system

Abstract

We provide the system. [Solution] A data receiving method for monitoring application status in real time, An analytical means for analyzing received data and calculating trends in the number of applications, A notification method that generates an alert when the number of applications falls below a predetermined target value, A proposal method for suggesting measures to increase the number of applications, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In enterprises and organizations, it is required to grasp the application situation and respond quickly to achieve the goals. However, data analysis and report creation for this purpose may take a great deal of effort and time. In particular, when the target number of applications is not reached, it is difficult to promptly formulate appropriate measures, resulting in problems such as effective management and marketing activities being hindered. 【Means for Solving the Problems】 【0005】 This invention provides a system for monitoring application status in real time and analyzing received application data. The system includes a means for immediately generating an alert and notifying relevant personnel if the number of applications falls below a set target. Furthermore, it supports efficient and effective management and marketing activities by incorporating suggestions for measures useful in increasing the number of applications, such as reward incentives and advertising content. 【0006】 A "data receiving means" is a function that receives application information transmitted from an external source, converts it into a format that can be processed within the system, and stores it there. 【0007】 "Analysis means" refers to a function that analyzes received application data based on statistical methods to reveal trends and patterns in the number of applications. 【0008】 The "notification method" is a function that sends an alert to specific administrators or stakeholders when the set application target value falls below a certain level. 【0009】 The "proposal tool" is a function that automatically devises and proposes feasible measures to increase the number of applications. 【0010】 An "alert" is a warning message that informs you that the number of applications is insufficient to meet your target and prompts you to take immediate action. 【0011】 A "reward system" is a plan of benefits or incentives offered to applicants with the aim of encouraging them to apply. 【0012】 "Advertising content" refers to promotional materials and information used to increase applications, and is distributed through online and offline media. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple 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】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 The system of the present invention includes data receiving means, analysis means, notification means, and suggestion means, and these functions enable efficient management of application status. The details of each means and how they function are described below. 【0035】 First, regarding the data reception method, the system functions by having the server receive application data over the internet. This data includes the applicant's ID, application date and time, and application details. The received data is immediately stored in the database and becomes accessible in real time. 【0036】 Next, the analysis method involves the server analyzing the application data in the database using statistical methods. Specifically, it aggregates the daily number of applications and calculates the trends. By identifying the average number of applications over the past few days and the daily increase / decrease patterns, it is possible to visualize the overall picture of the application situation. 【0037】 Based on the analysis results, the server generates an alert if the number of applications falls below the set target or if a sudden decrease in the number of applications is detected. This alert is sent to the terminals of the person in charge or administrator via email or in-system notification. Users who receive the notification can take prompt action. 【0038】 Furthermore, the suggestion system automatically generates measures to increase the number of applications. Examples of measures include promotional posts on social media, enhanced reward plans for applicants, and targeted marketing using email newsletters. These suggestions are implemented after user confirmation, and the actual results are fed back into the system, enabling even more accurate suggestions. 【0039】 For example, if analysis reveals that the number of applications for a campaign falls significantly below the target within three days of its launch, the notification system sends an alert to the administrator's terminal. Simultaneously, the suggestion system presents specific improvement plans, such as strengthening campaign advertising on social media or adding early application bonuses. In this way, users can quickly take action and increase the number of applications. 【0040】 By implementing this invention, companies and organizations can smoothly monitor application status in real time, understand the degree of goal achievement, and conduct effective promotional activities. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The server receives the application data. This data consists of information entered by users via online forms and includes the applicant's personal information, application date and time, application details, and number of applications. The server receives this data in real time and stores it in a database. 【0044】 Step 2: 【0045】 The server periodically analyzes the application data stored in the database. Using the analysis tools, the server aggregates the daily number of applications and calculates the average of the most recent number of applications. Based on this information, it detects trends and anomalies in the number of applications. 【0046】 Step 3: 【0047】 The server compares the target number of applications with the current number of applications and generates an alert via a notification system if the number of applications falls below the target. The alert is sent to the responsible person's terminal via email or in-system notification. The alert includes a message stating that the number of applications has not reached the target and that immediate action is required. 【0048】 Step 4: 【0049】 The user (person in charge) checks the alert on their terminal and understands the situation. The alert includes detailed information such as the current number of applications and completion rate, so the user can immediately understand the urgency of the situation. 【0050】 Step 5: 【0051】 The server utilizes suggestion tools to automatically generate measures to increase the number of applications. Specific measures include social media posting campaigns, customer reminders via email newsletters, and the introduction of special offer plans. This information is presented on the user's device, and the user awaits their decision. 【0052】 Step 6: 【0053】 The user reviews the proposed measures provided by the server and decides whether to implement them as appropriate. The selected measures are then executed by the user, and the results are fed back to the server. Based on this feedback, the server uses the data to further improve the effectiveness of future measures. 【0054】 (Example 1) 【0055】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0056】 Conventional application management systems have difficulty with real-time monitoring of application data and rapid response, and have been particularly challenging in taking appropriate measures when the number of applications decreases. Furthermore, insufficient proposals for measures aimed at increasing the number of applications have not led to effective applicant recruitment activities. 【0057】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0058】 In this invention, the server includes means for acquiring application status via an information processing device and storing it in a data storage area; means for analyzing the stored information using statistical methods to show trends in the number of applications; means for generating warning information when the results deviate from set criteria; means for transmitting the warning information to an administrator terminal via communication means; and means for making suggestions using an artificial intelligence model that generates measures to improve the number of applications. This enables real-time monitoring of application status, rapid implementation of countermeasures, and proposal of effective measures to increase the number of applications. 【0059】 "Application status" refers to information that shows the state and changes in data submitted by applicants, including the number of applications and trends. 【0060】 An "information processing device" is a mechanical device or system used for collecting, storing, and analyzing application data. 【0061】 The "data storage area" is a digital storage system for storing and securely keeping received application data. 【0062】 "Statistical methods" are mathematical and computational techniques used in data analysis, and are methods for revealing trends and characteristics of data. 【0063】 "Warning information" refers to a cautionary message generated when the system detects an abnormality or deviation from the configured criteria. 【0064】 "Communication means" refers to methods and infrastructure for transmitting information to other terminals or systems. 【0065】 An "artificial intelligence model" is a computer program that analyzes diverse data to make decisions, predictions, and suggestions. 【0066】 A "policy" is a specific action plan or method implemented to achieve a particular objective. 【0067】 An "administrator terminal" is an electronic device used for monitoring and managing a system. 【0068】 This invention provides a system for efficiently managing application status. The system consists of a server as an information processing device, a terminal that receives operations from an administrator, and a user that implements measures to increase the number of applications. 【0069】 The server receives data from applicants in real time and stores it in the data storage area. This includes data entered via application forms over the internet, as well as data received via email and APIs. The received data is stored in database software such as MySQL® or PostgreSQL. 【0070】 The server uses statistical analysis software such as Python's Pandas, NumPy, or the R language to analyze the data. This allows it to calculate the daily number of applications and their trends, and to understand the trends when the number of applications increases or decreases sharply. 【0071】 The server generates a warning if the analysis results deviate from the set criteria. This warning is sent to the administrator's terminal via email or the system's notification function. This allows the administrator to take prompt action according to the application status. 【0072】 Furthermore, the server utilizes a generative AI model to automatically generate specific measures to increase the number of applications. This prompt generation employs natural language processing techniques, which are widely used in AI models. Specific examples of proposed measures include strengthening advertising on social media and offering incentives to applicants. 【0073】 Based on this information, users allow administrators to review and implement suggestions. The results of the implemented measures are then fed back into the system, leading to improvements in the accuracy of the AI ​​model. 【0074】 For example, if the number of entries for a campaign falls below the target, the server will generate a prompt message such as, "The number of entries is below the target. To increase the number of entries, strengthen your social media campaign and offer rewards to early entrants." Upon receiving this information, the administrator can quickly implement countermeasures to improve the number of entries. 【0075】 By implementing this invention, companies and organizations will be able to smoothly monitor application status in real time, respond quickly, and implement effective promotional strategies. 【0076】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0077】 Step 1: 【0078】 The server receives application data from applicants in real time via the internet. The received data includes the applicant's ID, application date and time, and application details. The server immediately stores this data in its data storage area and saves the reception results in a database. 【0079】 Step 2: 【0080】 The server periodically analyzes current application data using statistical methods. Specifically, the server retrieves application data from the database and aggregates the daily number of applications using the Python Pandas library. As output of this analysis, it calculates trends in the number of applications and the average number of applications, and generates analysis results for further use. 【0081】 Step 3: 【0082】 Based on the analysis results, the server compares the number of applications against the set target value. If the number of applications falls below the target, the server generates a warning. This warning is output as an alert message and sent to the administrator's terminal via the email system. It is also distributed as an in-system notification. 【0083】 Step 4: 【0084】 The server uses the submitted analysis results and warning information to propose measures to increase the number of applications using an AI model. Specifically, the AI ​​model automatically generates measures such as strengthening promotion on social media and offering early application incentives. These proposals are sent from the server to the user's terminal as prompt messages. 【0085】 Step 5: 【0086】 The user reviews the suggestions received from the server, selects an appropriate measure, and implements it. The selected measure is executed, and the results are fed back to the server. This feedback data is stored to help improve the accuracy of subsequent suggestion generation. 【0087】 (Application Example 1) 【0088】 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." 【0089】 When conducting advertising campaigns, there are challenges in monitoring application status in real time and quickly formulating strategies to efficiently increase the number of applications. In particular, it is necessary to properly visualize the effectiveness of advertising and take immediate and effective countermeasures, but current methods are time-consuming in terms of information gathering and analysis, resulting in a reactive approach. 【0090】 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. 【0091】 In this invention, the server includes data receiving means for monitoring application status in real time, analysis means for analyzing the received data and calculating trends in the number of applications, and proposal means for visualizing advertising effectiveness and suggesting effective measures. This makes it possible to immediately grasp the effectiveness of advertising campaigns and take quick and appropriate countermeasures. 【0092】 "Data receiving means" refers to a means of monitoring application status in real time and acquiring application data. 【0093】 "Analysis means" refers to the means used to analyze received application data and calculate trends in the number of applications. 【0094】 "Notification method" refers to a means of generating an alert based on the analysis results when the number of applications falls below a set target value, and sending that alert via email or in-app notification. 【0095】 "Proposal methods" refer to means of visualizing advertising effectiveness and proposing effective measures to increase the number of applications. 【0096】 The system that implements this application is server-centric and uses the following hardware and software. The server runs a data analysis program written in Python and utilizes a web server framework using either Django or Flask. SQLite or PostgreSQL is used for the database for data storage and management. 【0097】 As a means of receiving data, the server retrieves application data via the internet and immediately saves it to the database. This data includes the application ID, date and time, and application details. 【0098】 The analysis method involves the server using statistical methods to analyze application data and understand trends in the number of applications. Specifically, it aggregates the number of applications, calculates daily increase / decrease patterns, and visualizes this data to facilitate an understanding of the overall application situation. 【0099】 The notification system uses server-generated analysis results to send alerts via email or in-app notifications if the number of applications falls below a set target value. This allows staff and administrators to quickly check the situation and take necessary actions. 【0100】 The suggestion system automatically generates measures to increase advertising effectiveness. These include targeted advertising to specific groups, revisions to the reward system, and strengthening campaigns on social media. The suggested measures are implemented after user confirmation, and the subsequent effects are fed back to improve the accuracy of the suggestions. 【0101】 For example, if the number of applications for a product suddenly drops during a promotional campaign, the server will send a notification stating, "The current click-through rate is 50% of the target. We recommend strengthening your targeted advertising on social media and increasing the incentives." In this way, users can quickly take action and improve the effectiveness of the advertising. 【0102】 An example of a prompt message could be, "Please come up with specific suggestions to improve the effectiveness of the new product campaign." 【0103】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0104】 Step 1: 【0105】 The server retrieves application data via the internet. Inputs include the applicant's ID, application date and time, and application details. This data is immediately saved to the database. Specifically, the server periodically calls an API or receives webhooks to receive data in real time. 【0106】 Step 2: 【0107】 The server analyzes application data stored in the database to calculate trends in the number of applications. It uses stored historical data as input. The output includes aggregated application numbers and patterns of increase / decrease. Specifically, it executes a script written in Python and processes the data using statistical libraries. 