system

Abstract

We provide the system. [Solution] Means for collecting information and issues related to business from users, A method for automatically defining requirements by analyzing collected information, A means of selecting the optimal artificial intelligence model based on the requirements definition, A means of constructing an artificial intelligence agent based on a selected artificial intelligence model, A means of integrating the constructed artificial intelligence agent into a company's business processes, A means to monitor the performance of an artificial intelligence agent and automatically make improvements as needed, 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 The present invention aims to efficiently and effectively solve multiple problems faced by enterprises and organizations when introducing artificial intelligence technology, such as selecting an appropriate artificial intelligence model, integrating it into an efficient business process, and monitoring and improving performance after introduction. In particular, for enterprises lacking expertise in artificial intelligence technology, these processes become more complex, and it is difficult to make an introduction suitable for the purpose. 【Means for Solving the Problems】 【0005】 According to this invention, specific needs of companies and organizations are extracted by means of collecting business-related information and challenges from users. Furthermore, the conditions necessary for designing an artificial intelligence agent are clarified by using means of automatically defining requirements based on that information. Subsequently, means are provided for selecting the optimal artificial intelligence model that meets the requirements, and means are provided for automatically constructing an artificial intelligence agent based on the selected model, enabling users to introduce an appropriate agent even without specialized knowledge. In addition, means are provided for integrating the constructed artificial intelligence agent into business processes, enabling rapid and efficient operation, and means are provided for monitoring the performance of the agent during operation and automatically making improvements, enabling the maintenance of optimal performance at all times. 【0006】 A "user" is the entity that provides information to a system and receives the results; typically, this is a representative of a company or organization. 【0007】 "Business-related information" refers to data and content that a company or organization generates or maintains through its daily activities. 【0008】 "Challenges" refer to problems or situations that a company or organization is currently facing that require improvement. 【0009】 "Means of collection" refers to the methods, processes, or technical mechanisms for obtaining information from users. 【0010】 "Requirements definition" is the act of clarifying the specifications and conditions of a system or process in order to address a problem that needs to be solved. 【0011】 An "artificial intelligence model" is a computational model that combines algorithms and data processing techniques used to solve a specific problem. 【0012】 "Means of selection" refers to the methods and processes for selecting the one that best fits the required specifications and conditions. 【0013】 An "artificial intelligence agent" is a system or program that uses artificial intelligence technology to autonomously perform specific roles or functions. 【0014】 "Means of construction" refers to the methods or processes used to actually build a system or program based on the designed specifications. 【0015】 "Means of integration" refer to methods and technologies for integrating new systems or functions into existing business processes. 【0016】 "Monitoring" is the activity of continuously observing and analyzing the operation and performance of a system or process. 【0017】 "Means of improvement" refer to methods of modification and optimization carried out to improve the performance of the current system or process. [Brief explanation of the drawing] 【0018】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0019】 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. 【0020】 First, the language used in the following description will be explained. [[ID=三十二]] 【0021】 [[ID=三十三]] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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. 【0022】 In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor. 【0023】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0024】 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). 【0025】 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." 【0026】 [First Embodiment] 【0027】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0028】 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. 【0029】 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). 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0035】 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. 【0036】 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. 【0037】 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. 【0038】 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". 【0039】 The system of the present invention comprises a user, a server, and a terminal. The user logs into the system and provides work-related information and tasks to the server via the terminal. The server receives this information and stores it in a database. 【0040】 Next, the server analyzes the received information and automatically defines the requirements necessary for business improvement. In this process, natural language processing technology is used to extract important keywords from the user's input, and the requirements are identified based on these keywords. Based on the defined requirements, the server selects the optimal model from the artificial intelligence model library in the database. 【0041】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This agent has the capability to perform various functions to streamline the user's business processes. 【0042】 The constructed agent is deployed to the user via a terminal. The server assists with the initial setup of the agent and its integration into business processes, ensuring a smooth deployment. After deployment, the server monitors the agent's execution status in real time and analyzes its performance data. 【0043】 If the agent's performance falls below expectations, the server uses an automatic adjustment function to improve the agent's performance. This continuous monitoring and improvement ensures that users can always enjoy efficient and optimal work performance. 【0044】 For example, if a retail user wants to improve the efficiency of their inventory management, they might input issues such as "slow real-time processing of sales data." The server receives this information and builds an artificial intelligence agent to forecast inventory and make optimal orders. Once the agent is running, the user can understand the inventory situation in real time and know the appropriate timing for ordering. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 Users log in to the system using their terminals and input specific details about their company's business operations and current challenges. The terminals then send this information to the server. 【0048】 Step 2: 【0049】 The server stores business information and issues received from users in a database. During this process, the server uses natural language processing technology to extract important keywords and prepare them for analysis. 【0050】 Step 3: 【0051】 Based on the collected information, the server automatically defines the necessary functions and conditions for the artificial intelligence agent. This clarifies which models and algorithms are most suitable. 【0052】 Step 4: 【0053】 Based on the requirements definition, the server selects the most suitable model from the AI ​​model library in the database. Each model is evaluated from the perspectives of performance, cost, and scope of application to make the optimal selection. 【0054】 Step 5: 【0055】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This process involves combining the necessary APIs and program code to form the agent. 【0056】 Step 6: 【0057】 The server sends initial setup information to the terminal to integrate the completed artificial intelligence agent into the business process and provides the user with setup guidelines. 【0058】 Step 7: 【0059】 Users follow the guidelines provided via their device, install the agent according to their own workflow, and begin operations. 【0060】 Step 8: 【0061】 The server monitors the performance of the running artificial intelligence agents in real time. It continuously analyzes the collected performance data to ensure that the system is being operated effectively. 【0062】 Step 9: 【0063】 Based on monitoring results, the server automatically adjusts agent settings and optimizes functionality if it determines that performance improvements are needed. Users receive reports of these improvements via their terminal. 【0064】 (Example 1) 【0065】 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." 【0066】 In today's business environment, effectively collecting user information and taking optimal measures based on that information is necessary to perform tasks quickly and efficiently. However, the process from information collection to analysis and implementation of appropriate solutions can be complex and time-consuming. Furthermore, the implemented solutions may not perform as expected, which is another challenge. This invention aims to solve these problems. 【0067】 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. 【0068】 In this invention, the server includes a device for collecting business-related information and issues from users, a device for analyzing the collected information and automatically defining requirements, and a device for selecting the optimal machine learning model based on the defined requirements. This enables the rapid construction and deployment of an optimal intelligent agent to improve the user's work efficiency. 【0069】 A "user" is an individual or organization that uses the system to provide information related to business operations and uses that information to improve those operations. 【0070】 "Business-related information" refers to the data and issues that users provide to the system in order to perform their own work. 【0071】 "Data collection device" refers to a technical means of acquiring information provided by the user and making it available within the system. 【0072】 "Analysis" refers to the process of clarifying requirements using data processing and algorithms based on collected information. 【0073】 "Requirements definition" refers to the process of clearly defining the conditions and functions necessary for business improvement. 【0074】 A "machine learning model" refers to an algorithm that learns from accumulated data and makes predictions and decisions based on those results. 【0075】 The "selection device" refers to a function within the system that selects the machine learning model best suited to the defined requirements. 【0076】 An "intelligent agent" refers to a program built on a selected machine learning model that automates and optimizes business processes. 【0077】 "The device to be constructed" refers to the technical means for generating intelligent agents based on the selected machine learning model. 【0078】 "Integration devices" refer to the technical support needed to integrate the constructed intelligent agent into existing business processes. 【0079】 A "device that monitors performance and automatically makes improvements as needed" refers to a technical means that continuously observes the activity of an intelligent agent and automatically adjusts it when performance falls below expectations. 【0080】 The system according to the present invention has a user, a server, and a terminal as its main components. The user uses the terminal to input information and issues related to their work and transmits them to the server. This information specifically includes problems and difficulties in improving the productivity and efficiency of their work. 【0081】 The server implements natural language processing technology using programming languages ​​such as Python to analyze information sent by the user. Possible toolkits to be used include NLTK and spaCy. Using these, important keywords are extracted from the user's input, and based on these, requirements necessary for business improvement are defined. 【0082】 Based on defined requirements, the server selects the optimal model from a library of machine learning models stored in the database. Frameworks such as TENSORFLOW® and PyTorch are used for this process. Using the selected model, the server builds an intelligent agent. 【0083】 The developed intelligent agent is deployed to the user via a terminal. The server assists with the initial setup of the agent, ensuring smooth integration into business processes. After deployment, the server monitors the agent's performance in real time and optimizes it as needed. Using an auto-adjustment function, the agent's efficiency is constantly improved, allowing users to enjoy optimal work performance. 【0084】 A concrete example is when a retail user seeks to improve the efficiency of inventory management and inputs a problem such as "slow real-time processing of sales data." In response, the server builds an intelligent agent that performs inventory forecasting and optimal ordering. This agent processes sales data in real time, providing the user with the ability to instantly understand the inventory status. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0085】 As an example of a prompt, one could input the text "Please suggest the optimal AI model for real-time inventory management" into the generating AI model. 【0086】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0087】 Step 1: 【0088】 Users input information and specific issues related to their work using a terminal. The input information is in text format and includes business problems such as "delays in real-time processing." This information is sent from the terminal to the server. The server then receives this input information and proceeds to the next analysis step. 【0089】 Step 2: 【0090】 The server analyzes the received information. Using natural language processing techniques, it extracts important keywords from the input text. Specifically, it uses NLTK and spaCy to identify keywords such as "real-time processing" and "delay." The keywords obtained through this analysis are output as data for requirements definition. 【0091】 Step 3: 【0092】 The server selects the optimal machine learning model based on the requirements obtained from the analysis. At this stage, it chooses the model best suited to the business requirements from the model libraries stored in TensorFlow or PyTorch. The selected model is output as input for subsequent agent construction. 【0093】 Step 4: 【0094】 The server builds an intelligent agent using a selected machine learning model. This process includes tuning the model parameters and installing software modules. The built agent is output as a program with functions to streamline business processes. 【0095】 Step 5: 【0096】 The server deploys intelligent agents to users via their terminals. During deployment, it supports initial agent setup and integration into the work environment. This distributes the agents to the user's terminal, making specific functions for improving work efficiency available. 【0097】 Step 6: 【0098】 The server monitors the performance of deployed agents in real time. This includes monitoring their execution status and evaluating their performance. If an agent fails to perform as expected, the server uses an automatic tuning function to readjust the data to improve the agent's performance. 【0099】 (Application Example 1) 【0100】 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." 【0101】 In recent years, with the increasing diversification of lifestyles, there has been a growing demand for efficient support in individuals' daily lives and household chores. However, conventional systems often struggle to respond to individual needs in a detailed manner, resulting in users bearing a heavy burden. There is a need to solve this problem and realize flexible and efficient life support tailored to the individual needs of users. 【0102】 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. 【0103】 In this invention, the server includes means for collecting information and issues related to the user's work, means for analyzing the collected information to automatically define requirements, and means for analyzing voice data, extracting important keywords, and proposing an optimal life support schedule to the user. This enables the user to smoothly receive optimal life support tailored to their individual needs. 【0104】 A "user" is an individual or organization that uses the system to provide information and receive support. 【0105】 "Information" refers to data and issues related to the user's work and daily life. 【0106】 "Analysis" is the process of identifying important elements and needs based on collected information and converting them into a format useful within the system. 【0107】 "Requirements definition" is the process of clarifying the goals and necessary functions that a system should achieve, derived from the analyzed information. 【0108】 An "artificial intelligence model" refers to a machine learning algorithm or data structure selected according to user needs and used to optimize specific business processes. 【0109】 An "artificial intelligence agent" is a dynamic program built on a selected artificial intelligence model to automate specific tasks or processes. 【0110】 The "life support process" refers to a series of actions and activities in which an artificial intelligence agent assists and streamlines the user's daily life and household chores. 【0111】 "Monitoring" is the process of continuously observing and recording the actions and performance of an agent. 【0112】 A "schedule" is a time and task management plan proposed by an artificial intelligence agent to help users efficiently carry out their activities and household chores. 【0113】 In the system that realizes this invention, a server plays a central role. The server first receives information about work and daily life from the user via a terminal. This information may be input as voice data and converted into text data using speech recognition software. At this time, the accuracy of voice input is improved by using the speech_recognition library. 