in a recording device. This record is used for future suggestions, so the system continuously learns and improves. 【0758】 This invention is expected to allow users to receive personalized advice tailored to their individual emotional state, thereby supporting them in building a better career. 【0759】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0760】 Step 1: 【0761】 The user enters text into the device. For example, they might enter a message such as, "I'm worried about my next career step." The device then sends this text to the server over the network. The input includes the user's text data. The output is the data transferred from the device to the server. 【0762】 Step 2: 【0763】 The server analyzes the input text received from the terminal using a language analysis device. Specifically, it uses natural language processing libraries such as SpaCy to extract the intent of the text. It identifies key information and intent from the text data received as input and outputs the analysis results as internal data. 【0764】 Step 3: 【0765】 The server identifies emotions using an emotion recognition device based on the analyzed data. In this step, the tone analyzer API is used to evaluate what emotions the user's input represents. As a result, an emotional state such as "anxiety" or "relief" is output. 【0766】 Step 4: 【0767】 The server generates suggestions via a knowledge processing unit based on emotional states and analysis results. This process utilizes a generative AI model, such as a GPT-based model. The input is data tagged with user intentions and emotions, and the output is user-optimized career advice text. 【0768】 Step 5: 【0769】 The server adjusts the generated suggestions according to the user's emotional state. For example, for an anxious user, the suggestions are modified to provide reassurance. The server then references emotional data to optimize the suggestions. The adjusted suggestion text is then output. 【0770】 Step 6: 【0771】 The server sends the final proposal to the terminal. The terminal receives this proposal and displays it in an easy-to-understand format for the user. The input is the revised proposal text, and the output is the information displayed on the user's screen. 【0772】 Step 7: 【0773】 The user can review the displayed advice and enter feedback into the terminal. The terminal sends this feedback to the server. The input is the user's feedback data, and the output is the transmission of the feedback to the server. 【0774】 Step 8: 【0775】 The server stores the received feedback in a recording device. This data will be used to improve future suggestions. The input is the feedback data, and the output is the data stored in the recording device. 【0776】 (Application Example 2) 【0777】 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". 【0778】 In modern society, there are few systems that allow users to receive accurate career counseling while simultaneously experiencing their emotions. In particular, there is a lack of individualized support that takes into account emotional states, which is a challenge in providing appropriate support that meets the needs of users. 【0779】 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. 【0780】 In this invention, the server includes communication means for receiving user input, natural language processing means for analyzing the input and understanding the user's intent, and emotion analysis means for recognizing the emotional state based on the input. This makes it possible to provide appropriate career advice individually based on the user's intent and emotions. 【0781】 "Communication means for receiving user input" refers to a device or method for transmitting data or information from a user to a server via a network. 【0782】 "Natural language processing means" refers to technologies that analyze text data received from users and understand its meaning and intent. 【0783】 "Emotional analysis methods" refer to technologies that recognize and determine an emotional state from user input data, accompanying audio, facial expressions, and other information. 【0784】 "Artificial intelligence tools" refer to algorithms and systems that generate appropriate suggestions and advice based on analyzed data and emotional information. 【0785】 "Display means" refers to a display or interface used to output generated proposals and information in a format that the user can understand. 【0786】 "Memory tools" refer to storage technologies that store suggestions, user feedback, and emotional data for future reference and analysis. 【0787】 An "internal database" is a collection of information used within a system, and it is a source of information that is referenced to enhance suggestions for users. 【0788】 To implement this invention, a system configuration is required in which a server and a user's terminal are connected via a network. The server acquires data using communication means to receive input from the user and analyzes the text using natural language processing means. This analysis makes it possible to accurately understand the user's intent. 【0789】 Next, the server uses emotion analysis tools to detect the user's emotional state. This emotion analysis uses information such as expressions and tone of voice contained in the user's input. For example, by analyzing the user's tone of voice through speech recognition and adding emotional information from voice and facial expressions to text analysis, the quality of suggestions can be improved. 【0790】 The analysis results are passed to artificial intelligence (AI) tools, which then generate optimized career advice and suggestions. The AI ​​consults its internal database and, based on the analysis results and sentiment data, derives the most beneficial information for the user. 【0791】 The generated suggestions are sent to the user's terminal via a display device. The user can review the information presented on their terminal and use it to develop their career. At this stage, feedback from the user is sent to the server and stored in a memory device. This feedback will be used to improve future suggestions. 【0792】 The software components to be used include Google Cloud's NLP service for natural language processing and Microsoft Azure's sentiment recognition API for sentiment analysis. Additionally, OpenAI's AI model will be used for suggestion generation using artificial intelligence. 【0793】 For example, when a user inputs "I'm anxious about my next career step," the emotion analysis recognizes this anxiety. In response, the AI ​​model can generate suggestions that "provide specific advice along with past success stories." 【0794】 Examples of prompt statements are as follows: 【0795】 "Generate encouraging words and specific advice for a user who is feeling anxious about their first day at a new workplace, referencing their past successes." 【0796】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0797】 Step 1: 【0798】 The server receives input from the user. This input is received as text or audio data. The server receives this data using a communication protocol and temporarily stores it for the next processing step. 【0799】 Step 2: 【0800】 The server passes the received input data to a natural language processing system. Here, an NLP engine (e.g., Google Cloud NLP service) is used to perform grammatical and semantic analysis on the text data, and output data to understand the user's intent. At this stage, the input text is converted into a structured data format. 