【0108】 Step 3: 【0109】 The server checks whether the number of applications is below the target based on the analysis results. It uses the analysis results and pre-set target values ​​as input. The output determines whether an alert needs to be generated. Specifically, it performs an automatic comparison using a threshold check algorithm. 【0110】 Step 4: 【0111】 The server sends alerts via notification methods as needed. Inputs include whether an alert should be generated and recipient information. Outputs include email and in-app notifications. Specifically, this involves sending emails using the SMTP protocol or utilizing a push notification service. 【0112】 Step 5: 【0113】 The server generates measures to increase the number of applications using the proposed methods. Past application data, market data, and analysis results are used as input. Specific proposed measures are generated as output. Specifically, the generation task is performed using an AI model, and the proposed content is refined using natural language processing technology. 【0114】 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. 【0115】 This invention provides a system for more effectively increasing the number of applications by monitoring application status in real time and analyzing user sentiment. This system includes data receiving means, analysis means, notification means, suggestion means, and sentiment engine. 【0116】 First, in the data reception process, the server receives application data via the internet. The received data is stored in a database by the server, accumulating information about each applicant. This allows for the management of applicants' behavioral history and past data. 【0117】 The analysis system has a function that allows the server to analyze application data in the database and calculate trends in the number of applications. This makes it possible to understand increases and decreases in the number of applications and detect abnormal fluctuations. In addition, the analysis results are generated as a report, allowing users to easily understand the application status. 【0118】 The notification system detects that the number of applications has not reached the target and sends an alert to the user's device. The alert is communicated via email or system notification, prompting the user to take prompt action. 【0119】 Furthermore, the proposed method automatically generates measures to increase the number of applications. These measures include a variety of strategies (for example, strengthening social media campaigns or proposing incentive plans) to facilitate the application process. 【0120】 The emotion engine is used to analyze the user's emotional state. The server takes in data from the user's past feedback and current behavior, and uses this to infer the user's emotions. This information is reflected in the suggested actions, providing the user with customized measures based on their emotions. 【0121】 For example, if the number of applications for a certain campaign falls below the target, the emotion engine analyzes user responses and, for instance, detects that users are "dissatisfied with the current application process." In this way, it can improve the number of applications through a more personalized approach. 【0122】 This embodiment allows companies to efficiently manage application status and implement flexible and effective measures that respond to user emotions. This is expected to improve the efficiency of application activities and increase participant satisfaction. 【0123】 The following describes the processing flow. 【0124】 Step 1: 【0125】 The server receives application data in real time. Applicants enter their application information using an online form, and this information is sent to the server via the internet. The server converts the received data into an easily understandable format and stores it in a database. 【0126】 Step 2: 【0127】 The server performs analysis based on existing application data. Daily or at other specified frequencies, the server aggregates application numbers and calculates averages, using the results to understand trends in application growth and decline. These analysis results are displayed on the terminal as a management dashboard. 【0128】 Step 3: 【0129】 The server compares the analysis results with the set application target. If the number of applications falls below the target, the server generates an alert via a notification system. This alert is sent to the responsible person's terminal via email or system notification. 【0130】 Step 4: 【0131】 Users who receive an alert on their device can review its contents. The alert contains detailed information about the current application status and any pressing issues, allowing users to quickly consider countermeasures based on that information. 【0132】 Step 5: 【0133】 The server uses an emotion engine to analyze user and applicant sentiment data. Specifically, it infers the applicant's emotional state based on data obtained from past feedback and current behavior. Sentiment categories include satisfaction and dissatisfaction with the application process. 【0134】 Step 6: 【0135】 The server generates strategies to increase the number of applications through suggestion mechanisms. This includes customized suggestions based on user sentiment data. For example, if the sentiment engine provides feedback indicating low satisfaction, it will suggest improving usability as a way to improve the application process. 【0136】 Step 7: 【0137】 The user reviews the proposed measures via their device and decides which measures to implement. The selected measures are then implemented by the user, and the results are fed back to the server for further analysis. 【0138】 These steps allow the system to maintain high application efficiency while flexibly responding to user emotions. 【0139】 (Example 2) 【0140】 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". 【0141】 In today's information-driven society, effectively increasing participation in application campaigns and events is a crucial challenge for many companies. However, tracking participant application status in real time and taking immediate, appropriate measures is technically and operationally difficult. Furthermore, implementing uniform measures without considering applicants' emotions and reactions can actually decrease participant satisfaction. A system is needed to address these challenges and improve the efficiency of application activities while enhancing the participant experience. 【0142】 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. 【0143】 In this invention, the server includes information processing means for monitoring the application status in real time, information analysis means for analyzing received information and calculating trends in the number of applications, information provision means for issuing a warning when the number of applications falls below a set standard, information generation means for suggesting measures to increase the number of applications, and sentiment analysis means for predicting users' emotions based on past opinions and behavioral data. This enables precise management of the application status and the implementation of flexible and effective measures based on the emotions of participants. 【0144】 "Information processing means" refers to system functions for monitoring application status in real time and effectively collecting necessary information. 【0145】 "Information analysis means" refers to a function that analyzes received data and calculates trends and patterns in the number of applications. 【0146】 The "information provision method" is a function that generates a warning when the number of applications falls below a set standard. 【0147】 "Information generation means" refers to a function that automatically suggests appropriate measures to increase the number of applications. 【0148】 "Emotional analysis tools" are functions that predict and analyze the emotions of applicants and users based on past feedback and behavioral data. 【0149】 The system of the present invention is a multi-functional platform centered around a server, for performing information processing, information analysis, information provision, information generation, and sentiment analysis. 【0150】 The server receives application data via the internet and stores it in a database in real time. The server uses high-performance database management software to efficiently process large amounts of data. Furthermore, dedicated analysis software is used for data analysis, quickly calculating trends in the number of applications and applicant behavior patterns. 【0151】 Based on the information provision mechanism, the server will generate warnings as needed, according to the configured criteria. For example, if the number of applications falls below 50% of the target, an alert will be generated and a notification will be sent to the user's device via email. This allows the user to immediately understand the situation and take appropriate countermeasures. 【0152】 The information generation method utilizes a generation AI model to automatically generate strategies to increase the number of applications. These strategies include specific measures such as strengthening social media campaigns and updating reward plans. These strategies are presented to users via their devices, guiding them as actionable options. 【0153】 Regarding sentiment analysis, a model is run on the server to predict user emotions based on past feedback and behavioral data. This model analyzes applicants' emotional responses and helps generate customized strategies. For example, if a user posts a complaint about the current application process, the server will suggest process improvements based on that content. 【0154】 An example of a prompt used as input to the generating AI model is a specific instruction such as, "Generate suggestions for new measures for a campaign that has seen a decline in applications." This allows users to quickly implement more accurate and effective measures. 【0155】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0156】 Step 1: 【0157】 The server receives application data via the internet. The received data includes the applicant's name, contact information, and application details. This data is stored in a database to aggregate the application information. The stored data forms the basis for future analysis and reporting. Furthermore, this information is used to create an application behavior history. 【0158】 Step 2: 【0159】 The server analyzes the application information in the database. The input is the entire contents of the database. The server uses statistical analysis algorithms to calculate trends in the number of applications and graphs the trends. It also compares the current data with past data to identify outliers and sudden fluctuations. The output is a report organized to allow the user to visually understand the application status. 【0160】 Step 3: 【0161】 The server generates a warning based on the analysis results. If the number of applications falls below the set threshold, the server receives the number of applications compared to the threshold as input. Based on this, it outputs an alert. Specifically, the alert is sent to the user's terminal as an email or system notification and includes content such as, "The number of applications is below expectations. Please check the details." 【0162】 Step 4: 【0163】 The server generates strategies to increase the number of applications. Past success stories and a generating AI model are provided as input. Based on this, specific measures to improve the number of applications are output. Examples include social media campaigns utilizing specific cultural events and incentive plans for new participants. 【0164】 Step 5: 【0165】 The server performs sentiment analysis. It uses the user's past feedback and current behavioral patterns as input. It applies an algorithm to infer emotional states and generates an action plan as a guide for participants. This makes it possible to provide personalized application promotion methods tailored to the user's emotions. 【0166】 (Application Example 2) 【0167】 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 device 14 will be referred to as the "terminal." 【0168】 In today's information-driven society, increasing the number of applicants for advertising campaigns and various recruitment activities is a crucial challenge. However, there is a lack of systems that can monitor application status in real time and implement effective countermeasures. Furthermore, traditional methods have made it difficult to accurately understand applicants' emotions and implement measures based on them. Therefore, there is a need for a system that can grasp trends in application numbers and propose quick and effective measures based on the emotional state of applicants. 【0169】 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. 【0170】 In this invention, the server includes an information receiving means for monitoring the application status in real time, an analysis means for analyzing the received information and calculating the trend in the number of applications, and an estimation engine means for analyzing emotional states and proposing measures. This enables the development of flexible and effective measures that respond to the emotions of applicants. 【0171】 "Information receiving means" refers to a system for monitoring application status in real time via the internet and collecting application data. 【0172】 An "analysis tool" is a system that analyzes received information and has the function of calculating trends and abnormal fluctuations in the number of applications. 【0173】 A "notification method" is a system that generates an alert and notifies users via electronic communication if the number of applications falls below a predetermined target value. 【0174】 The "estimation engine" is a mechanism for analyzing the emotional state of applicants and providing the information necessary when proposing measures. 【0175】 A "proposal tool" is a system that generates measures to increase the number of applications and presents appropriate solutions to users. 【0176】 The system of this invention is based on technology that aims to increase the number of applications by managing the application status and analyzing user sentiment. The server first receives application data in real time via the internet using an "information receiving means." This received data is stored in a database. The server analyzes the information in the database using an "analysis means" to calculate the trend in the number of applications and understand fluctuations. If the number of applications falls below the target number, an alert is sent to the user's terminal using a "notification means." The user receives this alert and is prompted to take prompt action. 【0177】 Furthermore, the server analyzes the user's emotional state using an "estimation engine." For this purpose, the server uses sentiment analysis software such as Google Cloud Natural Language API. The server collects user feedback, infers their emotions, and incorporates this into its "suggestion tools" to help propose measures to increase the number of applications. Specific measures include strengthening social media campaigns and offering incentives. 【0178】 For example, if an advertising campaign is failing to meet its target number of entries, the sentiment analysis engine might detect from user feedback that users are "dissatisfied with the current campaign." This information is then analyzed by the server, and suggestions for revising the campaign content are made as needed. 【0179】 An example of a prompt message generated using an AI model is: "The number of entries in the advertising campaign is below the target. Please conduct a sentiment analysis based on user feedback and propose measures to increase the number of entries." 【0180】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0181】 Step 1: 【0182】 The server receives information that monitors the application status in real time. Data from applicants is sent to the server via the internet and stored in the database. This input data includes applicant information and the date and time of application. 【0183】 Step 2: 【0184】 The server analyzes the information stored in the database through an analysis mechanism. Specifically, it converts the trend in the number of applications into a graph based on the timestamp of each application and applies an algorithm to detect fluctuations. As a result of this analysis process, the pattern of increase or decrease in the number of applications is output. 【0185】 Step 3: 【0186】 If the server determines through analysis that the number of applications has fallen below the set target, it will send an alert to the user using a "notification method." Specifically, this involves generating an email or system notification and sending it to the user's terminal. In this step, the condition checks for issuing the alert are the input, and the alert message is the output. 【0187】 Step 4: 【0188】 The server uses an estimation engine to obtain feedback from applicants and analyze the user's emotional state. It utilizes sentiment analysis tools such as the Google Cloud Natural Language API to infer emotions from text data. In this step, feedback data is input, and the inferred emotional state is output. 【0189】 Step 5: 【0190】 The server generates measures to increase the number of applications through suggested methods, based on the results of sentiment analysis. This process includes suggesting specific measures such as strengthening social media campaigns or offering incentives. In this step, the results of sentiment analysis are used as input, and content suggesting measures is output. 【0191】 Step 6: 【0192】 Users take action to improve their advertising campaigns based on the suggested measures provided by the server. They decide on specific measures according to the content of the measures to be implemented and use them in the deployment of the campaign. Here, the suggested measures become the input, and the improved campaign becomes the output. 【0193】 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. 【0194】 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. 【0195】 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. 【0196】 [Second Embodiment] 【0197】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0198】 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. 【0199】 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). 