【0114】 Next, the server utilizes natural language processing technology, using software called NLTK to analyze text data. This extracts important keywords from the collected information and identifies the user's individual needs and challenges. Based on the results of the language analysis, the server selects an appropriate artificial intelligence model using the scikit-learn library and builds an artificial intelligence agent. This agent has the ability to automate specific life support processes, such as managing household chore schedules. 【0115】 For example, if a user says to the system, "I can't finish cleaning," the server converts the audio data into text, extracts key keywords, and then suggests an optimal schedule based on them. This schedule might include specific action plans such as, "Vacuum at 3 PM and bring in the laundry at 4 PM." 【0116】 Allowing users to view this plan on their devices improves daily efficiency. Furthermore, the server constantly monitors the performance of the artificial intelligence agent and automatically improves the model using machine learning algorithms as needed. This ensures that users continuously receive optimal support. 【0117】 An example of a prompt message is: "Extract keywords related to the user's household chore support from the voice input and suggest an appropriate schedule." 【0118】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0119】 Step 1: 【0120】 The user uses the device's microphone to input voice data. The voice data is received and sent to the server. Here, the input is voice data, and the output is that raw voice data. 【0121】 Step 2: 【0122】 The server utilizes the speech_recognition library to convert received speech data into text data. The input is the speech data from step 1, and the output is the corresponding text data. This conversion is performed through the analysis of speech patterns. 【0123】 Step 3: 【0124】 The server uses the NLTK library to extract important keywords from text data. This process involves segmenting the text data, removing stop words, and identifying key words. The input is the text data from step 2, and the output is the extracted keywords. 【0125】 Step 4: 【0126】 The server uses the scikit-learn library to select an appropriate artificial intelligence model based on the extracted keywords. The input is the keywords obtained in step 3, and the output is the selected artificial intelligence model. Cluster analysis and fitting are performed during this process. 【0127】 Step 5: 【0128】 The server constructs an artificial intelligence agent using the selected artificial intelligence model. The agent plans to perform tasks necessary to support the user's daily life. The input is the artificial intelligence model from step 4, and the output is the constructed agent. 【0129】 Step 6: 【0130】 The server uses the constructed agent to propose an optimal lifestyle support schedule to the user via the terminal. The input is the agent data from step 5, and the output is specific schedule information. 【0131】 Step 7: 【0132】 The user reviews the suggested schedule on their terminal and makes any necessary modifications. The input is the schedule generated by the server, and the output is the schedule confirmed by the user. 【0133】 Step 8: 【0134】 The server continuously optimizes the agent by monitoring its performance in real time and adjusting the algorithm as needed. The input is the user usage data from step 7, and the output is the optimized agent behavior. 【0135】 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. 【0136】 The present invention relates to a system comprising a user, a server, a terminal, and an emotion engine. The user not only inputs work-related information and tasks through the terminal, but their emotional state is also recognized in real time by the emotion engine. The emotion engine analyzes emotional data from the user's voice tone, facial expressions, or nuances of text input when they input information into the terminal. 【0137】 The server stores business information, issues, and emotional data analyzed by the emotion engine, all submitted by the user, in a database. This data is then comprehensively analyzed to automatically define requirements that take into account the user's business needs and emotional state. Based on the requirements definition, the server selects the most suitable artificial intelligence model from the database library. The impact of the user's emotional state on business efficiency and problem-solving is a crucial factor in this selection process. 【0138】 The server builds an artificial intelligence agent based on a selected AI model, which has the ability to adaptively respond while considering the user's emotional state. This agent can not only efficiently accomplish business tasks but also adjust the interface and feedback according to the user's emotions. 【0139】 For example, if the emotion engine detects that a user is experiencing stress, the agent simplifies the human interface and provides clearer and more considerate instructions and feedback. The server monitors whether these emotion-based dynamic adjustments improve work efficiency and automatically improves the agent's settings as needed. 【0140】 By integrating with an emotion engine in this way, the system of the present invention considers not only the user's physical input information but also their emotional state, enabling more appropriate and effective business improvement. As a result, users can receive business support optimized for their individual circumstances. 【0141】 The following describes the processing flow. 【0142】 Step 1: 【0143】 Users log in to the system using a terminal and input their company's business information and challenges. The terminal sends the user's input to the server, and at the same time, the emotion engine detects changes in the user's tone of voice and facial expressions during input and analyzes the emotion data. 【0144】 Step 2: 【0145】 The server stores business information, tasks, and emotional data obtained by the emotion engine from the terminal into a database. Based on this information, the server automatically defines requirements that take into account the user's current emotional state. In this requirements definition process, natural language processing technology is used to analyze the input data and set conditions according to the request and emotional state. 【0146】 Step 3: 【0147】 The server selects the optimal model from the AI ​​model library in the database based on defined requirements. This selection process also takes into account how the user's emotional state influences the system's requirements. 【0148】 Step 4: 【0149】 The server automatically builds an AI agent based on the selected AI model, taking into account the user's emotional state to respond appropriately. This agent can flexibly adjust its interface and response content according to the emotional state. 【0150】 Step 5: 【0151】 The server sends initial setup information to the terminal to integrate the constructed artificial intelligence agent into the business process and guides the user through the setup procedure. The user follows the instructions to install the agent and begin operations according to the business flow. 【0152】 Step 6: 【0153】 The server monitors the operation and performance of the running artificial intelligence agent and analyzes performance data based on user sentiment data obtained from the emotion engine. If the agent is not working as expected or if improvements are needed, the server automatically adjusts and optimizes the agent's settings. 【0154】 (Example 2) 【0155】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0156】 Conventional information processing systems collect and analyze business information without considering the user's emotional state, which can lead to decreased work efficiency. Furthermore, the lack of interface adjustments based on user emotions is problematic, resulting in increased psychological burden on users. Additionally, there is a lack of effective methods for improving the performance of intelligent agents. 【0157】 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. 【0158】 In this invention, the server includes means for analyzing the user's emotional state and defining requirements, means for selecting the optimal information processing model, and means for constructing an intelligent agent and adjusting the interface according to the emotional state. This enables business support that takes the user's emotions into consideration and improves the performance of the intelligent agent effectively. 【0159】 "Information" refers to data and issues related to the work provided by the user. 【0160】 "Emotional state" refers to the user's psychological condition and is data analyzed from voice tone, facial expressions, and text. 【0161】 "Requirements definition" is the process of identifying user needs and problems based on collected information and emotional states, and clarifying solutions. 【0162】 An "information processing model" refers to the algorithm or analysis method best suited to the user's situation, and is selected from a library of options. 【0163】 An "intelligent agent" is a software entity built to assist users in their work and can adjust its interface as needed. 【0164】 An "interface" is a means or structure for exchanging information between a user and a system. 【0165】 "Performance" refers to the level of efficiency and effectiveness of the activities demonstrated by the intelligent agent. 【0166】 This invention is a system that operates by combining a user, a terminal, a server, and sentiment analysis software. The following describes in detail how each element is used. 【0167】 Users input work-related information and tasks using a terminal. The terminal is equipped with a voice input device and a camera, which simultaneously capture data on the user's voice tone and facial expressions. This data is transmitted in real time to emotion analysis software via the terminal's software. 【0168】 The emotion analysis software analyzes the user's emotional state by analyzing voice tone, facial expressions, and text nuances from the user's input and actions. This analysis result is sent to a server, which stores it in a database along with business information. 【0169】 Next, the server uses the collected information to define the requirements. In this process, the server selects a generative AI model from a library and determines the optimal information processing model. Based on this, the server builds an intelligent agent and adjusts the interface according to the user's emotional state. 【0170】 For example, if emotion analysis software determines that a user is experiencing stress based on the information they have entered, the server will provide a more intuitive user interface to help reduce the user's stress. In this case, an example of a prompt message could be given to the generative AI model such as, "If the user is experiencing stress, please provide an interface based on that." 【0171】 By implementing this system, users can improve the efficiency and comfort of their work. Server-based data analysis and the development of intelligent agents are crucial elements in providing support optimized for each user's individual situation. 【0172】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0173】 Step 1: 【0174】 Users input work information and tasks from a terminal. The devices used for input include a keyboard, microphone, and camera. This generates text data, audio data, and image data, which are then received by the terminal. The input data undergoes initial processing within the terminal, and the data format is standardized. 【0175】 Step 2: 【0176】 The device transmits the acquired audio and image data to emotion analysis software. The emotion analysis software analyzes the tone of the audio and the facial expressions in the images, and outputs emotional state data. This process is performed in real time, and the analysis results are obtained as a numerical representation of the emotional state. 【0177】 Step 3: 【0178】 The server receives business information and emotional state data transmitted from the terminal. The server stores this data in a database and automatically performs requirements definition by analyzing the business information. This analysis uses natural language processing and data mining techniques, and the output generates requirements that clearly define the user's needs and challenges. 【0179】 Step 4: 【0180】 The server searches the database library based on the requirements definition and selects the optimal generating AI model. This process uses an algorithm to evaluate the suitability of the AI ​​model, and the selected model is output. 【0181】 Step 5: 【0182】 The server constructs an intelligent agent based on the selected generative AI model. During this construction process, the AI ​​model is modified to reflect the user's emotional state, adjusting the interface and functions accordingly. As a result, an intelligent agent that responds to the user's emotions is output. 【0183】 Step 6: 【0184】 The intelligent agent built by the server provides feedback to the user's terminal. For example, if analysis indicates that the user is experiencing stress, the agent provides a simple and easy-to-understand interface and adjusts the instructions and feedback provided to the user. This allows the user to receive support that reduces their psychological burden. 【0185】 Step 7: 【0186】 The server monitors the performance of the intelligent agents and collects performance data in real time. This data is analyzed, and the agents' functions and interfaces are automatically improved as needed. This provides continuously optimized business support. 【0187】 (Application Example 2) 【0188】 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". 【0189】 There is a need to mitigate the impact of stress and emotional fluctuations experienced by users during work on work efficiency, and to provide appropriate support means to facilitate smoother work processes. Conventional systems do not adequately consider user emotions in their interactions, and work execution tends to be uniform and standardized. Therefore, it is necessary to develop a system that can respond flexibly according to the user's state. 【0190】 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. 【0191】 In this invention, the server includes means for analyzing the user's emotional state in real time and dynamically adjusting the interface, means for providing appropriate content to the user, and means for monitoring the performance of the artificial intelligence agent and automatically making improvements as needed. This enables the provision of interactions that respond to the user's emotions, improving operational efficiency and optimizing the user experience. 【0192】 An "artificial intelligence model" is a set of mathematical algorithms or algorithms designed for data analysis and decision-making. 【0193】 An "artificial intelligence agent" is a program or system that acts autonomously in specific tasks or problem-solving situations, optimizing the results according to the user's needs. 【0194】 "Dynamic interface adjustment" is the process of changing the configuration and information presented in the user interface in real time according to the user's emotional state and operating status. 【0195】 "Emotional state analysis" is information processing that infers a user's mental state and emotions from their voice tone, facial expressions, text input, etc. 【0196】 "Content provision" refers to the act of presenting users with digital materials and information tailored to their specific purposes, such as information, entertainment, and feedback. 【0197】 "Performance monitoring" is a process for continuously evaluating whether artificial intelligence agents and the entire system are functioning effectively and efficiently. 【0198】 "Automatic improvement" refers to a system or agent making adjustments to improve its performance and functionality based on the environment and user feedback. 【0199】 To implement this invention, the user inputs work-related information into the terminal through voice, facial expressions, and text input. The emotion engine installed in the terminal analyzes the user's emotional state in real time from these inputs. Specifically, changes in voice tone are analyzed by a voice recognition API, and facial expressions are determined by facial expression recognition software. Furthermore, the nuances of text input are processed by a natural language processing engine. 【0200】 The server stores business information, issues, and analyzed sentiment data submitted by users in a database. A standard database management system is used here, enabling efficient storage and retrieval of information. Subsequently, the server uses a generation AI model based on the stored data to automatically define requirements tailored to the user's emotional state and business needs. This is handled by the requirements definition engine. 【0201】 Based on the selected artificial intelligence model, the server builds an AI agent. This agent adapts to the user's real-time emotional state, dynamically adjusts the interface, and provides the user with appropriate content. The robot can display entertainment content to provide the user with a relaxing environment. User feedback is sent back to the server, which monitors the agent's performance and automatically makes improvements as needed. 【0202】 A concrete example of a use case is in the home. For instance, if a child feels stressed while doing homework, the robot can detect the stress and suggest, "Let's take a short break and listen to some fun music together," thereby refreshing the user's mood. 【0203】 An example of a prompt message might be, "Simulate how the user will react when they experience stress." 【0204】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0205】 Step 1: 【0206】 Users provide information through voice, facial expressions, and text input via their devices. This input includes work-related data and emotional states. The device passes this information to an emotion engine, which performs nuance analysis of voice tone, facial expressions, and text. The analysis results are output as emotion data. 