【0801】 Step 3: 【0802】 The server passes the text analysis results to the sentiment analysis tool. The sentiment analysis tool generates sentiment data based on the characteristics of the input text and pronunciation. Specifically, it uses the Microsoft Azure sentiment recognition API to output labels such as "anxiety" and "joy" through a sentiment evaluation model. These labels indicate the user's emotional state. 【0803】 Step 4: 【0804】 The server sends text analysis results and sentiment data to an artificial intelligence (AI) model. The AI ​​model (e.g., OpenAI's AI model) uses this data to generate the most suitable suggestions for the user. The AI ​​refers to an internal database and outputs advice tailored to the user's situation. 【0805】 Step 5: 【0806】 The generated suggestions are sent from the server to the user's terminal. The terminal displays the received suggestions on its screen. The user reviews them and uses them to develop their career path as needed. 【0807】 Step 6: 【0808】 Users send feedback on their suggestions to the server via their device. The feedback is sent as text data, which the server stores in a memory device. The stored data is used as reference data for future suggestions. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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." 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 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. 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 The following is further disclosed regarding the embodiments described above. 【0831】 (Claim 1) 【0832】 A communication means for receiving user input, 【0833】 A natural language processing means that analyzes the aforementioned input to understand the user's intent, 【0834】 An artificial intelligence means for generating proposals based on the aforementioned analysis, 【0835】 A display means for providing the aforementioned proposal to the user, 【0836】 A storage means for storing the user history, 【0837】 A system that includes this. 【0838】 (Claim 2) 【0839】 The system according to claim 1, characterized in that the artificial intelligence means generates additional information by referring to internal resource data. 【0840】 (Claim 3) 【0841】 The system according to claim 1, characterized in that the storage means stores user feedback and uses it when making the next suggestion. 【0842】 "Example 1" 【0843】 (Claim 1) 【0844】 A means of information exchange for receiving user questions, 【0845】 A natural language processing device that analyzes the aforementioned questions to analyze the user's intent, 【0846】 A machine learning device that generates proposals based on the aforementioned analysis, 【0847】 An information display device that provides the above proposal to the user's terminal, 【0848】 An information storage device that stores the user's interactions, 【0849】 A system that includes this. 【0850】 (Claim 2) 【0851】 The system according to claim 1, characterized in that the machine learning device generates additional suggestion information by referring to an internal database. 【0852】 (Claim 3) 【0853】 The system according to claim 1, characterized in that the information storage device collects user feedback and utilizes that feedback when making future proposals. 【0854】 "Application Example 1" 【0855】 (Claim 1) 【0856】 A voice recognition means for receiving voice input from the user, 【0857】 A natural language processing means that analyzes the aforementioned input to understand the user's intent, 【0858】 An artificial intelligence means for generating career proposals based on the aforementioned analysis, 【0859】 A voice output means that provides the aforementioned proposal to the user in voice, 【0860】 A storage means for storing the user history, 【0861】 A system that includes this. 【0862】 (Claim 2) 【0863】 The system according to claim 1, characterized in that it is installed on a robot in the home, and the artificial intelligence means generates additional information by referring to internal data. 【0864】 (Claim 3) 【0865】 The system according to claim 1, characterized in that the storage means accumulates user feedback and uses it when making the next suggestion to improve the accuracy of the suggestion. 【0866】 "Example 2 of combining an emotion engine" 【0867】 (Claim 1) 【0868】 A receiving device that receives user input, 【0869】 A language analysis device that analyzes the aforementioned input to understand the user's intent, 【0870】 An emotion recognition device that recognizes the user's emotions based on the analyzed information, 【0871】 A knowledge processing device that generates and adjusts proposals based on the aforementioned emotions, 【0872】 A presentation device that provides the aforementioned proposal to the user, 【0873】 A recording device for storing the user's history and emotional data, 【0874】 An information processing system that includes this. 【0875】 (Claim 2) 【0876】 The information processing system according to claim 1, characterized in that the knowledge processing device generates additional information by referring to internal information data and adjusts the proposed content to reflect the emotion recognition result. 【0877】 (Claim 3) 【0878】 The information processing system according to claim 1, characterized in that the recording device accumulates user feedback and uses it in the next operation to improve the proposal. 【0879】 "Application example 2 when combining with an emotional engine" 【0880】 (Claim 1) 【0881】 A communication means for receiving user input, 【0882】 A natural language processing means that analyzes the aforementioned input to understand the user's intent, 【0883】 An emotion analysis means for recognizing an emotional state based on the aforementioned input, 【0884】 An artificial intelligence means for generating proposals based on the aforementioned analysis and emotional state, 【0885】 A display means for providing the aforementioned proposal to the user, 【0886】 The above proposal and emotional data are stored in a memory device, 【0887】 A system that includes this. 【0888】 (Claim 2) 【0889】 The system according to claim 1, characterized in that the artificial intelligence means generates additional information by referring to an internal database. 【0890】 (Claim 3) 【0891】 The system according to claim 1, characterized in that the storage means stores user feedback and is used to refer to past success stories when making the next proposal. [Explanation of Symbols] 【0892】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A communication means for receiving user input, A natural language processing means that analyzes the aforementioned input to understand the user's intent, An artificial intelligence means for generating proposals based on the aforementioned analysis, A display means for providing the aforementioned proposal to the user, A storage means for storing the user history, A system that includes this. [Claim 2] The system according to claim 1, characterized in that the artificial intelligence means generates additional information by referring to internal resource data. [Claim 3] The system according to claim 1, characterized in that the storage means stores user feedback and uses it when making the next suggestion.

Images