【0200】 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. 【0201】 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. 【0202】 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). 【0203】 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. 【0204】 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. 【0205】 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. 【0206】 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. 【0207】 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. 【0208】 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". 【0209】 The system of the present invention includes data receiving means, analysis means, notification means, and suggestion means, and these functions enable efficient management of application status. The details of each means and how they function are described below. 【0210】 First, regarding the data reception method, the system functions by having the server receive application data over the internet. This data includes the applicant's ID, application date and time, and application details. The received data is immediately stored in the database and becomes accessible in real time. 【0211】 Next, the analysis method involves the server analyzing the application data in the database using statistical methods. Specifically, it aggregates the daily number of applications and calculates the trends. By identifying the average number of applications over the past few days and the daily increase / decrease patterns, it is possible to visualize the overall picture of the application situation. 【0212】 Based on the analysis results, the server generates an alert if the number of applications falls below the set target or if a sudden decrease in the number of applications is detected. This alert is sent to the terminals of the person in charge or administrator via email or in-system notification. Users who receive the notification can take prompt action. 【0213】 Furthermore, the suggestion system automatically generates measures to increase the number of applications. Examples of measures include promotional posts on social media, enhanced reward plans for applicants, and targeted marketing using email newsletters. These suggestions are implemented after user confirmation, and the actual results are fed back into the system, enabling even more accurate suggestions. 【0214】 For example, if analysis reveals that the number of applications for a campaign falls significantly below the target within three days of its launch, the notification system sends an alert to the administrator's terminal. Simultaneously, the suggestion system presents specific improvement plans, such as strengthening campaign advertising on social media or adding early application bonuses. In this way, users can quickly take action and increase the number of applications. 【0215】 By implementing this invention, companies and organizations can smoothly monitor application status in real time, understand the degree of goal achievement, and conduct effective promotional activities. 【0216】 The following describes the processing flow. 【0217】 Step 1: 【0218】 The server receives the application data. This data consists of information entered by users via online forms and includes the applicant's personal information, application date and time, application details, and number of applications. The server receives this data in real time and stores it in a database. 【0219】 Step 2: 【0220】 The server periodically analyzes the application data stored in the database. Using the analysis tools, the server aggregates the daily number of applications and calculates the average of the most recent number of applications. Based on this information, it detects trends and anomalies in the number of applications. 【0221】 Step 3: 【0222】 The server compares the target number of applications with the current number of applications and generates an alert via a notification system if the number of applications falls below the target. The alert is sent to the responsible person's terminal via email or in-system notification. The alert includes a message stating that the number of applications has not reached the target and that immediate action is required. 【0223】 Step 4: 【0224】 The user (person in charge) checks the alert on their terminal and understands the situation. The alert includes detailed information such as the current number of applications and completion rate, so the user can immediately understand the urgency of the situation. 【0225】 Step 5: 【0226】 The server utilizes suggestion tools to automatically generate measures to increase the number of applications. Specific measures include social media posting campaigns, customer reminders via email newsletters, and the introduction of special offer plans. This information is presented on the user's device, and the user awaits their decision. 【0227】 Step 6: 【0228】 The user reviews the proposed measures provided by the server and decides whether to implement them as appropriate. The selected measures are then executed by the user, and the results are fed back to the server. Based on this feedback, the server uses the data to further improve the effectiveness of future measures. 【0229】 (Example 1) 【0230】 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." 【0231】 Conventional application management systems have difficulty with real-time monitoring of application data and rapid response, and have been particularly challenging in taking appropriate measures when the number of applications decreases. Furthermore, insufficient proposals for measures aimed at increasing the number of applications have not led to effective applicant recruitment activities. 【0232】 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. 【0233】 In this invention, the server includes means for acquiring application status via an information processing device and storing it in a data storage area; means for analyzing the stored information using statistical methods to show trends in the number of applications; means for generating warning information when the results deviate from set criteria; means for transmitting the warning information to an administrator terminal via communication means; and means for making suggestions using an artificial intelligence model that generates measures to improve the number of applications. This enables real-time monitoring of application status, rapid implementation of countermeasures, and proposal of effective measures to increase the number of applications. 【0234】 "Application status" refers to information that shows the state and changes in data submitted by applicants, including the number of applications and trends. 【0235】 An "information processing device" is a mechanical device or system used for collecting, storing, and analyzing application data. 【0236】 The "data storage area" is a digital storage system for storing and securely keeping received application data. 【0237】 "Statistical methods" are mathematical and computational techniques used in data analysis, and are methods for revealing trends and characteristics of data. 【0238】 "Warning information" refers to a cautionary message generated when the system detects an abnormality or deviation from the configured criteria. 【0239】 "Communication means" refers to methods and infrastructure for transmitting information to other terminals or systems. 【0240】 An "artificial intelligence model" is a computer program that analyzes diverse data to make decisions, predictions, and suggestions. 【0241】 A "policy" is a specific action plan or method implemented to achieve a particular objective. 【0242】 An "administrator terminal" is an electronic device used for monitoring and managing a system. 【0243】 This invention provides a system for efficiently managing application status. The system consists of a server as an information processing device, a terminal that receives operations from an administrator, and a user that implements measures to increase the number of applications. 【0244】 The server receives data from applicants in real time and stores it in the data storage area. This includes data entered via application forms over the internet, as well as data received via email and APIs. The received data is stored in database software such as MySQL or PostgreSQL. 【0245】 The server uses statistical analysis software such as Python's Pandas, NumPy, or the R language to analyze the data. This allows it to calculate the daily number of applications and their trends, and to understand the trends when the number of applications increases or decreases sharply. 【0246】 The server generates a warning if the analysis results deviate from the set criteria. This warning is sent to the administrator's terminal via email or the system's notification function. This allows the administrator to take prompt action according to the application status. 【0247】 Furthermore, the server utilizes a generative AI model to automatically generate specific measures to increase the number of applications. This prompt generation employs natural language processing techniques, which are widely used in AI models. Specific examples of proposed measures include strengthening advertising on social media and offering incentives to applicants. 【0248】 Based on this information, users allow administrators to review and implement suggestions. The results of the implemented measures are then fed back into the system, leading to improvements in the accuracy of the AI ​​model. 【0249】 For example, if the number of entries for a campaign falls below the target, the server will generate a prompt message such as, "The number of entries is below the target. To increase the number of entries, strengthen your social media campaign and offer rewards to early entrants." Upon receiving this information, the administrator can quickly implement countermeasures to improve the number of entries. 【0250】 By implementing this invention, companies and organizations will be able to smoothly monitor application status in real time, respond quickly, and implement effective promotional strategies. 【0251】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0252】 Step 1: 【0253】 The server receives application data from applicants in real time via the internet. The received data includes the applicant's ID, application date and time, and application details. The server immediately stores this data in its data storage area and saves the reception results in a database. 【0254】 Step 2: 【0255】 The server periodically analyzes current application data using statistical methods. Specifically, the server retrieves application data from the database and aggregates the daily number of applications using the Python Pandas library. As output of this analysis, it calculates trends in the number of applications and the average number of applications, and generates analysis results for further use. 【0256】 Step 3: 【0257】 Based on the analysis results, the server compares the number of applications against the set target value. If the number of applications falls below the target, the server generates a warning. This warning is output as an alert message and sent to the administrator's terminal via the email system. It is also distributed as an in-system notification. 【0258】 Step 4: 【0259】 The server uses the submitted analysis results and warning information to propose measures to increase the number of applications using an AI model. Specifically, the AI ​​model automatically generates measures such as strengthening promotion on social media and offering early application incentives. These proposals are sent from the server to the user's terminal as prompt messages. 【0260】 Step 5: 【0261】 The user reviews the suggestions received from the server, selects an appropriate measure, and implements it. The selected measure is executed, and the results are fed back to the server. This feedback data is stored to help improve the accuracy of subsequent suggestion generation. 【0262】 (Application Example 1) 【0263】 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." 【0264】 When conducting advertising campaigns, there are challenges in monitoring application status in real time and quickly formulating strategies to efficiently increase the number of applications. In particular, it is necessary to properly visualize the effectiveness of advertising and take immediate and effective countermeasures, but current methods are time-consuming in terms of information gathering and analysis, resulting in a reactive approach. 【0265】 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. 【0266】 In this invention, the server includes data receiving means for monitoring application status in real time, analysis means for analyzing the received data and calculating trends in the number of applications, and proposal means for visualizing advertising effectiveness and suggesting effective measures. This makes it possible to immediately grasp the effectiveness of advertising campaigns and take quick and appropriate countermeasures. 【0267】 "Data receiving means" refers to a means of monitoring application status in real time and acquiring application data. 【0268】 "Analysis means" refers to the means used to analyze received application data and calculate trends in the number of applications. 【0269】 "Notification method" refers to a means of generating an alert based on the analysis results when the number of applications falls below a set target value, and sending that alert via email or in-app notification. 【0270】 "Proposal methods" refer to means of visualizing advertising effectiveness and proposing effective measures to increase the number of applications. 【0271】 The system that implements this application is server-centric and uses the following hardware and software. The server runs a data analysis program written in Python and utilizes a web server framework using either Django or Flask. SQLite or PostgreSQL is used for the database for data storage and management. 【0272】 As a means of receiving data, the server retrieves application data via the internet and immediately saves it to the database. This data includes the application ID, date and time, and application details. 【0273】 The analysis method involves the server using statistical methods to analyze application data and understand trends in the number of applications. Specifically, it aggregates the number of applications, calculates daily increase / decrease patterns, and visualizes this data to facilitate an understanding of the overall application situation. 【0274】 The notification system uses server-generated analysis results to send alerts via email or in-app notifications if the number of applications falls below a set target value. This allows staff and administrators to quickly check the situation and take necessary actions. 【0275】 The suggestion system automatically generates measures to increase advertising effectiveness. These include targeted advertising to specific groups, revisions to the reward system, and strengthening campaigns on social media. The suggested measures are implemented after user confirmation, and the subsequent effects are fed back to improve the accuracy of the suggestions. 【0276】 For example, if the number of applications for a product suddenly drops during a promotional campaign, the server will send a notification stating, "The current click-through rate is 50% of the target. We recommend strengthening your targeted advertising on social media and increasing the incentives." In this way, users can quickly take action and improve the effectiveness of the advertising. 【0277】 As an example of a prompt sentence, an input such as "Please think of specific proposals to improve the campaign effect of the new product." can be considered. 【0278】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0279】 Step 1: 【0280】 The server acquires application data through the Internet. The inputs include the applicant's ID, application date and time, and application content. This data is immediately saved in the database. As specific operations, the server periodically calls an API or receives a Webhook to perform real-time data reception. 【0281】 Step 2: 【0282】 The server analyzes the application data saved in the database and calculates the trend of the number of applications. The saved historical data is used as the input. As the output, the aggregation result and increase / decrease pattern of the number of applications are obtained. As specific operations, a script written in Python is executed and a statistical library is used to process the data. 【0283】 Step 3: 【0284】 Based on the analysis result, the server checks whether the number of applications is below the target. The analysis result and the pre-set target value are referred to as the inputs. Whether an alert needs to be generated is determined as the output. As specific operations, an automatic comparison is performed using a threshold check algorithm. 【0285】 Step 4: 【0286】 If necessary, the server sends an alert through the notification means. The necessity of generating an alert and the notification destination information are used as the inputs. An email or an in-app notification is sent as the output. As specific operations, an email is sent using the SMTP protocol or a push notification service is used. 【0287】 Step 5: 【0288】 The server uses the proposal means to generate measures for increasing the number of applicants. Past application data, market data, and analysis results are used as inputs. As output, specific measure proposals are generated. As specific operations, the generation task is executed using an AI model, and the proposal content is organized by utilizing natural language processing technology. 【0289】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion. 【0290】 The present invention provides a system for more effectively increasing the number of applicants by monitoring the application situation in real time and analyzing the user's emotion. This system includes a data reception means, an analysis means, a notification means, a proposal means, and an emotion engine. 【0291】 First, in the data reception means, the server receives application data via the Internet. The received data is stored in a database by the server, and the information of each applicant is accumulated. Thereby, the action history and past data of the applicant are managed. 【0292】 The analysis means has a function of the server analyzing the application data in the database and calculating the trend of the number of applicants. Thereby, it is possible to grasp the increase or decrease in the number of applicants and detect abnormal fluctuations. Also, the analysis result is generated as a report, and the user can easily grasp the application situation. 