【0207】 Step 2: 【0208】 The server stores business information and sentiment data transmitted from the terminal in a database. During storage, the data is structured and converted into a format that allows for easy searching and analysis. Inputs include user business information and analyzed sentiment data, while output is a record in the database. 【0209】 Step 3: 【0210】 The server automatically defines requirements using a generative AI model based on business information and emotional data stored in the database. The input is information from the database, and the output is the result of the requirements definition. In this process, the impact of the user's emotional state on business operations is analyzed, and an appropriate artificial intelligence model is selected. 【0211】 Step 4: 【0212】 The server uses the AI ​​model selected based on the requirements definition to build an AI agent. The agent prepares to adjust the interface in response to the user's emotional state. At this stage, the selected AI model is used as input, and the result of building the agent is output. 【0213】 Step 5: 【0214】 Using a constructed artificial intelligence agent, the server dynamically adjusts the interface and content provided to the user. Specifically, if the agent determines that the user is experiencing stress, it will provide relaxing music or entertainment content. The input is the user's real-time emotional data, and the output is the adjusted interface and the content provided. 【0215】 Step 6: 【0216】 The server monitors the performance of the artificial intelligence agent based on feedback and automatically makes improvements as needed. The input is user feedback data, and the output is the improved agent settings. This process involves real-time system adjustments to maximize operational efficiency and user satisfaction. 【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】 The system of the present invention comprises a user, a server, and a terminal. The user logs into the system and provides work-related information and tasks to the server via the terminal. The server receives this information and stores it in a database. 【0234】 Next, the server analyzes the received information and automatically defines the requirements necessary for business improvement. In this process, natural language processing technology is used to extract important keywords from the user's input, and the requirements are identified based on these keywords. Based on the defined requirements, the server selects the optimal model from the artificial intelligence model library in the database. 【0235】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This agent has the capability to perform various functions to streamline the user's business processes. 【0236】 The constructed agent is deployed to the user via a terminal. The server assists with the initial setup of the agent and its integration into business processes, ensuring a smooth deployment. After deployment, the server monitors the agent's execution status in real time and analyzes its performance data. 【0237】 If the agent's performance falls below expectations, the server uses an automatic adjustment function to improve the agent's performance. This continuous monitoring and improvement ensures that users can always enjoy efficient and optimal work performance. 【0238】 For example, if a retail user wants to improve the efficiency of their inventory management, they might input issues such as "slow real-time processing of sales data." The server receives this information and builds an artificial intelligence agent to forecast inventory and make optimal orders. Once the agent is running, the user can understand the inventory situation in real time and know the appropriate timing for ordering. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0239】 The following describes the processing flow. 【0240】 Step 1: 【0241】 Users log in to the system using their terminals and input specific details about their company's business operations and current challenges. The terminals then send this information to the server. 【0242】 Step 2: 【0243】 The server stores business information and issues received from users in a database. During this process, the server uses natural language processing technology to extract important keywords and prepare them for analysis. 【0244】 Step 3: 【0245】 Based on the collected information, the server automatically defines the necessary functions and conditions for the artificial intelligence agent. This clarifies which models and algorithms are most suitable. 【0246】 Step 4: 【0247】 Based on the requirements definition, the server selects the most suitable model from the AI ​​model library in the database. Each model is evaluated from the perspectives of performance, cost, and scope of application to make the optimal selection. 【0248】 Step 5: 【0249】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This process involves combining the necessary APIs and program code to form the agent. 【0250】 Step 6: 【0251】 The server sends initial setup information to the terminal to integrate the completed artificial intelligence agent into the business process and provides the user with setup guidelines. 【0252】 Step 7: 【0253】 Users follow the guidelines provided via their device, install the agent according to their own workflow, and begin operations. 【0254】 Step 8: 【0255】 The server monitors the performance of the running artificial intelligence agents in real time. It continuously analyzes the collected performance data to ensure that the system is being operated effectively. 【0256】 Step 9: 【0257】 Based on monitoring results, the server automatically adjusts agent settings and optimizes functionality if it determines that performance improvements are needed. Users receive reports of these improvements via their terminal. 【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】 In today's business environment, effectively collecting user information and taking optimal measures based on that information is necessary to perform tasks quickly and efficiently. However, the process from information collection to analysis and implementation of appropriate solutions can be complex and time-consuming. Furthermore, the implemented solutions may not perform as expected, which is another challenge. This invention aims to solve these problems. 【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 a device for collecting business-related information and issues from users, a device for analyzing the collected information and automatically defining requirements, and a device for selecting the optimal machine learning model based on the defined requirements. This enables the rapid construction and deployment of an optimal intelligent agent to improve the user's work efficiency. 【0263】 A "user" is an individual or organization that uses the system to provide information related to business operations and uses that information to improve those operations. 【0264】 "Business-related information" refers to the data and issues that users provide to the system in order to perform their own work. 【0265】 "Data collection device" refers to a technical means of acquiring information provided by the user and making it available within the system. 【0266】 "Analysis" refers to the process of clarifying requirements using data processing and algorithms based on collected information. 【0267】 "Requirements definition" refers to the process of clearly defining the conditions and functions necessary for business improvement. 【0268】 A "machine learning model" refers to an algorithm that learns from accumulated data and makes predictions and decisions based on those results. 【0269】 The "selection device" refers to a function within the system that selects the machine learning model best suited to the defined requirements. 【0270】 An "intelligent agent" refers to a program built on a selected machine learning model that automates and optimizes business processes. 【0271】 "The device to be constructed" refers to the technical means for generating intelligent agents based on the selected machine learning model. 【0272】 "Integration devices" refer to the technical support needed to integrate the constructed intelligent agent into existing business processes. 【0273】 A "device that monitors performance and automatically makes improvements as needed" refers to a technical means that continuously observes the activity of an intelligent agent and automatically adjusts it when performance falls below expectations. 【0274】 The system according to the present invention has a user, a server, and a terminal as its main components. The user uses the terminal to input information and issues related to their work and transmits them to the server. This information specifically includes problems and difficulties in improving the productivity and efficiency of their work. 【0275】 The server implements natural language processing technology using programming languages ​​such as Python to analyze information sent by the user. Possible toolkits to be used include NLTK and spaCy. Using these, important keywords are extracted from the user's input, and based on these, requirements necessary for business improvement are defined. 【0276】 Based on defined requirements, the server selects the optimal model from a library of machine learning models stored in the database. Frameworks such as TensorFlow and PyTorch are used for this process. Using the selected model, the server builds an intelligent agent. 【0277】 The developed intelligent agent is deployed to the user via a terminal. The server assists with the initial setup of the agent, ensuring smooth integration into business processes. After deployment, the server monitors the agent's performance in real time and optimizes it as needed. Using an auto-adjustment function, the agent's efficiency is constantly improved, allowing users to enjoy optimal work performance. 【0278】 A concrete example is when a retail user seeks to improve the efficiency of inventory management and inputs a problem such as "slow real-time processing of sales data." In response, the server builds an intelligent agent that performs inventory forecasting and optimal ordering. This agent processes sales data in real time, providing the user with the ability to instantly understand the inventory status. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0279】 As an example of a prompt sentence, it is conceivable to input text such as "Please propose an optimal AI model for real-time inventory management" into a generative AI model. 【0280】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0281】 Step 1: 【0282】 The user uses a terminal to input information related to business and specific problems. The information to be input includes business problems such as "delay in real-time processing" in text form. This is sent from the terminal to the server. Then, the server receives this input information and proceeds to the next analysis step. 【0283】 Step 2: 【0284】 The server analyzes the received information. Using natural language processing technology, it extracts important keywords from the input text. Specifically, it uses NLTK or spaCy to identify keywords such as "real-time processing" and "delay". The keywords obtained by this analysis are output as data for requirement definition. 【0285】 Step 3: 【0286】 The server selects an optimal machine learning model based on the requirements obtained from the analysis. At this stage, from the model libraries stored in TensorFlow or PyTorch, the model most suitable for the business requirements is selected. The selected model is output as input for subsequent agent construction. 【0287】 Step 4: 【0288】 The server builds an intelligent agent using a selected machine learning model. This process includes tuning the model parameters and installing software modules. The built agent is output as a program with functions to streamline business processes. 【0289】 Step 5: 【0290】 The server deploys intelligent agents to users via their terminals. During deployment, it supports initial agent setup and integration into the work environment. This distributes the agents to the user's terminal, making specific functions for improving work efficiency available. 【0291】 Step 6: 【0292】 The server monitors the performance of deployed agents in real time. This includes monitoring their execution status and evaluating their performance. If an agent fails to perform as expected, the server uses an automatic tuning function to readjust the data to improve the agent's performance. 【0293】 (Application Example 1) 【0294】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0295】 In recent years, with the increasing diversification of lifestyles, there has been a growing demand for efficient support in individuals' daily lives and household chores. However, conventional systems often struggle to respond to individual needs in a detailed manner, resulting in users bearing a heavy burden. There is a need to solve this problem and realize flexible and efficient life support tailored to the individual needs of users. 【0296】 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. 【0297】 In this invention, the server includes means for collecting information and issues related to the user's work, means for analyzing the collected information to automatically define requirements, and means for analyzing voice data, extracting important keywords, and proposing an optimal life support schedule to the user. This enables the user to smoothly receive optimal life support tailored to their individual needs. 【0298】 A "user" is an individual or organization that uses the system to provide information and receive support. 【0299】 "Information" refers to data and issues related to the user's work and daily life. 【0300】 "Analysis" is the process of identifying important elements and needs based on collected information and converting them into a format useful within the system. 【0301】 "Requirements definition" is the process of clarifying the goals and necessary functions that a system should achieve, derived from the analyzed information. 【0302】 An "artificial intelligence model" refers to a machine learning algorithm or data structure selected according to user needs and used to optimize specific business processes. 【0303】 An "artificial intelligence agent" is a dynamic program built on a selected artificial intelligence model to automate specific tasks or processes. 【0304】 The "life support process" refers to a series of actions and activities in which an artificial intelligence agent assists and streamlines the user's daily life and household chores. 【0305】 "Monitoring" is the process of continuously observing and recording the actions and performance of an agent. 【0306】 A "schedule" is a management plan of time and tasks proposed by an artificial intelligence agent to efficiently carry out a user's activities and housework. 【0307】 In the system that realizes this invention, the server plays a central role. The server first receives information related to business and life from the user via a terminal. The information may be input as voice data and is converted into text data using speech recognition software. At this time, by using the speech_recognition library, the accuracy of voice input is improved. 【0308】 Next, the server utilizes natural language processing technology and uses software called nltk to analyze the text data. As a result, important keywords are extracted from the collected information, and the individual needs and issues of the user are identified. Based on the results of language analysis, the server selects an appropriate artificial intelligence model by using the sklearn library and constructs an artificial intelligence agent. This agent has the ability to automate a specific life support process, for example, to manage the schedule of housework. 【0309】 As a specific example, when the user talks to the system saying "I can't finish cleaning", the server converts the voice data into text, extracts important keywords, and proposes an optimal schedule based on them. This schedule includes a specific action plan such as "Run the vacuum cleaner at 3 pm and take in the laundry at 4 pm". 【0310】 By enabling the user to confirm this plan on the terminal, it has the effect of enhancing daily efficiency. Also, the server constantly monitors the performance of the artificial intelligence agent and, if necessary, automatically improves the model using machine learning algorithms. As a result, the user can continuously receive optimal support. 【0311】 An example of a prompt message is: "Extract keywords related to the user's household chore support from the voice input and suggest an appropriate schedule." 【0312】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0313】 Step 1: 【0314】 The user uses the device's microphone to input voice data. The voice data is received and sent to the server. Here, the input is voice data, and the output is that raw voice data. 【0315】 Step 2: 【0316】 The server utilizes the speech_recognition library to convert received speech data into text data. The input is the speech data from step 1, and the output is the corresponding text data. This conversion is performed through the analysis of speech patterns. 【0317】 Step 3: 【0318】 The server uses the NLTK library to extract important keywords from text data. This process involves segmenting the text data, removing stop words, and identifying key words. The input is the text data from step 2, and the output is the extracted keywords. 【0319】 Step 4: 【0320】 The server uses the scikit-learn library to select an appropriate artificial intelligence model based on the extracted keywords. The input is the keywords obtained in step 3, and the output is the selected artificial intelligence model. Cluster analysis and fitting are performed during this process. 【0321】 Step 5: 【0322】 The server constructs an artificial intelligence agent using the selected artificial intelligence model. The agent plans to perform tasks necessary to support the user's daily life. The input is the artificial intelligence model from step 4, and the output is the constructed agent. 【0323】 Step 6: 【0324】 The server uses the constructed agent to propose an optimal lifestyle support schedule to the user via the terminal. The input is the agent data from step 5, and the output is specific schedule information. 【0325】 Step 7: 【0326】 The user reviews the suggested schedule on their terminal and makes any necessary modifications. The input is the schedule generated by the server, and the output is the schedule confirmed by the user. 