【0293】 By the notification means, when the server detects that the number of applicants does not reach the target, it sends an alert to the user's terminal. The alert is transmitted through an email or a system notification, prompting the user to take prompt action. 【0294】 Furthermore, the proposed method automatically generates measures to increase the number of applications. These measures include a variety of strategies (for example, strengthening social media campaigns or proposing incentive plans) to facilitate the application process. 【0295】 The emotion engine is used to analyze the user's emotional state. The server takes in data from the user's past feedback and current behavior, and uses this to infer the user's emotions. This information is reflected in the suggested actions, providing the user with customized measures based on their emotions. 【0296】 For example, if the number of applications for a certain campaign falls below the target, the emotion engine analyzes user responses and, for instance, detects that users are "dissatisfied with the current application process." In this way, it can improve the number of applications through a more personalized approach. 【0297】 This embodiment allows companies to efficiently manage application status and implement flexible and effective measures that respond to user emotions. This is expected to improve the efficiency of application activities and increase participant satisfaction. 【0298】 The following describes the processing flow. 【0299】 Step 1: 【0300】 The server receives application data in real time. Applicants enter their application information using an online form, and this information is sent to the server via the internet. The server converts the received data into an easily understandable format and stores it in a database. 【0301】 Step 2: 【0302】 The server performs analysis based on existing application data. Daily or at other specified frequencies, the server aggregates the number of applications and calculates the average, and based on the results, grasps the increasing or decreasing trend of the number of applications. This analysis result is displayed on the terminal as a management dashboard. 【0303】 Step 3: 【0304】 The server compares the analysis result with the set application target. If the number of applications is below the target value, the server generates an alert via the notification means. This alert is sent to the terminals of the responsible persons as an email or a system notification. 【0305】 Step 4: 【0306】 The user who receives the alert on the terminal checks the content. The alert details the current application situation and the imminent problems, and the user can quickly consider countermeasures based on this information. 【0307】 Step 5: 【0308】 The server uses the sentiment engine to analyze the sentiment data of users and applicants. In particular, based on data obtained from past feedback and current behaviors, it infers the sentiment state of the applicants. The sentiment categories include satisfaction or dissatisfaction with the application process. 【0309】 Step 6: 【0310】 The server generates measures to increase the number of applications through the proposal means. This includes customized proposals based on the sentiment data of the users. For example, if there is feedback from the sentiment engine indicating low satisfaction, it proposes improving the operability as a measure to improve the application process. 【0311】 Step 7: 【0312】 The user reviews the proposed measures via their device and decides which measures to implement. The selected measures are then implemented by the user, and the results are fed back to the server for further analysis. 【0313】 These steps allow the system to maintain high application efficiency while flexibly responding to user emotions. 【0314】 (Example 2) 【0315】 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". 【0316】 In today's information-driven society, effectively increasing participation in application campaigns and events is a crucial challenge for many companies. However, tracking participant application status in real time and taking immediate, appropriate measures is technically and operationally difficult. Furthermore, implementing uniform measures without considering applicants' emotions and reactions can actually decrease participant satisfaction. A system is needed to address these challenges and improve the efficiency of application activities while enhancing the participant experience. 【0317】 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. 【0318】 In this invention, the server includes information processing means for monitoring the application status in real time, information analysis means for analyzing received information and calculating trends in the number of applications, information provision means for issuing a warning when the number of applications falls below a set standard, information generation means for suggesting measures to increase the number of applications, and sentiment analysis means for predicting users' emotions based on past opinions and behavioral data. This enables precise management of the application status and the implementation of flexible and effective measures based on the emotions of participants. 【0319】 "Information processing means" refers to system functions for monitoring application status in real time and effectively collecting necessary information. 【0320】 "Information analysis means" refers to a function that analyzes received data and calculates trends and patterns in the number of applications. 【0321】 The "information provision method" is a function that generates a warning when the number of applications falls below a set standard. 【0322】 "Information generation means" refers to a function that automatically suggests appropriate measures to increase the number of applications. 【0323】 "Emotional analysis tools" are functions that predict and analyze the emotions of applicants and users based on past feedback and behavioral data. 【0324】 The system of the present invention is a multi-functional platform centered around a server, for performing information processing, information analysis, information provision, information generation, and sentiment analysis. 【0325】 The server receives application data via the internet and stores it in a database in real time. The server uses high-performance database management software to efficiently process large amounts of data. Furthermore, dedicated analysis software is used for data analysis, quickly calculating trends in the number of applications and applicant behavior patterns. 【0326】 Based on the information provision mechanism, the server will generate warnings as needed, according to the configured criteria. For example, if the number of applications falls below 50% of the target, an alert will be generated and a notification will be sent to the user's device via email. This allows the user to immediately understand the situation and take appropriate countermeasures. 【0327】 The information generation method utilizes a generation AI model to automatically generate strategies to increase the number of applications. These strategies include specific measures such as strengthening social media campaigns and updating reward plans. These strategies are presented to users via their devices, guiding them as actionable options. 【0328】 Regarding sentiment analysis, a model is run on the server to predict user emotions based on past feedback and behavioral data. This model analyzes applicants' emotional responses and helps generate customized strategies. For example, if a user posts a complaint about the current application process, the server will suggest process improvements based on that content. 【0329】 An example of a prompt used as input to the generating AI model is a specific instruction such as, "Generate suggestions for new measures for a campaign that has seen a decline in applications." This allows users to quickly implement more accurate and effective measures. 【0330】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0331】 Step 1: 【0332】 The server receives application data via the internet. The received data includes the applicant's name, contact information, and application details. This data is stored in a database to aggregate the application information. The stored data forms the basis for future analysis and reporting. Furthermore, this information is used to create an application behavior history. 【0333】 Step 2: 【0334】 The server analyzes the application information in the database. The input is the entire contents of the database. The server uses statistical analysis algorithms to calculate trends in the number of applications and graphs the trends. It also compares the current data with past data to identify outliers and sudden fluctuations. The output is a report organized to allow the user to visually understand the application status. 【0335】 Step 3: 【0336】 The server generates a warning based on the analysis results. If the number of applications falls below the set threshold, the server receives the number of applications compared to the threshold as input. Based on this, it outputs an alert. Specifically, the alert is sent to the user's terminal as an email or system notification and includes content such as, "The number of applications is below expectations. Please check the details." 【0337】 Step 4: 【0338】 The server generates strategies to increase the number of applications. Past success stories and a generating AI model are provided as input. Based on this, specific measures to improve the number of applications are output. Examples include social media campaigns utilizing specific cultural events and incentive plans for new participants. 【0339】 Step 5: 【0340】 The server performs sentiment analysis. It uses the user's past feedback and current behavioral patterns as input. It applies an algorithm to infer emotional states and generates an action plan as a guide for participants. This makes it possible to provide personalized application promotion methods tailored to the user's emotions. 【0341】 (Application Example 2) 【0342】 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." 【0343】 In today's information-driven society, increasing the number of applicants for advertising campaigns and various recruitment activities is a crucial challenge. However, there is a lack of systems that can monitor application status in real time and implement effective countermeasures. Furthermore, traditional methods have made it difficult to accurately understand applicants' emotions and implement measures based on them. Therefore, there is a need for a system that can grasp trends in application numbers and propose quick and effective measures based on the emotional state of applicants. 【0344】 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. 【0345】 In this invention, the server includes an information receiving means for monitoring the application status in real time, an analysis means for analyzing the received information and calculating the trend in the number of applications, and an estimation engine means for analyzing emotional states and proposing measures. This enables the development of flexible and effective measures that respond to the emotions of applicants. 【0346】 "Information receiving means" refers to a system for monitoring application status in real time via the internet and collecting application data. 【0347】 An "analysis tool" is a system that analyzes received information and has the function of calculating trends and abnormal fluctuations in the number of applications. 【0348】 A "notification method" is a system that generates an alert and notifies users via electronic communication if the number of applications falls below a predetermined target value. 【0349】 The "estimation engine" is a mechanism for analyzing the emotional state of applicants and providing the information necessary when proposing measures. 【0350】 A "proposal tool" is a system that generates measures to increase the number of applications and presents appropriate solutions to users. 【0351】 The system of this invention is based on technology that aims to increase the number of applications by managing the application status and analyzing user sentiment. The server first receives application data in real time via the internet using an "information receiving means." This received data is stored in a database. The server analyzes the information in the database using an "analysis means" to calculate the trend in the number of applications and understand fluctuations. If the number of applications falls below the target number, an alert is sent to the user's terminal using a "notification means." The user receives this alert and is prompted to take prompt action. 【0352】 Furthermore, the server analyzes the user's emotional state using an "estimation engine." For this purpose, the server uses sentiment analysis software such as the Google Cloud Natural Language API. The server collects user feedback, infers their emotions, and incorporates this into its "suggestion tools," helping to propose measures to increase the number of applications. Specific measures include strengthening social media campaigns and offering incentives. 【0353】 For example, if an advertising campaign is failing to meet its target number of entries, the sentiment analysis engine might detect from user feedback that users are "dissatisfied with the current campaign." This information is then analyzed by the server, and suggestions for revising the campaign content are made as needed. 【0354】 An example of a prompt message generated using an AI model is: "The number of entries in the advertising campaign is below the target. Please conduct a sentiment analysis based on user feedback and propose measures to increase the number of entries." 【0355】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0356】 Step 1: 【0357】 The server receives information that monitors the application status in real time. Data from applicants is sent to the server via the internet and stored in the database. This input data includes applicant information and the date and time of application. 【0358】 Step 2: 【0359】 The server analyzes the information stored in the database through an analysis mechanism. Specifically, it converts the trend in the number of applications into a graph based on the timestamp of each application and applies an algorithm to detect fluctuations. As a result of this analysis process, the pattern of increase or decrease in the number of applications is output. 【0360】 Step 3: 【0361】 If the server determines through analysis that the number of applications has fallen below the set target, it will send an alert to the user using a "notification method." Specifically, this involves generating an email or system notification and sending it to the user's terminal. In this step, the condition checks for issuing the alert are the input, and the alert message is the output. 【0362】 Step 4: 【0363】 The server uses an estimation engine to obtain feedback from applicants and analyze the user's emotional state. It utilizes sentiment analysis tools such as the Google Cloud Natural Language API to infer emotions from text data. In this step, feedback data is input, and the inferred emotional state is output. 【0364】 Step 5: 【0365】 The server generates measures to increase the number of applications through suggested methods, based on the results of sentiment analysis. This process includes suggesting specific measures such as strengthening social media campaigns or offering incentives. In this step, the results of sentiment analysis are used as input, and content suggesting measures is output. 【0366】 Step 6: 【0367】 Users take action to improve their advertising campaigns based on the suggested measures provided by the server. They decide on specific measures according to the content of the measures to be implemented and use them in the deployment of the campaign. Here, the suggested measures become the input, and the improved campaign becomes the output. 【0368】 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. 【0369】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0370】 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. 【0371】 [Third Embodiment] 【0372】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0373】 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. 【0374】 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). 【0375】 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. 【0376】 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. 【0377】 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). 【0378】 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. 【0379】 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. 【0380】 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. 【0381】 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. 【0382】 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. 【0383】 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". 【0384】 The system of the present invention includes data receiving means, analysis means, notification means, and suggestion means, and these functions enable efficient management of application status. The details of each means and how they function are described below. 【0385】 First, regarding the data reception method, the system functions by having the server receive application data over the internet. This data includes the applicant's ID, application date and time, and application details. The received data is immediately stored in the database and becomes accessible in real time. 【0386】 Next, the analysis method involves the server analyzing the application data in the database using statistical methods. Specifically, it aggregates the daily number of applications and calculates the trends. By identifying the average number of applications over the past few days and the daily increase / decrease patterns, it is possible to visualize the overall picture of the application situation. 【0387】 Based on the analysis results, the server generates an alert if the number of applications falls below the set target or if a sudden decrease in the number of applications is detected. This alert is sent to the terminals of the person in charge or administrator via email or in-system notification. Users who receive the notification can take prompt action. 【0388】 Furthermore, the suggestion system automatically generates measures to increase the number of applications. Examples of measures include promotional posts on social media, enhanced reward plans for applicants, and targeted marketing using email newsletters. These suggestions are implemented after user confirmation, and the actual results are fed back into the system, enabling even more accurate suggestions. 【0389】 For example, if analysis reveals that the number of applications for a campaign falls significantly below the target within three days of its launch, the notification system sends an alert to the administrator's terminal. Simultaneously, the suggestion system presents specific improvement plans, such as strengthening campaign advertising on social media or adding early application bonuses. In this way, users can quickly take action and increase the number of applications. 【0390】 By implementing this invention, companies and organizations can smoothly monitor application status in real time, understand the degree of goal achievement, and conduct effective promotional activities. 【0391】 The following describes the processing flow. 【0392】 Step 1: 【0393】 The server receives the application data. This data consists of information entered by users via online forms and includes the applicant's personal information, application date and time, application details, and number of applications. The server receives this data in real time and stores it in a database. 【0394】 Step 2: 【0395】 The server periodically analyzes the application data stored in the database. Using the analysis tools, the server aggregates the daily number of applications and calculates the average of the most recent number of applications. Based on this information, it detects trends and anomalies in the number of applications. 【0396】 Step 3: 【0397】 The server compares the target number of applications with the current number of applications and generates an alert via a notification system if the number of applications falls below the target. The alert is sent to the responsible person's terminal via email or in-system notification. The alert includes a message stating that the number of applications has not reached the target and that immediate action is required. 【0398】 Step 4: 【0399】 The user (person in charge) checks the alert on their terminal and understands the situation. The alert includes detailed information such as the current number of applications and completion rate, so the user can immediately understand the urgency of the situation. 【0400】 Step 5: 【0401】 The server utilizes suggestion tools to automatically generate measures to increase the number of applications. Specific measures include social media posting campaigns, customer reminders via email newsletters, and the introduction of special offer plans. This information is presented on the user's device, and the user awaits their decision. 【0402】 Step 6: 【0403】 The user reviews the proposed measures provided by the server and decides whether to implement them as appropriate. The selected measures are then executed by the user, and the results are fed back to the server. Based on this feedback, the server uses the data to further improve the effectiveness of future measures. 【0404】 (Example 1) 【0405】 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." 【0406】 Conventional application management systems have difficulty with real-time monitoring of application data and rapid response, and have been particularly challenging in taking appropriate measures when the number of applications decreases. Furthermore, insufficient proposals for measures aimed at increasing the number of applications have not led to effective applicant recruitment activities. 【0407】 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. 【0408】 In this invention, the server includes means for acquiring application status via an information processing device and storing it in a data storage area; means for analyzing the stored information using statistical methods to show trends in the number of applications; means for generating warning information when the results deviate from set criteria; means for transmitting the warning information to an administrator terminal via communication means; and means for making suggestions using an artificial intelligence model that generates measures to improve the number of applications. This enables real-time monitoring of application status, rapid implementation of countermeasures, and proposal of effective measures to increase the number of applications. 【0409】 "Application status" refers to information that shows the state and changes in data submitted by applicants, including the number of applications and trends. 【0410】 An "information processing device" is a mechanical device or system used for collecting, storing, and analyzing application data. 【0411】 The "data storage area" is a digital storage system for storing and securely keeping received application data. 【0412】 "Statistical methods" are mathematical and computational techniques used in data analysis, and are methods for revealing trends and characteristics of data. 【0413】 "Warning information" refers to a cautionary message generated when the system detects an abnormality or deviation from the configured criteria. 【0414】 "Communication means" refers to methods and infrastructure for transmitting information to other terminals or systems. 【0415】 An "artificial intelligence model" is a computer program that analyzes diverse data to make decisions, predictions, and suggestions. 【0416】 A "policy" is a specific action plan or method implemented to achieve a particular objective. 【0417】 An "administrator terminal" is an electronic device used for monitoring and managing a system. 【0418】 This invention provides a system for efficiently managing application status. The system consists of a server as an information processing device, a terminal that receives operations from an administrator, and a user that implements measures to increase the number of applications. 【0419】 The server receives data from applicants in real time and stores it in the data storage area. This includes data entered via application forms over the internet, as well as data received via email and APIs. The received data is stored in database software such as MySQL or PostgreSQL. 【0420】 The server uses statistical analysis software such as Python's Pandas, NumPy, or the R language to analyze the data. This allows it to calculate the daily number of applications and their trends, and to understand the trends when the number of applications increases or decreases sharply. 【0421】 The server generates a warning if the analysis results deviate from the set criteria. This warning is sent to the administrator's terminal via email or the system's notification function. This allows the administrator to take prompt action according to the application status. 【0422】 Furthermore, the server utilizes a generative AI model to automatically generate specific measures to increase the number of applications. This prompt generation employs natural language processing techniques, which are widely used in AI models. Specific examples of proposed measures include strengthening advertising on social media and offering incentives to applicants. 【0423】 Based on this information, users allow administrators to review and implement suggestions. The results of the implemented measures are then fed back into the system, leading to improvements in the accuracy of the AI ​​model. 【0424】 For example, if the number of entries for a campaign falls below the target, the server will generate a prompt message such as, "The number of entries is below the target. To increase the number of entries, strengthen your social media campaign and offer rewards to early entrants." Upon receiving this information, the administrator can quickly implement countermeasures to improve the number of entries. 【0425】 By implementing this invention, companies and organizations will be able to smoothly monitor application status in real time, respond quickly, and implement effective promotional strategies. 【0426】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0427】 Step 1: 【0428】 The server receives application data from applicants in real time via the internet. The received data includes the applicant's ID, application date and time, and application details. The server immediately stores this data in its data storage area and saves the reception results in a database. 【0429】 Step 2: 【0430】 The server periodically analyzes current application data using statistical methods. Specifically, the server retrieves application data from the database and aggregates the daily number of applications using the Python Pandas library. As output of this analysis, it calculates trends in the number of applications and the average number of applications, and generates analysis results for further use. 【0431】 Step 3: 【0432】 Based on the analysis results, the server compares the number of applications against the set target value. If the number of applications falls below the target, the server generates a warning. This warning is output as an alert message and sent to the administrator's terminal via the email system. It is also distributed as an in-system notification. 【0433】 Step 4: 【0434】 The server uses the submitted analysis results and warning information to propose measures to increase the number of applications using an AI model. Specifically, the AI ​​model automatically generates measures such as strengthening promotion on social media and offering early application incentives. These proposals are sent from the server to the user's terminal as prompt messages. 【0435】 Step 5: 【0436】 The user reviews the suggestions received from the server, selects an appropriate measure, and implements it. The selected measure is executed, and the results are fed back to the server. This feedback data is stored to help improve the accuracy of subsequent suggestion generation. 【0437】 (Application Example 1) 【0438】 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." 【0439】 When conducting advertising campaigns, there are challenges in monitoring application status in real time and quickly formulating strategies to efficiently increase the number of applications. In particular, it is necessary to properly visualize the effectiveness of advertising and take immediate and effective countermeasures, but current methods are time-consuming in terms of information gathering and analysis, resulting in a reactive approach. 【0440】 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. 【0441】 In this invention, the server includes data receiving means for monitoring application status in real time, analysis means for analyzing the received data and calculating trends in the number of applications, and proposal means for visualizing advertising effectiveness and suggesting effective measures. This makes it possible to immediately grasp the effectiveness of advertising campaigns and take quick and appropriate countermeasures. 【0442】 "Data receiving means" refers to a means of monitoring application status in real time and acquiring application data. 【0443】 "Analysis means" refers to the means used to analyze received application data and calculate trends in the number of applications. 【0444】 "Notification method" refers to a means of generating an alert based on the analysis results when the number of applications falls below a set target value, and sending that alert via email or in-app notification. 【0445】 "Proposal methods" refer to means of visualizing advertising effectiveness and proposing effective measures to increase the number of applications. 【0446】 The system that implements this application is server-centric and uses the following hardware and software. The server runs a data analysis program written in Python and utilizes a web server framework using either Django or Flask. SQLite or PostgreSQL is used for the database for data storage and management. 【0447】 As a means of receiving data, the server retrieves application data via the internet and immediately saves it to the database. This data includes the application ID, date and time, and application details. 【0448】 The analysis method involves the server using statistical methods to analyze application data and understand trends in the number of applications. Specifically, it aggregates the number of applications, calculates daily increase / decrease patterns, and visualizes this data to facilitate an understanding of the overall application situation. 【0449】 The notification system uses server-generated analysis results to send alerts via email or in-app notifications if the number of applications falls below a set target value. This allows staff and administrators to quickly check the situation and take necessary actions. 【0450】 The suggestion system automatically generates measures to increase advertising effectiveness. These include targeted advertising to specific groups, revisions to the reward system, and strengthening campaigns on social media. The suggested measures are implemented after user confirmation, and the subsequent effects are fed back to improve the accuracy of the suggestions. 【0451】 For example, if the number of applications for a product suddenly drops during a promotional campaign, the server will send a notification stating, "The current click-through rate is 50% of the target. We recommend strengthening your targeted advertising on social media and increasing the incentives." In this way, users can quickly take action and improve the effectiveness of the advertising. 【0452】 An example of a prompt message could be, "Please come up with specific suggestions to improve the effectiveness of the new product campaign." 【0453】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0454】 Step 1: 【0455】 The server retrieves application data via the internet. Inputs include the applicant's ID, application date and time, and application details. This data is immediately saved to the database. Specifically, the server periodically calls an API or receives webhooks to receive data in real time. 【0456】 Step 2: 【0457】 The server analyzes application data stored in the database to calculate trends in the number of applications. It uses stored historical data as input. The output includes aggregated application numbers and patterns of increase / decrease. Specifically, it executes a script written in Python and processes the data using statistical libraries. 【0458】 Step 3: 【0459】 The server checks whether the number of applications is below the target based on the analysis results. It uses the analysis results and pre-set target values ​​as input. The output determines whether an alert needs to be generated. Specifically, it performs an automatic comparison using a threshold check algorithm. 【0460】 Step 4: 【0461】 The server sends alerts via notification methods as needed. Inputs include whether an alert should be generated and recipient information. Outputs include email and in-app notifications. Specifically, this involves sending emails using the SMTP protocol or utilizing a push notification service. 【0462】 Step 5: 【0463】 The server generates measures to increase the number of applications using the proposed methods. Past application data, market data, and analysis results are used as input. Specific proposed measures are generated as output. Specifically, the generation task is performed using an AI model, and the proposed content is refined using natural language processing technology. 【0464】 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. 【0465】 This invention provides a system for more effectively increasing the number of applications by monitoring application status in real time and analyzing user sentiment. This system includes data receiving means, analysis means, notification means, suggestion means, and sentiment engine. 【0466】 First, in the data reception process, the server receives application data via the internet. The received data is stored in a database by the server, accumulating information about each applicant. This allows for the management of applicants' behavioral history and past data. 【0467】 The analysis system has a function that allows the server to analyze application data in the database and calculate trends in the number of applications. This makes it possible to understand increases and decreases in the number of applications and detect abnormal fluctuations. In addition, the analysis results are generated as a report, allowing users to easily understand the application status. 【0468】 The notification system detects that the number of applications has not reached the target and sends an alert to the user's device. The alert is communicated via email or system notification, prompting the user to take prompt action. 【0469】 Furthermore, the proposed method automatically generates measures to increase the number of applications. These measures include a variety of strategies (for example, strengthening social media campaigns or proposing incentive plans) to facilitate the application process. 【0470】 The emotion engine is used to analyze the user's emotional state. The server takes in data from the user's past feedback and current behavior, and uses this to infer the user's emotions. This information is reflected in the suggested actions, providing the user with customized measures based on their emotions. 【0471】 For example, if the number of applications for a certain campaign falls below the target, the emotion engine analyzes user responses and, for instance, detects that users are "dissatisfied with the current application process." In this way, it can improve the number of applications through a more personalized approach. 【0472】 This embodiment allows companies to efficiently manage application status and implement flexible and effective measures that respond to user emotions. This is expected to improve the efficiency of application activities and increase participant satisfaction. 【0473】 The following describes the processing flow. 【0474】 Step 1: 【0475】 The server receives application data in real time. Applicants enter their application information using an online form, and this information is sent to the server via the internet. The server converts the received data into an easily understandable format and stores it in a database. 【0476】 Step 2: 【0477】 The server performs analysis based on existing application data. Daily or at other specified frequencies, the server aggregates application numbers and calculates averages, using the results to understand trends in application growth and decline. These analysis results are displayed on the terminal as a management dashboard. 