【0327】 Step 8: 【0328】 The server continuously optimizes the agent by monitoring its performance in real time and adjusting the algorithm as needed. The input is the user usage data from step 7, and the output is the optimized agent behavior. 【0329】 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. 【0330】 The present invention relates to a system comprising a user, a server, a terminal, and an emotion engine. The user not only inputs work-related information and tasks through the terminal, but their emotional state is also recognized in real time by the emotion engine. The emotion engine analyzes emotional data from the user's voice tone, facial expressions, or nuances of text input when they input information into the terminal. 【0331】 The server stores business information, issues, and emotional data analyzed by the emotion engine, all submitted by the user, in a database. This data is then comprehensively analyzed to automatically define requirements that take into account the user's business needs and emotional state. Based on the requirements definition, the server selects the most suitable artificial intelligence model from the database library. The impact of the user's emotional state on business efficiency and problem-solving is a crucial factor in this selection process. 【0332】 The server builds an artificial intelligence agent based on a selected AI model, which has the ability to adaptively respond while considering the user's emotional state. This agent can not only efficiently accomplish business tasks but also adjust the interface and feedback according to the user's emotions. 【0333】 For example, if the emotion engine detects that a user is experiencing stress, the agent simplifies the human interface and provides clearer and more considerate instructions and feedback. The server monitors whether these emotion-based dynamic adjustments improve work efficiency and automatically improves the agent's settings as needed. 【0334】 By integrating with an emotion engine in this way, the system of the present invention considers not only the user's physical input information but also their emotional state, enabling more appropriate and effective business improvement. As a result, users can receive business support optimized for their individual circumstances. 【0335】 The following describes the processing flow. 【0336】 Step 1: 【0337】 Users log in to the system using a terminal and input their company's business information and challenges. The terminal sends the user's input to the server, and at the same time, the emotion engine detects changes in the user's tone of voice and facial expressions during input and analyzes the emotion data. 【0338】 Step 2: 【0339】 The server stores business information, tasks, and emotional data obtained by the emotion engine from the terminal into a database. Based on this information, the server automatically defines requirements that take into account the user's current emotional state. In this requirements definition process, natural language processing technology is used to analyze the input data and set conditions according to the request and emotional state. 【0340】 Step 3: 【0341】 The server selects the optimal model from the AI ​​model library in the database based on defined requirements. This selection process also takes into account how the user's emotional state influences the system's requirements. 【0342】 Step 4: 【0343】 The server automatically builds an AI agent based on the selected AI model, taking into account the user's emotional state to respond appropriately. This agent can flexibly adjust its interface and response content according to the emotional state. 【0344】 Step 5: 【0345】 The server sends initial setup information to the terminal to integrate the constructed artificial intelligence agent into the business process and guides the user through the setup procedure. The user follows the instructions to install the agent and begin operations according to the business flow. 【0346】 Step 6: 【0347】 The server monitors the operation and performance of the running artificial intelligence agent and analyzes performance data based on user sentiment data obtained from the emotion engine. If the agent is not working as expected or if improvements are needed, the server automatically adjusts and optimizes the agent's settings. 【0348】 (Example 2) 【0349】 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". 【0350】 Conventional information processing systems collect and analyze business information without considering the user's emotional state, which can lead to decreased work efficiency. Furthermore, the lack of interface adjustments based on user emotions is problematic, resulting in increased psychological burden on users. Additionally, there is a lack of effective methods for improving the performance of intelligent agents. 【0351】 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. 【0352】 In this invention, the server includes means for analyzing the user's emotional state and defining requirements, means for selecting the optimal information processing model, and means for constructing an intelligent agent and adjusting the interface according to the emotional state. This enables business support that takes the user's emotions into consideration and improves the performance of the intelligent agent effectively. 【0353】 "Information" refers to data and issues related to the work provided by the user. 【0354】 "Emotional state" refers to the user's psychological condition and is data analyzed from voice tone, facial expressions, and text. 【0355】 "Requirements definition" is the process of identifying user needs and problems based on collected information and emotional states, and clarifying solutions. 【0356】 An "information processing model" refers to the algorithm or analysis method best suited to the user's situation, and is selected from a library of options. 【0357】 An "intelligent agent" is a software entity built to assist users in their work and can adjust its interface as needed. 【0358】 An "interface" is a means or structure for exchanging information between a user and a system. 【0359】 "Performance" refers to the level of efficiency and effectiveness of the activities demonstrated by the intelligent agent. 【0360】 This invention is a system that operates by combining a user, a terminal, a server, and sentiment analysis software. The following describes in detail how each element is used. 【0361】 Users input work-related information and tasks using a terminal. The terminal is equipped with a voice input device and a camera, which simultaneously capture data on the user's voice tone and facial expressions. This data is transmitted in real time to emotion analysis software via the terminal's software. 【0362】 The emotion analysis software analyzes the user's emotional state by analyzing voice tone, facial expressions, and text nuances from the user's input and actions. This analysis result is sent to a server, which stores it in a database along with business information. 【0363】 Next, the server uses the collected information to define the requirements. In this process, the server selects a generative AI model from a library and determines the optimal information processing model. Based on this, the server builds an intelligent agent and adjusts the interface according to the user's emotional state. 【0364】 For example, if emotion analysis software determines that a user is experiencing stress based on the information they have entered, the server will provide a more intuitive user interface to help reduce the user's stress. In this case, an example of a prompt message could be given to the generative AI model such as, "If the user is experiencing stress, please provide an interface based on that." 【0365】 By implementing this system, users can improve the efficiency and comfort of their work. Server-based data analysis and the development of intelligent agents are crucial elements in providing support optimized for each user's individual situation. 【0366】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0367】 Step 1: 【0368】 Users input work information and tasks from a terminal. The devices used for input include a keyboard, microphone, and camera. This generates text data, audio data, and image data, which are then received by the terminal. The input data undergoes initial processing within the terminal, and the data format is standardized. 【0369】 Step 2: 【0370】 The device transmits the acquired audio and image data to emotion analysis software. The emotion analysis software analyzes the tone of the audio and the facial expressions in the images, and outputs emotional state data. This process is performed in real time, and the analysis results are obtained as a numerical representation of the emotional state. 【0371】 Step 3: 【0372】 The server receives business information and emotional state data transmitted from the terminal. The server stores this data in a database and automatically performs requirements definition by analyzing the business information. This analysis uses natural language processing and data mining techniques, and the output generates requirements that clearly define the user's needs and challenges. 【0373】 Step 4: 【0374】 The server searches the database library based on the requirements definition and selects the optimal generating AI model. This process uses an algorithm to evaluate the suitability of the AI ​​model, and the selected model is output. 【0375】 Step 5: 【0376】 The server constructs an intelligent agent based on the selected generative AI model. During this construction process, the AI ​​model is modified to reflect the user's emotional state, adjusting the interface and functions accordingly. As a result, an intelligent agent that responds to the user's emotions is output. 【0377】 Step 6: 【0378】 The intelligent agent built by the server provides feedback to the user's terminal. For example, if analysis indicates that the user is experiencing stress, the agent provides a simple and easy-to-understand interface and adjusts the instructions and feedback provided to the user. This allows the user to receive support that reduces their psychological burden. 【0379】 Step 7: 【0380】 The server monitors the performance of the intelligent agents and collects performance data in real time. This data is analyzed, and the agents' functions and interfaces are automatically improved as needed. This provides continuously optimized business support. 【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】 There is a need to mitigate the impact of stress and emotional fluctuations experienced by users during work on work efficiency, and to provide appropriate support means to facilitate smoother work processes. Conventional systems do not adequately consider user emotions in their interactions, and work execution tends to be uniform and standardized. Therefore, it is necessary to develop a system that can respond flexibly according to the user's state. 【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 means for analyzing the user's emotional state in real time and dynamically adjusting the interface, means for providing appropriate content to the user, and means for monitoring the performance of the artificial intelligence agent and automatically making improvements as needed. This enables the provision of interactions that respond to the user's emotions, improving operational efficiency and optimizing the user experience. 【0386】 An "artificial intelligence model" is a set of mathematical algorithms or algorithms designed for data analysis and decision-making. 【0387】 An "artificial intelligence agent" is a program or system that acts autonomously in specific tasks or problem-solving situations, optimizing the results according to the user's needs. 【0388】 "Dynamic interface adjustment" is the process of changing the configuration and information presented in the user interface in real time according to the user's emotional state and operating status. 【0389】 "Emotional state analysis" is information processing that infers a user's mental state and emotions from their voice tone, facial expressions, text input, etc. 【0390】 "Content provision" refers to the act of presenting users with digital materials and information tailored to their specific purposes, such as information, entertainment, and feedback. 【0391】 "Performance monitoring" is a process for continuously evaluating whether artificial intelligence agents and the entire system are functioning effectively and efficiently. 【0392】 "Automatic improvement" refers to a system or agent making adjustments to improve its performance and functionality based on the environment and user feedback. 【0393】 To implement this invention, the user inputs work-related information into the terminal through voice, facial expressions, and text input. The emotion engine installed in the terminal analyzes the user's emotional state in real time from these inputs. Specifically, changes in voice tone are analyzed by a voice recognition API, and facial expressions are determined by facial expression recognition software. Furthermore, the nuances of text input are processed by a natural language processing engine. 【0394】 The server stores business information, issues, and analyzed sentiment data submitted by users in a database. A standard database management system is used here, enabling efficient storage and retrieval of information. Subsequently, the server uses a generation AI model based on the stored data to automatically define requirements tailored to the user's emotional state and business needs. This is handled by the requirements definition engine. 【0395】 Based on the selected artificial intelligence model, the server builds an AI agent. This agent adapts to the user's real-time emotional state, dynamically adjusts the interface, and provides the user with appropriate content. The robot can display entertainment content to provide the user with a relaxing environment. User feedback is sent back to the server, which monitors the agent's performance and automatically makes improvements as needed. 【0396】 A concrete example of a use case is in the home. For instance, if a child feels stressed while doing homework, the robot can detect the stress and suggest, "Let's take a short break and listen to some fun music together," thereby refreshing the user's mood. 【0397】 An example of a prompt message might be, "Simulate how the user will react when they experience stress." 【0398】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0399】 Step 1: 【0400】 Users provide information through voice, facial expressions, and text input via their devices. This input includes work-related data and emotional states. The device passes this information to an emotion engine, which performs nuance analysis of voice tone, facial expressions, and text. The analysis results are output as emotion data. 【0401】 Step 2: 【0402】 The server stores business information and sentiment data transmitted from the terminal in a database. During storage, the data is structured and converted into a format that allows for easy searching and analysis. Inputs include user business information and analyzed sentiment data, while output is a record in the database. 【0403】 Step 3: 【0404】 The server automatically defines requirements using a generative AI model based on business information and emotional data stored in the database. The input is information from the database, and the output is the result of the requirements definition. In this process, the impact of the user's emotional state on business operations is analyzed, and an appropriate artificial intelligence model is selected. 【0405】 Step 4: 【0406】 The server uses the AI ​​model selected based on the requirements definition to build an AI agent. The agent prepares to adjust the interface in response to the user's emotional state. At this stage, the selected AI model is used as input, and the result of building the agent is output. 【0407】 Step 5: 【0408】 Using a constructed artificial intelligence agent, the server dynamically adjusts the interface and content provided to the user. Specifically, if the agent determines that the user is experiencing stress, it will provide relaxing music or entertainment content. The input is the user's real-time emotional data, and the output is the adjusted interface and the content provided. 【0409】 Step 6: 【0410】 The server monitors the performance of the artificial intelligence agent based on feedback and automatically makes improvements as needed. The input is user feedback data, and the output is the improved agent settings. This process involves real-time system adjustments to maximize operational efficiency and user satisfaction. 【0411】 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. 【0412】 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. 【0413】 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. 【0414】 [Third Embodiment] 【0415】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0416】 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. 【0417】 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). 【0418】 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. 【0419】 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. 【0420】 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). 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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. 【0426】 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". 【0427】 The system of the present invention comprises a user, a server, and a terminal. The user logs into the system and provides work-related information and tasks to the server via the terminal. The server receives this information and stores it in a database. 【0428】 Next, the server analyzes the received information and automatically defines the requirements necessary for business improvement. In this process, natural language processing technology is used to extract important keywords from the user's input, and the requirements are identified based on these keywords. Based on the defined requirements, the server selects the optimal model from the artificial intelligence model library in the database. 【0429】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This agent has the capability to perform various functions to streamline the user's business processes. 【0430】 The constructed agent is deployed to the user via a terminal. The server assists with the initial setup of the agent and its integration into business processes, ensuring a smooth deployment. After deployment, the server monitors the agent's execution status in real time and analyzes its performance data. 