【0478】 Step 3: 【0479】 The server compares the analysis results with the set application target. If the number of applications falls below the target, the server generates an alert via a notification system. This alert is sent to the responsible person's terminal via email or system notification. 【0480】 Step 4: 【0481】 Users who receive an alert on their device can review its contents. The alert contains detailed information about the current application status and any pressing issues, allowing users to quickly consider countermeasures based on that information. 【0482】 Step 5: 【0483】 The server uses an emotion engine to analyze user and applicant sentiment data. Specifically, it infers the applicant's emotional state based on data obtained from past feedback and current behavior. Sentiment categories include satisfaction and dissatisfaction with the application process. 【0484】 Step 6: 【0485】 The server generates strategies to increase the number of applications through suggestion mechanisms. This includes customized suggestions based on user sentiment data. For example, if the sentiment engine provides feedback indicating low satisfaction, it will suggest improving usability as a way to improve the application process. 【0486】 Step 7: 【0487】 The user reviews the proposed measures via their device and decides which measures to implement. The selected measures are then implemented by the user, and the results are fed back to the server for further analysis. 【0488】 These steps allow the system to maintain high application efficiency while flexibly responding to user emotions. 【0489】 (Example 2) 【0490】 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." 【0491】 In today's information-driven society, effectively increasing participation in application campaigns and events is a crucial challenge for many companies. However, tracking participant application status in real time and taking immediate, appropriate measures is technically and operationally difficult. Furthermore, implementing uniform measures without considering applicants' emotions and reactions can actually decrease participant satisfaction. A system is needed to address these challenges and improve the efficiency of application activities while enhancing the participant experience. 【0492】 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. 【0493】 In this invention, the server includes information processing means for monitoring the application status in real time, information analysis means for analyzing received information and calculating trends in the number of applications, information provision means for issuing a warning when the number of applications falls below a set standard, information generation means for suggesting measures to increase the number of applications, and sentiment analysis means for predicting users' emotions based on past opinions and behavioral data. This enables precise management of the application status and the implementation of flexible and effective measures based on the emotions of participants. 【0494】 "Information processing means" refers to system functions for monitoring application status in real time and effectively collecting necessary information. 【0495】 "Information analysis means" refers to a function that analyzes received data and calculates trends and patterns in the number of applications. 【0496】 The "information provision method" is a function that generates a warning when the number of applications falls below a set standard. 【0497】 "Information generation means" refers to a function that automatically suggests appropriate measures to increase the number of applications. 【0498】 "Emotional analysis tools" are functions that predict and analyze the emotions of applicants and users based on past feedback and behavioral data. 【0499】 The system of the present invention is a multi-functional platform centered around a server, for performing information processing, information analysis, information provision, information generation, and sentiment analysis. 【0500】 The server receives application data via the internet and stores it in a database in real time. The server uses high-performance database management software to efficiently process large amounts of data. Furthermore, dedicated analysis software is used for data analysis, quickly calculating trends in the number of applications and applicant behavior patterns. 【0501】 Based on the information provision mechanism, the server will generate warnings as needed, according to the configured criteria. For example, if the number of applications falls below 50% of the target, an alert will be generated and a notification will be sent to the user's device via email. This allows the user to immediately understand the situation and take appropriate countermeasures. 【0502】 The information generation method utilizes a generation AI model to automatically generate strategies to increase the number of applications. These strategies include specific measures such as strengthening social media campaigns and updating reward plans. These strategies are presented to users via their devices, guiding them as actionable options. 【0503】 Regarding sentiment analysis, a model is run on the server to predict user emotions based on past feedback and behavioral data. This model analyzes applicants' emotional responses and helps generate customized strategies. For example, if a user posts a complaint about the current application process, the server will suggest process improvements based on that content. 【0504】 An example of a prompt used as input to the generating AI model is a specific instruction such as, "Generate suggestions for new measures for a campaign that has seen a decline in applications." This allows users to quickly implement more accurate and effective measures. 【0505】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0506】 Step 1: 【0507】 The server receives application data via the internet. The received data includes the applicant's name, contact information, and application details. This data is stored in a database to aggregate the application information. The stored data forms the basis for future analysis and reporting. Furthermore, this information is used to create an application behavior history. 【0508】 Step 2: 【0509】 The server analyzes the application information in the database. The input is the entire contents of the database. The server uses statistical analysis algorithms to calculate trends in the number of applications and graphs the trends. It also compares the current data with past data to identify outliers and sudden fluctuations. The output is a report organized to allow the user to visually understand the application status. 【0510】 Step 3: 【0511】 The server generates a warning based on the analysis results. If the number of applications falls below the set threshold, the server receives the number of applications compared to the threshold as input. Based on this, it outputs an alert. Specifically, the alert is sent to the user's terminal as an email or system notification and includes content such as, "The number of applications is below expectations. Please check the details." 【0512】 Step 4: 【0513】 The server generates strategies to increase the number of applications. Past success stories and a generating AI model are provided as input. Based on this, specific measures to improve the number of applications are output. Examples include social media campaigns utilizing specific cultural events and incentive plans for new participants. 【0514】 Step 5: 【0515】 The server performs sentiment analysis. It uses the user's past feedback and current behavioral patterns as input. It applies an algorithm to infer emotional states and generates an action plan as a guide for participants. This makes it possible to provide personalized application promotion methods tailored to the user's emotions. 【0516】 (Application Example 2) 【0517】 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." 【0518】 In today's information-driven society, increasing the number of applicants for advertising campaigns and various recruitment activities is a crucial challenge. However, there is a lack of systems that can monitor application status in real time and implement effective countermeasures. Furthermore, traditional methods have made it difficult to accurately understand applicants' emotions and implement measures based on them. Therefore, there is a need for a system that can grasp trends in application numbers and propose quick and effective measures based on the emotional state of applicants. 【0519】 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. 【0520】 In this invention, the server includes an information receiving means for monitoring the application status in real time, an analysis means for analyzing the received information and calculating the trend in the number of applications, and an estimation engine means for analyzing emotional states and proposing measures. This enables the development of flexible and effective measures that respond to the emotions of applicants. 【0521】 "Information receiving means" refers to a system for monitoring application status in real time via the internet and collecting application data. 【0522】 An "analysis tool" is a system that analyzes received information and has the function of calculating trends and abnormal fluctuations in the number of applications. 【0523】 A "notification method" is a system that generates an alert and notifies users via electronic communication if the number of applications falls below a predetermined target value. 【0524】 The "estimation engine" is a mechanism for analyzing the emotional state of applicants and providing the information necessary when proposing measures. 【0525】 A "proposal tool" is a system that generates measures to increase the number of applications and presents appropriate solutions to users. 【0526】 The system of this invention is based on technology that aims to increase the number of applications by managing the application status and analyzing user sentiment. The server first receives application data in real time via the internet using an "information receiving means." This received data is stored in a database. The server analyzes the information in the database using an "analysis means" to calculate the trend in the number of applications and understand fluctuations. If the number of applications falls below the target number, an alert is sent to the user's terminal using a "notification means." The user receives this alert and is prompted to take prompt action. 【0527】 Furthermore, the server analyzes the user's emotional state using an "estimation engine." For this purpose, the server uses sentiment analysis software such as the Google Cloud Natural Language API. The server collects user feedback, infers their emotions, and incorporates this into its "suggestion tools," helping to propose measures to increase the number of applications. Specific measures include strengthening social media campaigns and offering incentives. 【0528】 For example, if an advertising campaign is failing to meet its target number of entries, the sentiment analysis engine might detect from user feedback that users are "dissatisfied with the current campaign." This information is then analyzed by the server, and suggestions for revising the campaign content are made as needed. 【0529】 An example of a prompt message generated using an AI model is: "The number of entries in the advertising campaign is below the target. Please conduct a sentiment analysis based on user feedback and propose measures to increase the number of entries." 【0530】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0531】 Step 1: 【0532】 The server receives information that monitors the application status in real time. Data from applicants is sent to the server via the internet and stored in the database. This input data includes applicant information and the date and time of application. 【0533】 Step 2: 【0534】 The server analyzes the information stored in the database through an analysis mechanism. Specifically, it converts the trend in the number of applications into a graph based on the timestamp of each application and applies an algorithm to detect fluctuations. As a result of this analysis process, the pattern of increase or decrease in the number of applications is output. 【0535】 Step 3: 【0536】 If the server determines through analysis that the number of applications has fallen below the set target, it will send an alert to the user using a "notification method." Specifically, this involves generating an email or system notification and sending it to the user's terminal. In this step, the condition checks for issuing the alert are the input, and the alert message is the output. 【0537】 Step 4: 【0538】 The server uses an estimation engine to obtain feedback from applicants and analyze the user's emotional state. It utilizes sentiment analysis tools such as the Google Cloud Natural Language API to infer emotions from text data. In this step, feedback data is input, and the inferred emotional state is output. 【0539】 Step 5: 【0540】 The server generates measures to increase the number of applications through suggested methods, based on the results of sentiment analysis. This process includes suggesting specific measures such as strengthening social media campaigns or offering incentives. In this step, the results of sentiment analysis are used as input, and content suggesting measures is output. 【0541】 Step 6: 【0542】 Users take action to improve their advertising campaigns based on the suggested measures provided by the server. They decide on specific measures according to the content of the measures to be implemented and use them in the deployment of the campaign. Here, the suggested measures become the input, and the improved campaign becomes the output. 【0543】 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. 【0544】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0545】 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. 【0546】 [Fourth Embodiment] 【0547】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0548】 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. 【0549】 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). 【0550】 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. 【0551】 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. 【0552】 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). 【0553】 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. 【0554】 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. 【0555】 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. 【0556】 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. 【0557】 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. 【0558】 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. 【0559】 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". 【0560】 The system of the present invention includes data receiving means, analysis means, notification means, and suggestion means, and these functions enable efficient management of application status. The details of each means and how they function are described below. 【0561】 First, regarding the data reception method, the system functions by having the server receive application data over the internet. This data includes the applicant's ID, application date and time, and application details. The received data is immediately stored in the database and becomes accessible in real time. 【0562】 Next, the analysis method involves the server analyzing the application data in the database using statistical methods. Specifically, it aggregates the daily number of applications and calculates the trends. By identifying the average number of applications over the past few days and the daily increase / decrease patterns, it is possible to visualize the overall picture of the application situation. 【0563】 Based on the analysis results, the server generates an alert if the number of applications falls below the set target or if a sudden decrease in the number of applications is detected. This alert is sent to the terminals of the person in charge or administrator via email or in-system notification. Users who receive the notification can take prompt action. 【0564】 Furthermore, the suggestion system automatically generates measures to increase the number of applications. Examples of measures include promotional posts on social media, enhanced reward plans for applicants, and targeted marketing using email newsletters. These suggestions are implemented after user confirmation, and the actual results are fed back into the system, enabling even more accurate suggestions. 【0565】 For example, if analysis reveals that the number of applications for a campaign falls significantly below the target within three days of its launch, the notification system sends an alert to the administrator's terminal. Simultaneously, the suggestion system presents specific improvement plans, such as strengthening campaign advertising on social media or adding early application bonuses. In this way, users can quickly take action and increase the number of applications. 【0566】 By implementing this invention, companies and organizations can smoothly monitor application status in real time, understand the degree of goal achievement, and conduct effective promotional activities. 【0567】 The following describes the processing flow. 【0568】 Step 1: 【0569】 The server receives the application data. This data consists of information entered by users via online forms and includes the applicant's personal information, application date and time, application details, and number of applications. The server receives this data in real time and stores it in a database. 【0570】 Step 2: 【0571】 The server periodically analyzes the application data stored in the database. Using the analysis tools, the server aggregates the daily number of applications and calculates the average of the most recent number of applications. Based on this information, it detects trends and anomalies in the number of applications. 【0572】 Step 3: 【0573】 The server compares the target number of applications with the current number of applications and generates an alert via a notification system if the number of applications falls below the target. The alert is sent to the responsible person's terminal via email or in-system notification. The alert includes a message stating that the number of applications has not reached the target and that immediate action is required. 【0574】 Step 4: 【0575】 The user (person in charge) checks the alert on their terminal and understands the situation. The alert includes detailed information such as the current number of applications and completion rate, so the user can immediately understand the urgency of the situation. 【0576】 Step 5: 【0577】 The server utilizes suggestion tools to automatically generate measures to increase the number of applications. Specific measures include social media posting campaigns, customer reminders via email newsletters, and the introduction of special offer plans. This information is presented on the user's device, and the user awaits their decision. 【0578】 Step 6: 【0579】 The user reviews the proposed measures provided by the server and decides whether to implement them as appropriate. The selected measures are then executed by the user, and the results are fed back to the server. Based on this feedback, the server uses the data to further improve the effectiveness of future measures. 【0580】 (Example 1) 【0581】 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". 【0582】 Conventional application management systems have difficulty with real-time monitoring of application data and rapid response, and have been particularly challenging in taking appropriate measures when the number of applications decreases. Furthermore, insufficient proposals for measures aimed at increasing the number of applications have not led to effective applicant recruitment activities. 【0583】 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. 【0584】 In this invention, the server includes means for acquiring application status via an information processing device and storing it in a data storage area; means for analyzing the stored information using statistical methods to show trends in the number of applications; means for generating warning information when the results deviate from set criteria; means for transmitting the warning information to an administrator terminal via communication means; and means for making suggestions using an artificial intelligence model that generates measures to improve the number of applications. This enables real-time monitoring of application status, rapid implementation of countermeasures, and proposal of effective measures to increase the number of applications. 【0585】 "Application status" refers to information that shows the state and changes in data submitted by applicants, including the number of applications and trends. 【0586】 An "information processing device" is a mechanical device or system used for collecting, storing, and analyzing application data. 【0587】 The "data storage area" is a digital storage system for storing and securely keeping received application data. 【0588】 "Statistical methods" are mathematical and computational techniques used in data analysis, and are methods for revealing trends and characteristics of data. 【0589】 "Warning information" refers to a cautionary message generated when the system detects an abnormality or deviation from the configured criteria. 【0590】 "Communication means" refers to methods and infrastructure for transmitting information to other terminals or systems. 【0591】 An "artificial intelligence model" is a computer program that analyzes diverse data to make decisions, predictions, and suggestions. 【0592】 A "policy" is a specific action plan or method implemented to achieve a particular objective. 【0593】 An "administrator terminal" is an electronic device used for monitoring and managing a system. 【0594】 This invention provides a system for efficiently managing application status. The system consists of a server as an information processing device, a terminal that receives operations from an administrator, and a user that implements measures to increase the number of applications. 【0595】 The server receives data from applicants in real time and stores it in the data storage area. This includes data entered via application forms over the internet, as well as data received via email and APIs. The received data is stored in database software such as MySQL or PostgreSQL. 【0596】 The server uses statistical analysis software such as Python's Pandas, NumPy, or the R language to analyze the data. This allows it to calculate the daily number of applications and their trends, and to understand the trends when the number of applications increases or decreases sharply. 【0597】 The server generates a warning if the analysis results deviate from the set criteria. This warning is sent to the administrator's terminal via email or the system's notification function. This allows the administrator to take prompt action according to the application status. 【0598】 Furthermore, the server utilizes a generative AI model to automatically generate specific measures to increase the number of applications. This prompt generation employs natural language processing techniques, which are widely used in AI models. Specific examples of proposed measures include strengthening advertising on social media and offering incentives to applicants. 【0599】 Based on this information, users allow administrators to review and implement suggestions. The results of the implemented measures are then fed back into the system, leading to improvements in the accuracy of the AI ​​model. 【0600】 For example, if the number of entries for a campaign falls below the target, the server will generate a prompt message such as, "The number of entries is below the target. To increase the number of entries, strengthen your social media campaign and offer rewards to early entrants." Upon receiving this information, the administrator can quickly implement countermeasures to improve the number of entries. 【0601】 By implementing this invention, companies and organizations will be able to smoothly monitor application status in real time, respond quickly, and implement effective promotional strategies. 【0602】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0603】 Step 1: 【0604】 The server receives application data from applicants in real time via the internet. The received data includes the applicant's ID, application date and time, and application details. The server immediately stores this data in its data storage area and saves the reception results in a database. 【0605】 Step 2: 【0606】 The server periodically analyzes current application data using statistical methods. Specifically, the server retrieves application data from the database and aggregates the daily number of applications using the Python Pandas library. As output of this analysis, it calculates trends in the number of applications and the average number of applications, and generates analysis results for further use. 【0607】 Step 3: 【0608】 Based on the analysis results, the server compares the number of applications against the set target value. If the number of applications falls below the target, the server generates a warning. This warning is output as an alert message and sent to the administrator's terminal via the email system. It is also distributed as an in-system notification. 【0609】 Step 4: 【0610】 The server uses the submitted analysis results and warning information to propose measures to increase the number of applications using an AI model. Specifically, the AI ​​model automatically generates measures such as strengthening promotion on social media and offering early application incentives. These proposals are sent from the server to the user's terminal as prompt messages. 【0611】 Step 5: 【0612】 The user reviews the suggestions received from the server, selects an appropriate measure, and implements it. The selected measure is executed, and the results are fed back to the server. This feedback data is stored to help improve the accuracy of subsequent suggestion generation. 【0613】 (Application Example 1) 【0614】 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". 【0615】 When conducting advertising campaigns, there are challenges in monitoring application status in real time and quickly formulating strategies to efficiently increase the number of applications. In particular, it is necessary to properly visualize the effectiveness of advertising and take immediate and effective countermeasures, but current methods are time-consuming in terms of information gathering and analysis, resulting in a reactive approach. 【0616】 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. 【0617】 In this invention, the server includes data receiving means for monitoring application status in real time, analysis means for analyzing the received data and calculating trends in the number of applications, and proposal means for visualizing advertising effectiveness and suggesting effective measures. This makes it possible to immediately grasp the effectiveness of advertising campaigns and take quick and appropriate countermeasures. 【0618】 "Data receiving means" refers to a means of monitoring application status in real time and acquiring application data. 【0619】 "Analysis means" refers to the means used to analyze received application data and calculate trends in the number of applications. 【0620】 "Notification method" refers to a means of generating an alert based on the analysis results when the number of applications falls below a set target value, and sending that alert via email or in-app notification. 【0621】 "Proposal methods" refer to means of visualizing advertising effectiveness and proposing effective measures to increase the number of applications. 【0622】 The system that implements this application is server-centric and uses the following hardware and software. The server runs a data analysis program written in Python and utilizes a web server framework using either Django or Flask. SQLite or PostgreSQL is used for the database for data storage and management. 【0623】 As a means of receiving data, the server retrieves application data via the internet and immediately saves it to the database. This data includes the application ID, date and time, and application details. 【0624】 The analysis method involves the server using statistical methods to analyze application data and understand trends in the number of applications. Specifically, it aggregates the number of applications, calculates daily increase / decrease patterns, and visualizes this data to facilitate an understanding of the overall application situation. 【0625】 The notification system uses server-generated analysis results to send alerts via email or in-app notifications if the number of applications falls below a set target value. This allows staff and administrators to quickly check the situation and take necessary actions. 【0626】 The suggestion system automatically generates measures to increase advertising effectiveness. These include targeted advertising to specific groups, revisions to the reward system, and strengthening campaigns on social media. The suggested measures are implemented after user confirmation, and the subsequent effects are fed back to improve the accuracy of the suggestions. 【0627】 For example, if the number of applications for a product suddenly drops during a promotional campaign, the server will send a notification stating, "The current click-through rate is 50% of the target. We recommend strengthening your targeted advertising on social media and increasing the incentives." In this way, users can quickly take action and improve the effectiveness of the advertising. 【0628】 An example of a prompt message could be, "Please come up with specific suggestions to improve the effectiveness of the new product campaign." 【0629】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0630】 Step 1: 【0631】 The server retrieves application data via the internet. Inputs include the applicant's ID, application date and time, and application details. This data is immediately saved to the database. Specifically, the server periodically calls an API or receives webhooks to receive data in real time. 【0632】 Step 2: 【0633】 The server analyzes application data stored in the database to calculate trends in the number of applications. It uses stored historical data as input. The output includes aggregated application numbers and patterns of increase / decrease. Specifically, it executes a script written in Python and processes the data using statistical libraries. 【0634】 Step 3: 【0635】 The server checks whether the number of applications is below the target based on the analysis results. It uses the analysis results and pre-set target values ​​as input. The output determines whether an alert needs to be generated. Specifically, it performs an automatic comparison using a threshold check algorithm. 【0636】 Step 4: 【0637】 The server sends alerts via notification methods as needed. Inputs include whether an alert should be generated and recipient information. Outputs include email and in-app notifications. Specifically, this involves sending emails using the SMTP protocol or utilizing a push notification service. 【0638】 Step 5: 【0639】 The server generates measures to increase the number of applications using the proposed methods. Past application data, market data, and analysis results are used as input. Specific proposed measures are generated as output. Specifically, the generation task is performed using an AI model, and the proposed content is refined using natural language processing technology. 【0640】 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. 【0641】 This invention provides a system for more effectively increasing the number of applications by monitoring application status in real time and analyzing user sentiment. This system includes data receiving means, analysis means, notification means, suggestion means, and sentiment engine. 【0642】 First, in the data reception process, the server receives application data via the internet. The received data is stored in a database by the server, accumulating information about each applicant. This allows for the management of applicants' behavioral history and past data. 【0643】 The analysis system has a function that allows the server to analyze application data in the database and calculate trends in the number of applications. This makes it possible to understand increases and decreases in the number of applications and detect abnormal fluctuations. In addition, the analysis results are generated as a report, allowing users to easily understand the application status. 【0644】 The notification system detects that the number of applications has not reached the target and sends an alert to the user's device. The alert is communicated via email or system notification, prompting the user to take prompt action. 【0645】 Furthermore, the proposed method automatically generates measures to increase the number of applications. These measures include a variety of strategies (for example, strengthening social media campaigns or proposing incentive plans) to facilitate the application process. 【0646】 The emotion engine is used to analyze the user's emotional state. The server takes in data from the user's past feedback and current behavior, and uses this to infer the user's emotions. This information is reflected in the suggested actions, providing the user with customized measures based on their emotions. 【0647】 For example, if the number of applications for a certain campaign falls below the target, the emotion engine analyzes user responses and, for instance, detects that users are "dissatisfied with the current application process." In this way, it can improve the number of applications through a more personalized approach. 【0648】 This embodiment allows companies to efficiently manage application status and implement flexible and effective measures that respond to user emotions. This is expected to improve the efficiency of application activities and increase participant satisfaction. 【0649】 The following describes the processing flow. 【0650】 Step 1: 【0651】 The server receives application data in real time. Applicants enter their application information using an online form, and this information is sent to the server via the internet. The server converts the received data into an easily understandable format and stores it in a database. 【0652】 Step 2: 【0653】 The server performs analysis based on existing application data. Daily or at other specified frequencies, the server aggregates application numbers and calculates averages, using the results to understand trends in application growth and decline. These analysis results are displayed on the terminal as a management dashboard. 【0654】 Step 3: 【0655】 The server compares the analysis results with the set application target. If the number of applications falls below the target, the server generates an alert via a notification system. This alert is sent to the responsible person's terminal via email or system notification. 【0656】 Step 4: 【0657】 Users who receive an alert on their device can review its contents. The alert contains detailed information about the current application status and any pressing issues, allowing users to quickly consider countermeasures based on that information. 【0658】 Step 5: 【0659】 The server uses an emotion engine to analyze user and applicant sentiment data. Specifically, it infers the applicant's emotional state based on data obtained from past feedback and current behavior. Sentiment categories include satisfaction and dissatisfaction with the application process. 【0660】 Step 6: 【0661】 The server generates strategies to increase the number of applications through suggestion mechanisms. This includes customized suggestions based on user sentiment data. For example, if the sentiment engine provides feedback indicating low satisfaction, it will suggest improving usability as a way to improve the application process. 【0662】 Step 7: 【0663】 The user reviews the proposed measures via their device and decides which measures to implement. The selected measures are then implemented by the user, and the results are fed back to the server for further analysis. 【0664】 These steps allow the system to maintain high application efficiency while flexibly responding to user emotions. 【0665】 (Example 2) 【0666】 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". 【0667】 In today's information-driven society, effectively increasing participation in application campaigns and events is a crucial challenge for many companies. However, tracking participant application status in real time and taking immediate, appropriate measures is technically and operationally difficult. Furthermore, implementing uniform measures without considering applicants' emotions and reactions can actually decrease participant satisfaction. A system is needed to address these challenges and improve the efficiency of application activities while enhancing the participant experience. 