【0431】 If the agent's performance falls below expectations, the server uses an automatic adjustment function to improve the agent's performance. This continuous monitoring and improvement ensures that users can always enjoy efficient and optimal work performance. 【0432】 For example, if a retail user wants to improve the efficiency of their inventory management, they might input issues such as "slow real-time processing of sales data." The server receives this information and builds an artificial intelligence agent to forecast inventory and make optimal orders. Once the agent is running, the user can understand the inventory situation in real time and know the appropriate timing for ordering. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0433】 The following describes the processing flow. 【0434】 Step 1: 【0435】 Users log in to the system using their terminals and input specific details about their company's business operations and current challenges. The terminals then send this information to the server. 【0436】 Step 2: 【0437】 The server stores business information and issues received from users in a database. During this process, the server uses natural language processing technology to extract important keywords and prepare them for analysis. 【0438】 Step 3: 【0439】 Based on the collected information, the server automatically defines the necessary functions and conditions for the artificial intelligence agent. This clarifies which models and algorithms are most suitable. 【0440】 Step 4: 【0441】 Based on the requirements definition, the server selects the most suitable model from the AI ​​model library in the database. Each model is evaluated from the perspectives of performance, cost, and scope of application to make the optimal selection. 【0442】 Step 5: 【0443】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This process involves combining the necessary APIs and program code to form the agent. 【0444】 Step 6: 【0445】 The server sends initial setup information to the terminal to integrate the completed artificial intelligence agent into the business process and provides the user with setup guidelines. 【0446】 Step 7: 【0447】 Users follow the guidelines provided via their device, install the agent according to their own workflow, and begin operations. 【0448】 Step 8: 【0449】 The server monitors the performance of the running artificial intelligence agents in real time. It continuously analyzes the collected performance data to ensure that the system is being operated effectively. 【0450】 Step 9: 【0451】 Based on monitoring results, the server automatically adjusts agent settings and optimizes functionality if it determines that performance improvements are needed. Users receive reports of these improvements via their terminal. 【0452】 (Example 1) 【0453】 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." 【0454】 In today's business environment, effectively collecting user information and taking optimal measures based on that information is necessary to perform tasks quickly and efficiently. However, the process from information collection to analysis and implementation of appropriate solutions can be complex and time-consuming. Furthermore, the implemented solutions may not perform as expected, which is another challenge. This invention aims to solve these problems. 【0455】 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. 【0456】 In this invention, the server includes a device for collecting business-related information and issues from users, a device for analyzing the collected information and automatically defining requirements, and a device for selecting the optimal machine learning model based on the defined requirements. This enables the rapid construction and deployment of an optimal intelligent agent to improve the user's work efficiency. 【0457】 A "user" is an individual or organization that uses the system to provide information related to business operations and uses that information to improve those operations. 【0458】 "Business-related information" refers to the data and issues that users provide to the system in order to perform their own work. 【0459】 "Data collection device" refers to a technical means of acquiring information provided by the user and making it available within the system. 【0460】 "Analysis" refers to the process of clarifying requirements using data processing and algorithms based on collected information. 【0461】 "Requirements definition" refers to the process of clearly defining the conditions and functions necessary for business improvement. 【0462】 A "machine learning model" refers to an algorithm that learns from accumulated data and makes predictions and decisions based on those results. 【0463】 The "selection device" refers to a function within the system that selects the machine learning model best suited to the defined requirements. 【0464】 An "intelligent agent" refers to a program built on a selected machine learning model that automates and optimizes business processes. 【0465】 "The device to be constructed" refers to the technical means for generating intelligent agents based on the selected machine learning model. 【0466】 "Integration devices" refer to the technical support needed to integrate the constructed intelligent agent into existing business processes. 【0467】 A "device that monitors performance and automatically makes improvements as needed" refers to a technical means that continuously observes the activity of an intelligent agent and automatically adjusts it when performance falls below expectations. 【0468】 The system according to the present invention has a user, a server, and a terminal as its main components. The user uses the terminal to input information and issues related to their work and transmits them to the server. This information specifically includes problems and difficulties in improving the productivity and efficiency of their work. 【0469】 The server implements natural language processing technology using programming languages ​​such as Python to analyze information sent by the user. Possible toolkits to be used include NLTK and spaCy. Using these, important keywords are extracted from the user's input, and based on these, requirements necessary for business improvement are defined. 【0470】 Based on defined requirements, the server selects the optimal model from a library of machine learning models stored in the database. Frameworks such as TensorFlow and PyTorch are used for this process. Using the selected model, the server builds an intelligent agent. 【0471】 The developed intelligent agent is deployed to the user via a terminal. The server assists with the initial setup of the agent, ensuring smooth integration into business processes. After deployment, the server monitors the agent's performance in real time and optimizes it as needed. Using an auto-adjustment function, the agent's efficiency is constantly improved, allowing users to enjoy optimal work performance. 【0472】 A concrete example is when a retail user seeks to improve the efficiency of inventory management and inputs a problem such as "slow real-time processing of sales data." In response, the server builds an intelligent agent that performs inventory forecasting and optimal ordering. This agent processes sales data in real time, providing the user with the ability to instantly understand the inventory status. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0473】 As an example of a prompt, one could input the text "Please suggest the optimal AI model for real-time inventory management" into the generating AI model. 【0474】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0475】 Step 1: 【0476】 Users input information and specific issues related to their work using a terminal. The input information is in text format and includes business problems such as "delays in real-time processing." This information is sent from the terminal to the server. The server then receives this input information and proceeds to the next analysis step. 【0477】 Step 2: 【0478】 The server analyzes the received information. Using natural language processing techniques, it extracts important keywords from the input text. Specifically, it uses NLTK and spaCy to identify keywords such as "real-time processing" and "delay." The keywords obtained through this analysis are output as data for requirements definition. 【0479】 Step 3: 【0480】 The server selects the optimal machine learning model based on the requirements obtained from the analysis. At this stage, it chooses the model best suited to the business requirements from the model libraries stored in TensorFlow or PyTorch. The selected model is output as input for subsequent agent construction. 【0481】 Step 4: 【0482】 The server builds an intelligent agent using a selected machine learning model. This process includes tuning the model parameters and installing software modules. The built agent is output as a program with functions to streamline business processes. 【0483】 Step 5: 【0484】 The server deploys intelligent agents to users via their terminals. During deployment, it supports initial agent setup and integration into the work environment. This distributes the agents to the user's terminal, making specific functions for improving work efficiency available. 【0485】 Step 6: 【0486】 The server monitors the performance of deployed agents in real time. This includes monitoring their execution status and evaluating their performance. If an agent fails to perform as expected, the server uses an automatic tuning function to readjust the data to improve the agent's performance. 【0487】 (Application Example 1) 【0488】 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." 【0489】 In recent years, with the increasing diversification of lifestyles, there has been a growing demand for efficient support in individuals' daily lives and household chores. However, conventional systems often struggle to respond to individual needs in a detailed manner, resulting in users bearing a heavy burden. There is a need to solve this problem and realize flexible and efficient life support tailored to the individual needs of users. 【0490】 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. 【0491】 In this invention, the server includes means for collecting information and issues related to the user's work, means for analyzing the collected information to automatically define requirements, and means for analyzing voice data, extracting important keywords, and proposing an optimal life support schedule to the user. This enables the user to smoothly receive optimal life support tailored to their individual needs. 【0492】 A "user" is an individual or organization that uses the system to provide information and receive support. 【0493】 "Information" refers to data and issues related to the user's work and daily life. 【0494】 "Analysis" is the process of identifying important elements and needs based on collected information and converting them into a format useful within the system. 【0495】 "Requirements definition" is the process of clarifying the goals and necessary functions that a system should achieve, derived from the analyzed information. 【0496】 An "artificial intelligence model" refers to a machine learning algorithm or data structure selected according to user needs and used to optimize specific business processes. 【0497】 An "artificial intelligence agent" is a dynamic program built on a selected artificial intelligence model to automate specific tasks or processes. 【0498】 The "life support process" refers to a series of actions and activities in which an artificial intelligence agent assists and streamlines the user's daily life and household chores. 【0499】 "Monitoring" is the process of continuously observing and recording the actions and performance of an agent. 【0500】 A "schedule" is a time and task management plan proposed by an artificial intelligence agent to help users efficiently carry out their activities and household chores. 【0501】 In the system that realizes this invention, a server plays a central role. The server first receives information about work and daily life from the user via a terminal. This information may be input as voice data and converted into text data using speech recognition software. At this time, the accuracy of voice input is improved by using the speech_recognition library. 【0502】 Next, the server utilizes natural language processing technology, using software called NLTK to analyze text data. This extracts important keywords from the collected information and identifies the user's individual needs and challenges. Based on the results of the language analysis, the server selects an appropriate artificial intelligence model using the scikit-learn library and builds an artificial intelligence agent. This agent has the ability to automate specific life support processes, such as managing household chore schedules. 【0503】 For example, if a user says to the system, "I can't finish cleaning," the server converts the audio data into text, extracts key keywords, and then suggests an optimal schedule based on them. This schedule might include specific action plans such as, "Vacuum at 3 PM and bring in the laundry at 4 PM." 【0504】 Allowing users to view this plan on their devices improves daily efficiency. Furthermore, the server constantly monitors the performance of the artificial intelligence agent and automatically improves the model using machine learning algorithms as needed. This ensures that users continuously receive optimal support. 【0505】 An example of a prompt message is: "Extract keywords related to the user's household chore support from the voice input and suggest an appropriate schedule." 【0506】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0507】 Step 1: 【0508】 The user uses the device's microphone to input voice data. The voice data is received and sent to the server. Here, the input is voice data, and the output is that raw voice data. 【0509】 Step 2: 【0510】 The server utilizes the speech_recognition library to convert received speech data into text data. The input is the speech data from step 1, and the output is the corresponding text data. This conversion is performed through the analysis of speech patterns. 【0511】 Step 3: 【0512】 The server uses the NLTK library to extract important keywords from text data. This process involves segmenting the text data, removing stop words, and identifying key words. The input is the text data from step 2, and the output is the extracted keywords. 【0513】 Step 4: 【0514】 The server uses the scikit-learn library to select an appropriate artificial intelligence model based on the extracted keywords. The input is the keywords obtained in step 3, and the output is the selected artificial intelligence model. Cluster analysis and fitting are performed during this process. 【0515】 Step 5: 【0516】 The server constructs an artificial intelligence agent using the selected artificial intelligence model. The agent plans to perform tasks necessary to support the user's daily life. The input is the artificial intelligence model from step 4, and the output is the constructed agent. 【0517】 Step 6: 【0518】 The server uses the constructed agent to propose an optimal lifestyle support schedule to the user via the terminal. The input is the agent data from step 5, and the output is specific schedule information. 【0519】 Step 7: 【0520】 The user reviews the suggested schedule on their terminal and makes any necessary modifications. The input is the schedule generated by the server, and the output is the schedule confirmed by the user. 【0521】 Step 8: 【0522】 The server continuously optimizes the agent by monitoring its performance in real time and adjusting the algorithm as needed. The input is the user usage data from step 7, and the output is the optimized agent behavior. 【0523】 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. 【0524】 The present invention relates to a system comprising a user, a server, a terminal, and an emotion engine. The user not only inputs work-related information and tasks through the terminal, but their emotional state is also recognized in real time by the emotion engine. The emotion engine analyzes emotional data from the user's voice tone, facial expressions, or nuances of text input when they input information into the terminal. 【0525】 The server stores business information, issues, and emotional data analyzed by the emotion engine, all submitted by the user, in a database. This data is then comprehensively analyzed to automatically define requirements that take into account the user's business needs and emotional state. Based on the requirements definition, the server selects the most suitable artificial intelligence model from the database library. The impact of the user's emotional state on business efficiency and problem-solving is a crucial factor in this selection process. 【0526】 The server builds an artificial intelligence agent based on a selected AI model, which has the ability to adaptively respond while considering the user's emotional state. This agent can not only efficiently accomplish business tasks but also adjust the interface and feedback according to the user's emotions. 【0527】 For example, if the emotion engine detects that a user is experiencing stress, the agent simplifies the human interface and provides clearer and more considerate instructions and feedback. The server monitors whether these emotion-based dynamic adjustments improve work efficiency and automatically improves the agent's settings as needed. 【0528】 By integrating with an emotion engine in this way, the system of the present invention considers not only the user's physical input information but also their emotional state, enabling more appropriate and effective business improvement. As a result, users can receive business support optimized for their individual circumstances. 【0529】 The following describes the processing flow. 【0530】 Step 1: 【0531】 Users log in to the system using a terminal and input their company's business information and challenges. The terminal sends the user's input to the server, and at the same time, the emotion engine detects changes in the user's tone of voice and facial expressions during input and analyzes the emotion data. 【0532】 Step 2: 【0533】 The server stores business information, tasks, and emotional data obtained by the emotion engine from the terminal into a database. Based on this information, the server automatically defines requirements that take into account the user's current emotional state. In this requirements definition process, natural language processing technology is used to analyze the input data and set conditions according to the request and emotional state. 【0534】 Step 3: 【0535】 The server selects the optimal model from the AI ​​model library in the database based on defined requirements. This selection process also takes into account how the user's emotional state influences the system's requirements. 【0536】 Step 4: 【0537】 The server automatically builds an AI agent based on the selected AI model, taking into account the user's emotional state to respond appropriately. This agent can flexibly adjust its interface and response content according to the emotional state. 【0538】 Step 5: 【0539】 The server sends initial setup information to the terminal to integrate the constructed artificial intelligence agent into the business process and guides the user through the setup procedure. The user follows the instructions to install the agent and begin operations according to the business flow. 【0540】 Step 6: 【0541】 The server monitors the operation and performance of the running artificial intelligence agent and analyzes performance data based on user sentiment data obtained from the emotion engine. If the agent is not working as expected or if improvements are needed, the server automatically adjusts and optimizes the agent's settings. 【0542】 (Example 2) 【0543】 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." 【0544】 Conventional information processing systems collect and analyze business information without considering the user's emotional state, which can lead to decreased work efficiency. Furthermore, the lack of interface adjustments based on user emotions is problematic, resulting in increased psychological burden on users. Additionally, there is a lack of effective methods for improving the performance of intelligent agents. 【0545】 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. 【0546】 In this invention, the server includes means for analyzing the user's emotional state and defining requirements, means for selecting the optimal information processing model, and means for constructing an intelligent agent and adjusting the interface according to the emotional state. This enables business support that takes the user's emotions into consideration and improves the performance of the intelligent agent effectively. 【0547】 "Information" refers to data and issues related to the work provided by the user. 【0548】 "Emotional state" refers to the user's psychological condition and is data analyzed from voice tone, facial expressions, and text. 【0549】 "Requirements definition" is the process of identifying user needs and problems based on collected information and emotional states, and clarifying solutions. 【0550】 An "information processing model" refers to the algorithm or analysis method best suited to the user's situation, and is selected from a library of options. 【0551】 An "intelligent agent" is a software entity built to assist users in their work and can adjust its interface as needed. 【0552】 An "interface" is a means or structure for exchanging information between a user and a system. 【0553】 "Performance" refers to the level of efficiency and effectiveness of the activities demonstrated by the intelligent agent. 【0554】 This invention is a system that operates by combining a user, a terminal, a server, and sentiment analysis software. The following describes in detail how each element is used. 【0555】 Users input work-related information and tasks using a terminal. The terminal is equipped with a voice input device and a camera, which simultaneously capture data on the user's voice tone and facial expressions. This data is transmitted in real time to emotion analysis software via the terminal's software. 【0556】 The emotion analysis software analyzes the user's emotional state by analyzing voice tone, facial expressions, and text nuances from the user's input and actions. This analysis result is sent to a server, which stores it in a database along with business information. 【0557】 Next, the server uses the collected information to define the requirements. In this process, the server selects a generative AI model from a library and determines the optimal information processing model. Based on this, the server builds an intelligent agent and adjusts the interface according to the user's emotional state. 【0558】 For example, if emotion analysis software determines that a user is experiencing stress based on the information they have entered, the server will provide a more intuitive user interface to help reduce the user's stress. In this case, an example of a prompt message could be given to the generative AI model such as, "If the user is experiencing stress, please provide an interface based on that." 【0559】 By implementing this system, users can improve the efficiency and comfort of their work. Server-based data analysis and the development of intelligent agents are crucial elements in providing support optimized for each user's individual situation. 【0560】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0561】 Step 1: 【0562】 Users input work information and tasks from a terminal. The devices used for input include a keyboard, microphone, and camera. This generates text data, audio data, and image data, which are then received by the terminal. The input data undergoes initial processing within the terminal, and the data format is standardized. 【0563】 Step 2: 【0564】 The device transmits the acquired audio and image data to emotion analysis software. The emotion analysis software analyzes the tone of the audio and the facial expressions in the images, and outputs emotional state data. This process is performed in real time, and the analysis results are obtained as a numerical representation of the emotional state. 【0565】 Step 3: 【0566】 The server receives business information and emotional state data transmitted from the terminal. The server stores this data in a database and automatically performs requirements definition by analyzing the business information. This analysis uses natural language processing and data mining techniques, and the output generates requirements that clearly define the user's needs and challenges. 【0567】 Step 4: 【0568】 The server searches the database library based on the requirements definition and selects the optimal generating AI model. This process uses an algorithm to evaluate the suitability of the AI ​​model, and the selected model is output. 【0569】 Step 5: 【0570】 The server constructs an intelligent agent based on the selected generative AI model. During this construction process, the AI ​​model is modified to reflect the user's emotional state, adjusting the interface and functions accordingly. As a result, an intelligent agent that responds to the user's emotions is output. 【0571】 Step 6: 【0572】 The intelligent agent built by the server provides feedback to the user's terminal. For example, if analysis indicates that the user is experiencing stress, the agent provides a simple and easy-to-understand interface and adjusts the instructions and feedback provided to the user. This allows the user to receive support that reduces their psychological burden. 【0573】 Step 7: 【0574】 The server monitors the performance of the intelligent agents and collects performance data in real time. This data is analyzed, and the agents' functions and interfaces are automatically improved as needed. This provides continuously optimized business support. 【0575】 (Application Example 2) 【0576】 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." 【0577】 There is a need to mitigate the impact of stress and emotional fluctuations experienced by users during work on work efficiency, and to provide appropriate support means to facilitate smoother work processes. Conventional systems do not adequately consider user emotions in their interactions, and work execution tends to be uniform and standardized. Therefore, it is necessary to develop a system that can respond flexibly according to the user's state. 【0578】 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. 【0579】 In this invention, the server includes means for analyzing the user's emotional state in real time and dynamically adjusting the interface, means for providing appropriate content to the user, and means for monitoring the performance of the artificial intelligence agent and automatically making improvements as needed. This enables the provision of interactions that respond to the user's emotions, improving operational efficiency and optimizing the user experience. 【0580】 An "artificial intelligence model" is a set of mathematical algorithms or algorithms designed for data analysis and decision-making. 【0581】 An "artificial intelligence agent" is a program or system that acts autonomously in specific tasks or problem-solving situations, optimizing the results according to the user's needs. 【0582】 "Dynamic interface adjustment" is the process of changing the configuration and information presented in the user interface in real time according to the user's emotional state and operating status. 【0583】 "Emotional state analysis" is information processing that infers a user's mental state and emotions from their voice tone, facial expressions, text input, etc. 【0584】 "Content provision" refers to the act of presenting users with digital materials and information tailored to their specific purposes, such as information, entertainment, and feedback. 【0585】 "Performance monitoring" is a process for continuously evaluating whether artificial intelligence agents and the entire system are functioning effectively and efficiently. 【0586】 "Automatic improvement" refers to a system or agent making adjustments to improve its performance and functionality based on the environment and user feedback. 【0587】 To implement this invention, the user inputs work-related information into the terminal through voice, facial expressions, and text input. The emotion engine installed in the terminal analyzes the user's emotional state in real time from these inputs. Specifically, changes in voice tone are analyzed by a voice recognition API, and facial expressions are determined by facial expression recognition software. Furthermore, the nuances of text input are processed by a natural language processing engine. 【0588】 The server stores business information, issues, and analyzed sentiment data submitted by users in a database. A standard database management system is used here, enabling efficient storage and retrieval of information. Subsequently, the server uses a generation AI model based on the stored data to automatically define requirements tailored to the user's emotional state and business needs. This is handled by the requirements definition engine. 【0589】 Based on the selected artificial intelligence model, the server builds an AI agent. This agent adapts to the user's real-time emotional state, dynamically adjusts the interface, and provides the user with appropriate content. The robot can display entertainment content to provide the user with a relaxing environment. User feedback is sent back to the server, which monitors the agent's performance and automatically makes improvements as needed. 【0590】 A concrete example of a use case is in the home. For instance, if a child feels stressed while doing homework, the robot can detect the stress and suggest, "Let's take a short break and listen to some fun music together," thereby refreshing the user's mood. 【0591】 An example of a prompt message might be, "Simulate how the user will react when they experience stress." 【0592】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0593】 Step 1: 【0594】 Users provide information through voice, facial expressions, and text input via their devices. This input includes work-related data and emotional states. The device passes this information to an emotion engine, which performs nuance analysis of voice tone, facial expressions, and text. The analysis results are output as emotion data. 【0595】 Step 2: 【0596】 The server stores business information and sentiment data transmitted from the terminal in a database. During storage, the data is structured and converted into a format that allows for easy searching and analysis. Inputs include user business information and analyzed sentiment data, while output is a record in the database. 【0597】 Step 3: 【0598】 The server automatically defines requirements using a generative AI model based on business information and emotional data stored in the database. The input is information from the database, and the output is the result of the requirements definition. In this process, the impact of the user's emotional state on business operations is analyzed, and an appropriate artificial intelligence model is selected. 【0599】 Step 4: 【0600】 The server uses the AI ​​model selected based on the requirements definition to build an AI agent. The agent prepares to adjust the interface in response to the user's emotional state. At this stage, the selected AI model is used as input, and the result of building the agent is output. 【0601】 Step 5: 【0602】 Using a constructed artificial intelligence agent, the server dynamically adjusts the interface and content provided to the user. Specifically, if the agent determines that the user is experiencing stress, it will provide relaxing music or entertainment content. The input is the user's real-time emotional data, and the output is the adjusted interface and the content provided. 【0603】 Step 6: 【0604】 The server monitors the performance of the artificial intelligence agent based on feedback and automatically makes improvements as needed. The input is user feedback data, and the output is the improved agent settings. This process involves real-time system adjustments to maximize operational efficiency and user satisfaction. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 [Fourth Embodiment] 【0609】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0610】 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. 【0611】 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). 【0612】 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. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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". 【0622】 The system of the present invention comprises a user, a server, and a terminal. The user logs into the system and provides work-related information and tasks to the server via the terminal. The server receives this information and stores it in a database. 【0623】 Next, the server analyzes the received information and automatically defines the requirements necessary for business improvement. In this process, natural language processing technology is used to extract important keywords from the user's input, and the requirements are identified based on these keywords. Based on the defined requirements, the server selects the optimal model from the artificial intelligence model library in the database. 【0624】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This agent has the capability to perform various functions to streamline the user's business processes. 【0625】 The constructed agent is deployed to the user via a terminal. The server assists with the initial setup of the agent and its integration into business processes, ensuring a smooth deployment. After deployment, the server monitors the agent's execution status in real time and analyzes its performance data. 【0626】 If the agent's performance falls below expectations, the server uses an automatic adjustment function to improve the agent's performance. This continuous monitoring and improvement ensures that users can always enjoy efficient and optimal work performance. 【0627】 For example, if a retail user wants to improve the efficiency of their inventory management, they might input issues such as "slow real-time processing of sales data." The server receives this information and builds an artificial intelligence agent to forecast inventory and make optimal orders. Once the agent is running, the user can understand the inventory situation in real time and know the appropriate timing for ordering. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0628】 The following describes the processing flow. 【0629】 Step 1: 【0630】 Users log in to the system using their terminals and input specific details about their company's business operations and current challenges. The terminals then send this information to the server. 【0631】 Step 2: 【0632】 The server stores business information and issues received from users in a database. During this process, the server uses natural language processing technology to extract important keywords and prepare them for analysis. 【0633】 Step 3: 【0634】 Based on the collected information, the server automatically defines the necessary functions and conditions for the artificial intelligence agent. This clarifies which models and algorithms are most suitable. 【0635】 Step 4: 【0636】 Based on the requirements definition, the server selects the most suitable model from the AI ​​model library in the database. Each model is evaluated from the perspectives of performance, cost, and scope of application to make the optimal selection. 【0637】 Step 5: 【0638】 Based on the selected artificial intelligence model, the server automatically builds an artificial intelligence agent. This process involves combining the necessary APIs and program code to form the agent. 【0639】 Step 6: 【0640】 The server sends initial setup information to the terminal to integrate the completed artificial intelligence agent into the business process and provides the user with setup guidelines. 【0641】 Step 7: 【0642】 Users follow the guidelines provided via their device, install the agent according to their own workflow, and begin operations. 【0643】 Step 8: 【0644】 The server monitors the performance of the running artificial intelligence agents in real time. It continuously analyzes the collected performance data to ensure that the system is being operated effectively. 【0645】 Step 9: 【0646】 Based on monitoring results, the server automatically adjusts agent settings and optimizes functionality if it determines that performance improvements are needed. Users receive reports of these improvements via their terminal. 【0647】 (Example 1) 【0648】 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". 