【0668】 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. 【0669】 In this invention, the server includes information processing means for monitoring the application status in real time, information analysis means for analyzing received information and calculating trends in the number of applications, information provision means for issuing a warning when the number of applications falls below a set standard, information generation means for suggesting measures to increase the number of applications, and sentiment analysis means for predicting users' emotions based on past opinions and behavioral data. This enables precise management of the application status and the implementation of flexible and effective measures based on the emotions of participants. 【0670】 "Information processing means" refers to system functions for monitoring application status in real time and effectively collecting necessary information. 【0671】 "Information analysis means" refers to a function that analyzes received data and calculates trends and patterns in the number of applications. 【0672】 The "information provision method" is a function that generates a warning when the number of applications falls below a set standard. 【0673】 "Information generation means" refers to a function that automatically suggests appropriate measures to increase the number of applications. 【0674】 "Emotional analysis tools" are functions that predict and analyze the emotions of applicants and users based on past feedback and behavioral data. 【0675】 The system of the present invention is a multi-functional platform centered around a server, for performing information processing, information analysis, information provision, information generation, and sentiment analysis. 【0676】 The server receives application data via the internet and stores it in a database in real time. The server uses high-performance database management software to efficiently process large amounts of data. Furthermore, dedicated analysis software is used for data analysis, quickly calculating trends in the number of applications and applicant behavior patterns. 【0677】 Based on the information provision mechanism, the server will generate warnings as needed, according to the configured criteria. For example, if the number of applications falls below 50% of the target, an alert will be generated and a notification will be sent to the user's device via email. This allows the user to immediately understand the situation and take appropriate countermeasures. 【0678】 The information generation method utilizes a generation AI model to automatically generate strategies to increase the number of applications. These strategies include specific measures such as strengthening social media campaigns and updating reward plans. These strategies are presented to users via their devices, guiding them as actionable options. 【0679】 Regarding sentiment analysis, a model is run on the server to predict user emotions based on past feedback and behavioral data. This model analyzes applicants' emotional responses and helps generate customized strategies. For example, if a user posts a complaint about the current application process, the server will suggest process improvements based on that content. 【0680】 An example of a prompt used as input to the generating AI model is a specific instruction such as, "Generate suggestions for new measures for a campaign that has seen a decline in applications." This allows users to quickly implement more accurate and effective measures. 【0681】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0682】 Step 1: 【0683】 The server receives application data via the internet. The received data includes the applicant's name, contact information, and application details. This data is stored in a database to aggregate the application information. The stored data forms the basis for future analysis and reporting. Furthermore, this information is used to create an application behavior history. 【0684】 Step 2: 【0685】 The server analyzes the application information in the database. The input is the entire contents of the database. The server uses statistical analysis algorithms to calculate trends in the number of applications and graphs the trends. It also compares the current data with past data to identify outliers and sudden fluctuations. The output is a report organized to allow the user to visually understand the application status. 【0686】 Step 3: 【0687】 The server generates a warning based on the analysis results. If the number of applications falls below the set threshold, the server receives the number of applications compared to the threshold as input. Based on this, it outputs an alert. Specifically, the alert is sent to the user's terminal as an email or system notification and includes content such as, "The number of applications is below expectations. Please check the details." 【0688】 Step 4: 【0689】 The server generates strategies to increase the number of applications. Past success stories and a generating AI model are provided as input. Based on this, specific measures to improve the number of applications are output. Examples include social media campaigns utilizing specific cultural events and incentive plans for new participants. 【0690】 Step 5: 【0691】 The server performs sentiment analysis. It uses the user's past feedback and current behavioral patterns as input. It applies an algorithm to infer emotional states and generates an action plan as a guide for participants. This makes it possible to provide personalized application promotion methods tailored to the user's emotions. 【0692】 (Application Example 2) 【0693】 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". 【0694】 In today's information-driven society, increasing the number of applicants for advertising campaigns and various recruitment activities is a crucial challenge. However, there is a lack of systems that can monitor application status in real time and implement effective countermeasures. Furthermore, traditional methods have made it difficult to accurately understand applicants' emotions and implement measures based on them. Therefore, there is a need for a system that can grasp trends in application numbers and propose quick and effective measures based on the emotional state of applicants. 【0695】 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. 【0696】 In this invention, the server includes an information receiving means for monitoring the application status in real time, an analysis means for analyzing the received information and calculating the trend in the number of applications, and an estimation engine means for analyzing emotional states and proposing measures. This enables the development of flexible and effective measures that respond to the emotions of applicants. 【0697】 "Information receiving means" refers to a system for monitoring application status in real time via the internet and collecting application data. 【0698】 An "analysis tool" is a system that analyzes received information and has the function of calculating trends and abnormal fluctuations in the number of applications. 【0699】 A "notification method" is a system that generates an alert and notifies users via electronic communication if the number of applications falls below a predetermined target value. 【0700】 The "estimation engine" is a mechanism for analyzing the emotional state of applicants and providing the information necessary when proposing measures. 【0701】 A "proposal tool" is a system that generates measures to increase the number of applications and presents appropriate solutions to users. 【0702】 The system of this invention is based on technology that aims to increase the number of applications by managing the application status and analyzing user sentiment. The server first receives application data in real time via the internet using an "information receiving means." This received data is stored in a database. The server analyzes the information in the database using an "analysis means" to calculate the trend in the number of applications and understand fluctuations. If the number of applications falls below the target number, an alert is sent to the user's terminal using a "notification means." The user receives this alert and is prompted to take prompt action. 【0703】 Furthermore, the server analyzes the user's emotional state using an "estimation engine." For this purpose, the server uses sentiment analysis software such as the Google Cloud Natural Language API. The server collects user feedback, infers their emotions, and incorporates this into its "suggestion tools," helping to propose measures to increase the number of applications. Specific measures include strengthening social media campaigns and offering incentives. 【0704】 For example, if an advertising campaign is failing to meet its target number of entries, the sentiment analysis engine might detect from user feedback that users are "dissatisfied with the current campaign." This information is then analyzed by the server, and suggestions for revising the campaign content are made as needed. 【0705】 An example of a prompt message generated using an AI model is: "The number of entries in the advertising campaign is below the target. Please conduct a sentiment analysis based on user feedback and propose measures to increase the number of entries." 【0706】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0707】 Step 1: 【0708】 The server receives information that monitors the application status in real time. Data from applicants is sent to the server via the internet and stored in the database. This input data includes applicant information and the date and time of application. 【0709】 Step 2: 【0710】 The server analyzes the information stored in the database through an analysis mechanism. Specifically, it converts the trend in the number of applications into a graph based on the timestamp of each application and applies an algorithm to detect fluctuations. As a result of this analysis process, the pattern of increase or decrease in the number of applications is output. 【0711】 Step 3: 【0712】 If the server determines through analysis that the number of applications has fallen below the set target, it will send an alert to the user using a "notification method." Specifically, this involves generating an email or system notification and sending it to the user's terminal. In this step, the condition checks for issuing the alert are the input, and the alert message is the output. 【0713】 Step 4: 【0714】 The server uses an estimation engine to obtain feedback from applicants and analyze the user's emotional state. It utilizes sentiment analysis tools such as the Google Cloud Natural Language API to infer emotions from text data. In this step, feedback data is input, and the inferred emotional state is output. 【0715】 Step 5: 【0716】 The server generates measures to increase the number of applications through suggested methods, based on the results of sentiment analysis. This process includes suggesting specific measures such as strengthening social media campaigns or offering incentives. In this step, the results of sentiment analysis are used as input, and content suggesting measures is output. 【0717】 Step 6: 【0718】 Users take action to improve their advertising campaigns based on the suggested measures provided by the server. They decide on specific measures according to the content of the measures to be implemented and use them in the deployment of the campaign. Here, the suggested measures become the input, and the improved campaign becomes the output. 【0719】 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. 【0720】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0721】 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. 【0722】 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. 【0723】 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. 【0724】 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. 【0725】 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. 【0726】 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. 【0727】 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." 【0728】 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. 【0729】 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. 【0730】 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. 【0731】 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. 【0732】 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. 【0733】 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. 【0734】 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. 【0735】 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. 【0736】 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. 【0737】 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. 【0738】 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. 【0739】 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. 【0740】 The following is further disclosed regarding the embodiments described above. 【0741】 (Claim 1) 【0742】 A data receiving method for monitoring application status in real time, 【0743】 An analytical means for analyzing received data and calculating trends in the number of applications, 【0744】 A notification method that generates an alert when the number of applications falls below a predetermined target value, 【0745】 A proposal method for suggesting measures to increase the number of applications, 【0746】 A system that includes this. 【0747】 (Claim 2) 【0748】 The system according to claim 1, wherein the notification means sends an alert via email. 【0749】 (Claim 3) 【0750】 The system according to claim 1, wherein the proposed means proposes multiple measures, including a reward system and advertising content, for increasing the number of applications. 【0751】 "Example 1" 【0752】 (Claim 1) 【0753】 A means for acquiring application status via an information processing device and storing it in a data storage area, 【0754】 A means of analyzing the stored information using statistical methods to show trends in the number of applications, 【0755】 A means for generating warning information when the set criteria are deviated from the analysis results, 【0756】 A means for transmitting warning information to an administrator terminal via a communication means, 【0757】 A method of making proposals using an artificial intelligence model that generates measures to increase the number of applications, 【0758】 A system that includes this. 【0759】 (Claim 2) 【0760】 The system according to claim 1, wherein warning information is transmitted to an administrator using a communication network. 【0761】 (Claim 3) 【0762】 The system according to claim 1, which proposes diverse reward systems and advertising methods for measures to increase the number of applications. 【0763】 "Application Example 1" 【0764】 (Claim 1) 【0765】 A data receiving method for monitoring application status in real time, 【0766】 An analytical means for analyzing received data and calculating trends in the number of applications, 【0767】 A notification method that generates an alert when the number of applications falls below a predetermined target value, 【0768】 A proposal tool that visualizes advertising effectiveness and suggests effective measures, 【0769】 A system that includes this. 【0770】 (Claim 2) 【0771】 The system according to claim 1, wherein the notification means sends an alert via email or in-app notification. 【0772】 (Claim 3) 【0773】 The system according to claim 1, wherein the proposed means proposes multiple measures, including a reward system and advertising strategies for increasing the number of applications. 【0774】 "Example 2 of combining an emotion engine" 【0775】 (Claim 1) 【0776】 Information processing means for monitoring application status in real time, 【0777】 Information analysis means for analyzing received information and calculating trends in the number of applications, 【0778】 A means of providing information that issues a warning if the number of applications falls below a set standard, 【0779】 Information generation means that presents measures to increase the number of applications, 【0780】 A sentiment analysis method that predicts users' emotions based on past opinions and behavioral data, 【0781】 A system that includes this. 【0782】 (Claim 2) 【0783】 The system according to claim 1, wherein the information provision means transmits a warning via electronic communication. 【0784】 (Claim 3) 【0785】 The system according to claim 1, wherein the information generation means presents multiple measures, including a reward system and promotional content, for increasing the number of applications. 【0786】 "Application example 2 when combining with an emotional engine" 【0787】 (Claim 1) 【0788】 A means of receiving information to monitor the application status in real time, 【0789】 An analytical method for analyzing received information and calculating trends in the number of applications, 【0790】 A notification method that generates an alert when the number of applications falls below a predetermined target value, 【0791】 An estimation engine for analyzing emotional states and proposing measures, 【0792】 A proposal method for suggesting measures to increase the number of applications, 【0793】 A system that includes this. 【0794】 (Claim 2) 【0795】 The system according to claim 1, wherein the notification means transmits an alert via electronic communication. 【0796】 (Claim 3) 【0797】 The system according to claim 1, wherein the proposed means proposes multiple measures, including a reward system and promotional content, to increase the number of applications. [Explanation of Symbols] 【0798】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data receiving method for monitoring application status in real time, An analytical means for analyzing received data and calculating trends in the number of applications, A notification method that generates an alert when the number of applications falls below a predetermined target value, A proposal method for suggesting measures to increase the number of applications, A system that includes this. [Claim 2] The system according to claim 1, wherein the notification means sends an alert via email. [Claim 3] The system according to claim 1, wherein the proposed means proposes multiple measures, including a reward system and advertising content, for increasing the number of applications.

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