【0649】 In today's business environment, effectively collecting user information and taking optimal measures based on that information is necessary to perform tasks quickly and efficiently. However, the process from information collection to analysis and implementation of appropriate solutions can be complex and time-consuming. Furthermore, the implemented solutions may not perform as expected, which is another challenge. This invention aims to solve these problems. 【0650】 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. 【0651】 In this invention, the server includes a device for collecting business-related information and issues from users, a device for analyzing the collected information and automatically defining requirements, and a device for selecting the optimal machine learning model based on the defined requirements. This enables the rapid construction and deployment of an optimal intelligent agent to improve the user's work efficiency. 【0652】 A "user" is an individual or organization that uses the system to provide information related to business operations and uses that information to improve those operations. 【0653】 "Business-related information" refers to the data and issues that users provide to the system in order to perform their own work. 【0654】 "Data collection device" refers to a technical means of acquiring information provided by the user and making it available within the system. 【0655】 "Analysis" refers to the process of clarifying requirements using data processing and algorithms based on collected information. 【0656】 "Requirements definition" refers to the process of clearly defining the conditions and functions necessary for business improvement. 【0657】 A "machine learning model" refers to an algorithm that learns from accumulated data and makes predictions and decisions based on those results. 【0658】 The "selection device" refers to a function within the system that selects the machine learning model best suited to the defined requirements. 【0659】 An "intelligent agent" refers to a program built on a selected machine learning model that automates and optimizes business processes. 【0660】 "The device to be constructed" refers to the technical means for generating intelligent agents based on the selected machine learning model. 【0661】 "Integration devices" refer to the technical support needed to integrate the constructed intelligent agent into existing business processes. 【0662】 A "device that monitors performance and automatically makes improvements as needed" refers to a technical means that continuously observes the activity of an intelligent agent and automatically adjusts it when performance falls below expectations. 【0663】 The system according to the present invention has a user, a server, and a terminal as its main components. The user uses the terminal to input information and issues related to their work and transmits them to the server. This information specifically includes problems and difficulties in improving the productivity and efficiency of their work. 【0664】 The server implements natural language processing technology using programming languages ​​such as Python to analyze information sent by the user. Possible toolkits to be used include NLTK and spaCy. Using these, important keywords are extracted from the user's input, and based on these, requirements necessary for business improvement are defined. 【0665】 Based on defined requirements, the server selects the optimal model from a library of machine learning models stored in the database. Frameworks such as TensorFlow and PyTorch are used for this process. Using the selected model, the server builds an intelligent agent. 【0666】 The developed intelligent agent is deployed to the user via a terminal. The server assists with the initial setup of the agent, ensuring smooth integration into business processes. After deployment, the server monitors the agent's performance in real time and optimizes it as needed. Using an auto-adjustment function, the agent's efficiency is constantly improved, allowing users to enjoy optimal work performance. 【0667】 A concrete example is when a retail user seeks to improve the efficiency of inventory management and inputs a problem such as "slow real-time processing of sales data." In response, the server builds an intelligent agent that performs inventory forecasting and optimal ordering. This agent processes sales data in real time, providing the user with the ability to instantly understand the inventory status. Throughout this process, the server constantly evaluates the agent's performance and optimizes it as needed. 【0668】 As an example of a prompt, one could input the text "Please suggest the optimal AI model for real-time inventory management" into the generating AI model. 【0669】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0670】 Step 1: 【0671】 Users input information and specific issues related to their work using a terminal. The input information is in text format and includes business problems such as "delays in real-time processing." This information is sent from the terminal to the server. The server then receives this input information and proceeds to the next analysis step. 【0672】 Step 2: 【0673】 The server analyzes the received information. Using natural language processing techniques, it extracts important keywords from the input text. Specifically, it uses NLTK and spaCy to identify keywords such as "real-time processing" and "delay." The keywords obtained through this analysis are output as data for requirements definition. 【0674】 Step 3: 【0675】 The server selects the optimal machine learning model based on the requirements obtained from the analysis. At this stage, it chooses the model best suited to the business requirements from the model libraries stored in TensorFlow or PyTorch. The selected model is output as input for subsequent agent construction. 【0676】 Step 4: 【0677】 The server builds an intelligent agent using a selected machine learning model. This process includes tuning the model parameters and installing software modules. The built agent is output as a program with functions to streamline business processes. 【0678】 Step 5: 【0679】 The server deploys intelligent agents to users via their terminals. During deployment, it supports initial agent setup and integration into the work environment. This distributes the agents to the user's terminal, making specific functions for improving work efficiency available. 【0680】 Step 6: 【0681】 The server monitors the performance of deployed agents in real time. This includes monitoring their execution status and evaluating their performance. If an agent fails to perform as expected, the server uses an automatic tuning function to readjust the data to improve the agent's performance. 【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 recent years, with the increasing diversification of lifestyles, there has been a growing demand for efficient support in individuals' daily lives and household chores. However, conventional systems often struggle to respond to individual needs in a detailed manner, resulting in users bearing a heavy burden. There is a need to solve this problem and realize flexible and efficient life support tailored to the individual needs of users. 【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 means for collecting information and issues related to the user's work, means for analyzing the collected information to automatically define requirements, and means for analyzing voice data, extracting important keywords, and proposing an optimal life support schedule to the user. This enables the user to smoothly receive optimal life support tailored to their individual needs. 【0687】 A "user" is an individual or organization that uses the system to provide information and receive support. 【0688】 "Information" refers to data and issues related to the user's work and daily life. 【0689】 "Analysis" is the process of identifying important elements and needs based on collected information and converting them into a format useful within the system. 【0690】 "Requirements definition" is the process of clarifying the goals and necessary functions that a system should achieve, derived from the analyzed information. 【0691】 An "artificial intelligence model" refers to a machine learning algorithm or data structure selected according to user needs and used to optimize specific business processes. 【0692】 An "artificial intelligence agent" is a dynamic program built on a selected artificial intelligence model to automate specific tasks or processes. 【0693】 The "life support process" refers to a series of actions and activities in which an artificial intelligence agent assists and streamlines the user's daily life and household chores. 【0694】 "Monitoring" is the process of continuously observing and recording the actions and performance of an agent. 【0695】 A "schedule" is a time and task management plan proposed by an artificial intelligence agent to help users efficiently carry out their activities and household chores. 【0696】 In the system that realizes this invention, a server plays a central role. The server first receives information about work and daily life from the user via a terminal. This information may be input as voice data and converted into text data using speech recognition software. At this time, the accuracy of voice input is improved by using the speech_recognition library. 【0697】 Next, the server utilizes natural language processing technology, using software called NLTK to analyze text data. This extracts important keywords from the collected information and identifies the user's individual needs and challenges. Based on the results of the language analysis, the server selects an appropriate artificial intelligence model using the scikit-learn library and builds an artificial intelligence agent. This agent has the ability to automate specific life support processes, such as managing household chore schedules. 【0698】 For example, if a user says to the system, "I can't finish cleaning," the server converts the audio data into text, extracts key keywords, and then suggests an optimal schedule based on them. This schedule might include specific action plans such as, "Vacuum at 3 PM and bring in the laundry at 4 PM." 【0699】 Allowing users to view this plan on their devices improves daily efficiency. Furthermore, the server constantly monitors the performance of the artificial intelligence agent and automatically improves the model using machine learning algorithms as needed. This ensures that users continuously receive optimal support. 【0700】 An example of a prompt message is: "Extract keywords related to the user's household chore support from the voice input and suggest an appropriate schedule." 【0701】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0702】 Step 1: 【0703】 The user uses the device's microphone to input voice data. The voice data is received and sent to the server. Here, the input is voice data, and the output is that raw voice data. 【0704】 Step 2: 【0705】 The server utilizes the speech_recognition library to convert received speech data into text data. The input is the speech data from step 1, and the output is the corresponding text data. This conversion is performed through the analysis of speech patterns. 【0706】 Step 3: 【0707】 The server uses the NLTK library to extract important keywords from text data. This process involves segmenting the text data, removing stop words, and identifying key words. The input is the text data from step 2, and the output is the extracted keywords. 【0708】 Step 4: 【0709】 The server uses the scikit-learn library to select an appropriate artificial intelligence model based on the extracted keywords. The input is the keywords obtained in step 3, and the output is the selected artificial intelligence model. Cluster analysis and fitting are performed during this process. 【0710】 Step 5: 【0711】 The server constructs an artificial intelligence agent using the selected artificial intelligence model. The agent plans to perform tasks necessary to support the user's daily life. The input is the artificial intelligence model from step 4, and the output is the constructed agent. 【0712】 Step 6: 【0713】 The server uses the constructed agent to propose an optimal lifestyle support schedule to the user via the terminal. The input is the agent data from step 5, and the output is specific schedule information. 【0714】 Step 7: 【0715】 The user reviews the suggested schedule on their terminal and makes any necessary modifications. The input is the schedule generated by the server, and the output is the schedule confirmed by the user. 【0716】 Step 8: 【0717】 The server continuously optimizes the agent by monitoring its performance in real time and adjusting the algorithm as needed. The input is the user usage data from step 7, and the output is the optimized agent behavior. 【0718】 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. 【0719】 The present invention relates to a system comprising a user, a server, a terminal, and an emotion engine. The user not only inputs work-related information and tasks through the terminal, but their emotional state is also recognized in real time by the emotion engine. The emotion engine analyzes emotional data from the user's voice tone, facial expressions, or nuances of text input when they input information into the terminal. 【0720】 The server stores business information, issues, and emotional data analyzed by the emotion engine, all submitted by the user, in a database. This data is then comprehensively analyzed to automatically define requirements that take into account the user's business needs and emotional state. Based on the requirements definition, the server selects the most suitable artificial intelligence model from the database library. The impact of the user's emotional state on business efficiency and problem-solving is a crucial factor in this selection process. 【0721】 The server builds an artificial intelligence agent based on a selected AI model, which has the ability to adaptively respond while considering the user's emotional state. This agent can not only efficiently accomplish business tasks but also adjust the interface and feedback according to the user's emotions. 【0722】 For example, if the emotion engine detects that a user is experiencing stress, the agent simplifies the human interface and provides clearer and more considerate instructions and feedback. The server monitors whether these emotion-based dynamic adjustments improve work efficiency and automatically improves the agent's settings as needed. 【0723】 By integrating with an emotion engine in this way, the system of the present invention considers not only the user's physical input information but also their emotional state, enabling more appropriate and effective business improvement. As a result, users can receive business support optimized for their individual circumstances. 【0724】 The following describes the processing flow. 【0725】 Step 1: 【0726】 Users log in to the system using a terminal and input their company's business information and challenges. The terminal sends the user's input to the server, and at the same time, the emotion engine detects changes in the user's tone of voice and facial expressions during input and analyzes the emotion data. 【0727】 Step 2: 【0728】 The server stores business information, tasks, and emotional data obtained by the emotion engine from the terminal into a database. Based on this information, the server automatically defines requirements that take into account the user's current emotional state. In this requirements definition process, natural language processing technology is used to analyze the input data and set conditions according to the request and emotional state. 【0729】 Step 3: 【0730】 The server selects the optimal model from the AI ​​model library in the database based on defined requirements. This selection process also takes into account how the user's emotional state influences the system's requirements. 【0731】 Step 4: 【0732】 The server automatically builds an AI agent based on the selected AI model, taking into account the user's emotional state to respond appropriately. This agent can flexibly adjust its interface and response content according to the emotional state. 【0733】 Step 5: 【0734】 The server sends initial setup information to the terminal to integrate the constructed artificial intelligence agent into the business process and guides the user through the setup procedure. The user follows the instructions to install the agent and begin operations according to the business flow. 【0735】 Step 6: 【0736】 The server monitors the operation and performance of the running artificial intelligence agent and analyzes performance data based on user sentiment data obtained from the emotion engine. If the agent is not working as expected or if improvements are needed, the server automatically adjusts and optimizes the agent's settings. 【0737】 (Example 2) 【0738】 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". 【0739】 Conventional information processing systems collect and analyze business information without considering the user's emotional state, which can lead to decreased work efficiency. Furthermore, the lack of interface adjustments based on user emotions is problematic, resulting in increased psychological burden on users. Additionally, there is a lack of effective methods for improving the performance of intelligent agents. 【0740】 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. 【0741】 In this invention, the server includes means for analyzing the user's emotional state and defining requirements, means for selecting the optimal information processing model, and means for constructing an intelligent agent and adjusting the interface according to the emotional state. This enables business support that takes the user's emotions into consideration and improves the performance of the intelligent agent effectively. 【0742】 "Information" refers to data and issues related to the work provided by the user. 【0743】 "Emotional state" refers to the user's psychological condition and is data analyzed from voice tone, facial expressions, and text. 【0744】 "Requirements definition" is the process of identifying user needs and problems based on collected information and emotional states, and clarifying solutions. 【0745】 An "information processing model" refers to the algorithm or analysis method best suited to the user's situation, and is selected from a library of options. 【0746】 An "intelligent agent" is a software entity built to assist users in their work and can adjust its interface as needed. 【0747】 An "interface" is a means or structure for exchanging information between a user and a system. 【0748】 "Performance" refers to the level of efficiency and effectiveness of the activities demonstrated by the intelligent agent. 【0749】 This invention is a system that operates by combining a user, a terminal, a server, and sentiment analysis software. The following describes in detail how each element is used. 【0750】 Users input work-related information and tasks using a terminal. The terminal is equipped with a voice input device and a camera, which simultaneously capture data on the user's voice tone and facial expressions. This data is transmitted in real time to emotion analysis software via the terminal's software. 【0751】 The emotion analysis software analyzes the user's emotional state by analyzing voice tone, facial expressions, and text nuances from the user's input and actions. This analysis result is sent to a server, which stores it in a database along with business information. 【0752】 Next, the server uses the collected information to define the requirements. In this process, the server selects a generative AI model from a library and determines the optimal information processing model. Based on this, the server builds an intelligent agent and adjusts the interface according to the user's emotional state. 【0753】 For example, if emotion analysis software determines that a user is experiencing stress based on the information they have entered, the server will provide a more intuitive user interface to help reduce the user's stress. In this case, an example of a prompt message could be given to the generative AI model such as, "If the user is experiencing stress, please provide an interface based on that." 【0754】 By implementing this system, users can improve the efficiency and comfort of their work. Server-based data analysis and the development of intelligent agents are crucial elements in providing support optimized for each user's individual situation. 【0755】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0756】 Step 1: 【0757】 Users input work information and tasks from a terminal. The devices used for input include a keyboard, microphone, and camera. This generates text data, audio data, and image data, which are then received by the terminal. The input data undergoes initial processing within the terminal, and the data format is standardized. 【0758】 Step 2: 【0759】 The device transmits the acquired audio and image data to emotion analysis software. The emotion analysis software analyzes the tone of the audio and the facial expressions in the images, and outputs emotional state data. This process is performed in real time, and the analysis results are obtained as a numerical representation of the emotional state. 【0760】 Step 3: 【0761】 The server receives business information and emotional state data transmitted from the terminal. The server stores this data in a database and automatically performs requirements definition by analyzing the business information. This analysis uses natural language processing and data mining techniques, and the output generates requirements that clearly define the user's needs and challenges. 【0762】 Step 4: 【0763】 The server searches the database library based on the requirements definition and selects the optimal generating AI model. This process uses an algorithm to evaluate the suitability of the AI ​​model, and the selected model is output. 【0764】 Step 5: 【0765】 The server constructs an intelligent agent based on the selected generative AI model. During this construction process, the AI ​​model is modified to reflect the user's emotional state, adjusting the interface and functions accordingly. As a result, an intelligent agent that responds to the user's emotions is output. 【0766】 Step 6: 【0767】 The intelligent agent built by the server provides feedback to the user's terminal. For example, if analysis indicates that the user is experiencing stress, the agent provides a simple and easy-to-understand interface and adjusts the instructions and feedback provided to the user. This allows the user to receive support that reduces their psychological burden. 【0768】 Step 7: 【0769】 The server monitors the performance of the intelligent agents and collects performance data in real time. This data is analyzed, and the agents' functions and interfaces are automatically improved as needed. This provides continuously optimized business support. 【0770】 (Application Example 2) 【0771】 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". 【0772】 There is a need to mitigate the impact of stress and emotional fluctuations experienced by users during work on work efficiency, and to provide appropriate support means to facilitate smoother work processes. Conventional systems do not adequately consider user emotions in their interactions, and work execution tends to be uniform and standardized. Therefore, it is necessary to develop a system that can respond flexibly according to the user's state. 【0773】 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. 【0774】 In this invention, the server includes means for analyzing the user's emotional state in real time and dynamically adjusting the interface, means for providing appropriate content to the user, and means for monitoring the performance of the artificial intelligence agent and automatically making improvements as needed. This enables the provision of interactions that respond to the user's emotions, improving operational efficiency and optimizing the user experience. 【0775】 An "artificial intelligence model" is a set of mathematical algorithms or algorithms designed for data analysis and decision-making. 【0776】 An "artificial intelligence agent" is a program or system that acts autonomously in specific tasks or problem-solving situations, optimizing the results according to the user's needs. 【0777】 "Dynamic interface adjustment" is the process of changing the configuration and information presented in the user interface in real time according to the user's emotional state and operating status. 【0778】 "Emotional state analysis" is information processing that infers a user's mental state and emotions from their voice tone, facial expressions, text input, etc. 【0779】 "Content provision" refers to the act of presenting users with digital materials and information tailored to their specific purposes, such as information, entertainment, and feedback. 【0780】 "Performance monitoring" is a process for continuously evaluating whether artificial intelligence agents and the entire system are functioning effectively and efficiently. 【0781】 "Automatic improvement" refers to a system or agent making adjustments to improve its performance and functionality based on the environment and user feedback. 【0782】 To implement this invention, the user inputs work-related information into the terminal through voice, facial expressions, and text input. The emotion engine installed in the terminal analyzes the user's emotional state in real time from these inputs. Specifically, changes in voice tone are analyzed by a voice recognition API, and facial expressions are determined by facial expression recognition software. Furthermore, the nuances of text input are processed by a natural language processing engine. 【0783】 The server stores business information, issues, and analyzed sentiment data submitted by users in a database. A standard database management system is used here, enabling efficient storage and retrieval of information. Subsequently, the server uses a generation AI model based on the stored data to automatically define requirements tailored to the user's emotional state and business needs. This is handled by the requirements definition engine. 【0784】 Based on the selected artificial intelligence model, the server builds an AI agent. This agent adapts to the user's real-time emotional state, dynamically adjusts the interface, and provides the user with appropriate content. The robot can display entertainment content to provide the user with a relaxing environment. User feedback is sent back to the server, which monitors the agent's performance and automatically makes improvements as needed. 【0785】 A concrete example of a use case is in the home. For instance, if a child feels stressed while doing homework, the robot can detect the stress and suggest, "Let's take a short break and listen to some fun music together," thereby refreshing the user's mood. 【0786】 An example of a prompt message might be, "Simulate how the user will react when they experience stress." 【0787】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0788】 Step 1: 【0789】 Users provide information through voice, facial expressions, and text input via their devices. This input includes work-related data and emotional states. The device passes this information to an emotion engine, which performs nuance analysis of voice tone, facial expressions, and text. The analysis results are output as emotion data. 【0790】 Step 2: 【0791】 The server stores business information and sentiment data transmitted from the terminal in a database. During storage, the data is structured and converted into a format that allows for easy searching and analysis. Inputs include user business information and analyzed sentiment data, while output is a record in the database. 【0792】 Step 3: 【0793】 The server automatically defines requirements using a generative AI model based on business information and emotional data stored in the database. The input is information from the database, and the output is the result of the requirements definition. In this process, the impact of the user's emotional state on business operations is analyzed, and an appropriate artificial intelligence model is selected. 【0794】 Step 4: 【0795】 The server uses the AI ​​model selected based on the requirements definition to build an AI agent. The agent prepares to adjust the interface in response to the user's emotional state. At this stage, the selected AI model is used as input, and the result of building the agent is output. 【0796】 Step 5: 【0797】 Using a constructed artificial intelligence agent, the server dynamically adjusts the interface and content provided to the user. Specifically, if the agent determines that the user is experiencing stress, it will provide relaxing music or entertainment content. The input is the user's real-time emotional data, and the output is the adjusted interface and the content provided. 【0798】 Step 6: 【0799】 The server monitors the performance of the artificial intelligence agent based on feedback and automatically makes improvements as needed. The input is user feedback data, and the output is the improved agent settings. This process involves real-time system adjustments to maximize operational efficiency and user satisfaction. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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." 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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 to be incorporated by reference. 【0821】 The following is further disclosed regarding the embodiments described above. 【0822】 (Claim 1) 【0823】 Means for collecting information and issues related to business from users, 【0824】 A method for automatically defining requirements by analyzing collected information, 【0825】 A means of selecting the optimal artificial intelligence model based on the requirements definition, 【0826】 A means of constructing an artificial intelligence agent based on a selected artificial intelligence model, 【0827】 A means of integrating the constructed artificial intelligence agent into a company's business processes, 【0828】 A means to monitor the performance of an artificial intelligence agent and automatically make improvements as needed, 【0829】 A system that includes this. 【0830】 (Claim 2) 【0831】 The system according to claim 1, comprising means for extracting important keywords from information collected from users and defining requirements based on those keywords. 【0832】 (Claim 3) 【0833】 The system according to claim 1, comprising means for analyzing performance data obtained through monitoring and automatically making adjustments to optimize the operation of an artificial intelligence agent. 【0834】 "Example 1" 【0835】 (Claim 1) 【0836】 A device for collecting information and issues related to business operations from users, 【0837】 A device that automatically performs requirements definition by analyzing collected information, 【0838】 A device for selecting the optimal machine learning model based on requirements definition, 【0839】 A device that constructs an intelligent agent based on a selected machine learning model, 【0840】 A device for integrating the constructed intelligent agent into the organization's business processes, 【0841】 A device that monitors the performance of intelligent agents and automatically makes improvements as needed, 【0842】 A system that includes this. 【0843】 (Claim 2) 【0844】 The system according to claim 1, comprising a device that extracts key keywords from information collected from users and defines requirements based on those keywords. 【0845】 (Claim 3) 【0846】 The system according to claim 1, comprising a device that analyzes performance data obtained through monitoring and automatically adjusts to optimize the operation of an intelligent agent. 【0847】 "Application Example 1" 【0848】 (Claim 1) 【0849】 Means for collecting information and issues related to business from users, 【0850】 A method for automatically defining requirements by analyzing collected information, 【0851】 A means of selecting the optimal artificial intelligence model based on the requirements definition, 【0852】 A means of constructing an artificial intelligence agent based on a selected artificial intelligence model, 【0853】 A means of integrating the constructed artificial intelligence agent into human life support processes, 【0854】 A means to monitor the performance of an artificial intelligence agent and automatically make improvements as needed, 【0855】 A method for analyzing voice data, extracting important keywords, and proposing an optimal lifestyle support schedule to the user, 【0856】 A system that includes this. 【0857】 (Claim 2) 【0858】 The system according to claim 1, comprising means for extracting important keywords from information collected from users and defining requirements based on those keywords. 【0859】 (Claim 3) 【0860】 The system according to claim 1, comprising means for analyzing performance data obtained through monitoring and automatically making adjustments to optimize the operation of an artificial intelligence agent. 【0861】 "Example 2 of combining an emotion engine" 【0862】 (Claim 1) 【0863】 A device that collects information from users, 【0864】 A device that automatically defines requirements by analyzing collected information and the emotional state of users, 【0865】 A device that selects the optimal information processing model based on the requirements definition, 【0866】 A device that constructs an intelligent agent based on a selected information processing model, 【0867】 A device in which the constructed intelligent agent adjusts the interface according to the user's emotional state, 【0868】 A device that monitors the performance of intelligent agents and automatically improves their effectiveness, 【0869】 A system that includes this. 【0870】 (Claim 2) 【0871】 The system according to claim 1, which includes a device for comprehensively analyzing information and emotional states collected from users and defining requirements. 【0872】 (Claim 3) 【0873】 The system according to claim 1, comprising a device that analyzes performance data obtained through monitoring and automatically adjusts to optimize the operation of an intelligent agent. 【0874】 "Application example 2 when combining with an emotional engine" 【0875】 (Claim 1) 【0876】 Means for collecting information and issues related to business from users, 【0877】 A method for automatically defining requirements by analyzing collected information, 【0878】 A means of selecting the optimal artificial intelligence model based on the requirements definition, 【0879】 A means of constructing an artificial intelligence agent based on a selected artificial intelligence model, 【0880】 A means of integrating the constructed artificial intelligence agent into the business procedures of society, 【0881】 A means to monitor the performance of artificial intelligence agents and automatically make improvements as needed, 【0882】 A means of analyzing the user's emotional state in real time and dynamically adjusting the interface, 【0883】 Means for providing appropriate content to users, 【0884】 A system that includes this. 【0885】 (Claim 2) 【0886】 The system according to claim 1, comprising means for extracting important patterns from the emotional state of a user and defining requirements based on those patterns. 【0887】 (Claim 3) 【0888】 The system according to claim 1, comprising means for analyzing emotional data obtained through monitoring and automatically making adjustments to optimize the operation of an artificial intelligence agent. [Explanation of symbols] 【0889】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Means for collecting information and issues related to business from users, A method for automatically defining requirements by analyzing collected information, A means of selecting the optimal artificial intelligence model based on the requirements definition, A means of constructing an artificial intelligence agent based on a selected artificial intelligence model, Methods for integrating the constructed artificial intelligence agent into a company's business processes, A means to monitor the performance of an artificial intelligence agent and automatically make improvements as needed, A system that includes this. [Claim 2] The system according to claim 1, which includes means for extracting important keywords from information collected from users and defining requirements based on those keywords. [Claim 3] The system according to claim 1, comprising means for analyzing performance data obtained through monitoring and automatically making adjustments to optimize the operation of the artificial intelligence agent.

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