system

Abstract

We provide the system. [Solution] A storage means for collecting and storing data on past scandals, A means of collecting data related to ongoing scandals from social media, A means of searching for and relating similar past cases based on the collected data, A processing means for executing a machine learning algorithm to evaluate the impact of a scandal, A means of proposing the optimal countermeasures based on the evaluation results, A system that integrates the above methods to present countermeasures for misconduct.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 recent years, with the expansion of the Internet and social media, the number of cases in which misfortunes caused by companies or individuals spread rapidly and widely, having a profound social impact, has been increasing. If inappropriate responses are made to this problem, there is a possibility of further deterioration of the brand image and economic losses. Therefore, there is a need to provide a system that can quickly and appropriately present countermeasures and minimize the impact on companies and individuals. 【Means for Solving the Problems】 【0005】 This invention provides a storage means for collecting and storing data on past misconduct cases, as well as means for collecting and analyzing data on current misconduct from social media. Next, it performs a process of searching for and associating similar past cases based on the collected data. Furthermore, it includes a processing means for evaluating the impact of misconduct using a machine learning algorithm, and a means for proposing optimal countermeasures based on the evaluation results, thereby providing a system that enables companies and individuals to quickly obtain appropriate countermeasures. This system also makes it possible to convert the impact of misconduct into monetary terms and perform performance-based compensation calculations. 【0006】 A "scandal" refers to socially problematic actions or situations that are unintentionally caused by a company or individual. 【0007】 A "database" refers to an information system used to efficiently store and manage past cases of misconduct and the resulting social reactions. 【0008】 "Social media" is a general term for platforms used to share and spread information via the internet. 【0009】 A "machine learning algorithm" refers to the logical framework of computer programs that learn patterns from data and automatically make predictions and decisions about future data. 【0010】 "Evaluation results" refer to information that shows the impact of misconduct and the results of analysis obtained through machine learning algorithms. 【0011】 "Countermeasures" refer to a series of actions and measures taken to minimize the impact of a scandal. 【0012】 "Performance-based compensation" refers to payment made in accordance with the effectiveness of the services or solutions provided. [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, the numbered 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】 This invention relates to a system that enables companies and individuals to take swift and appropriate countermeasures when misconduct occurs. Specifically, it has means for storing past misconduct data in a storage device and for collecting and analyzing data related to current misconduct in real time from social media. 【0035】 The server searches its database for similar past cases based on the collected information. This process involves analyzing data attributes and patterns and executing algorithms to identify relevant cases. Subsequently, machine learning algorithms are used to calculate and quantify the impact of the current scandal. This allows the server to quantitatively evaluate the impact and provide objective indicators. 【0036】 Furthermore, based on the evaluation results, the server generates optimal countermeasures, including methods of apology, information disclosure, and public relations activities. These countermeasures are designed to minimize damage to the user's brand image and economic costs. The server sends the generated countermeasures to the terminal, and the user receives the proposed countermeasures through the terminal. The user can then take actual action based on these measures and adjust the content as needed. 【0037】 Furthermore, the server has a function to convert the degree of impact into a monetary value in order to calculate performance-based compensation. Based on this calculated amount, users can consider an appropriate compensation system. Through these steps, users can obtain concrete and practical guidelines to minimize damage. 【0038】 As a concrete example, if a company receives a sudden surge of criticism on social media due to a product defect, this system will quickly collect data, find similar cases, and suggest appropriate countermeasures. For instance, it will refer to data from companies that have experienced similar problems in the past and propose specific steps based on insights into what responses were successful and unsuccessful. This will enable companies to quickly and effectively resolve the situation and protect their brand image. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 Users use a terminal to input information about the incident. This includes details of the incident, the people involved, and the scope of the impact. This data is sent from the terminal to the server. 【0042】 Step 2: 【0043】 The server activates an SNS data collection module to gather additional data on the scandal from the internet. It retrieves relevant posts and comments using specific keywords and hashtags. 【0044】 Step 3: 【0045】 The server compares collected data related to the misconduct with past cases in the database. It extracts similar past incidents and searches for their countermeasures and results. This information is used for subsequent processing. 【0046】 Step 4: 【0047】 The server feeds the acquired data into a machine learning algorithm to analyze the impact of the ongoing scandal. Here, the impact is quantified and output as a specific score. 【0048】 Step 5: 【0049】 The server generates the optimal response plan based on the assessed impact and past response examples. This plan includes specific methods for apologizing, procedures for public relations, and explanations to stakeholders. 【0050】 Step 6: 【0051】 The server sends the generated solution to the terminal and proposes it to the user. The user can review the received solution and customize it or provide feedback as needed. 【0052】 Step 7: 【0053】 The server converts the impact of the misconduct into monetary terms and creates a proposed performance-based compensation plan based on the results. This information is notified to the user and is considered as part of the compensation agreement. 【0054】 Step 8: 【0055】 Users send feedback to the server about the effectiveness of the countermeasures they have taken. The server uses this feedback to update its database and utilize it for learning to improve the accuracy and effectiveness of the system. 【0056】 (Example 1) 【0057】 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." 【0058】 For companies and individuals, responding quickly and appropriately to misconduct is extremely important. However, current methods make it difficult to quickly formulate countermeasures by referring to appropriate past cases. Furthermore, the process of quantitatively evaluating the impact of misconduct and formulating effective countermeasures based on that evaluation is time-consuming and often subjective. Moreover, it is difficult to translate the results of countermeasures into monetary terms and reflect them in compensation systems. There is a need to efficiently solve these problems and minimize losses caused by misconduct. 【0059】 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. 【0060】 In this invention, the server includes a storage means for collecting and storing past case information, a means for collecting information related to the current event from an information sharing platform, and a means for searching for and associating similar past cases based on the collected information. This enables companies and individuals to quickly formulate optimal countermeasures from similar past cases and to construct an appropriate reward system based on the quantitatively evaluated impact. 【0061】 "Case information" refers to data on specific events that have occurred in the past, and includes detailed background information, causes, impacts, and countermeasures. 【0062】 "Memory means" refers to a device or system for storing collected case information, and includes recording media such as databases. 【0063】 An "information sharing platform" refers to any service used on the internet that allows users to publish and view information, and includes social networking services (SNS) and blogs. 【0064】 An "event" refers to an unforeseen situation or incident related to a company or individual, and includes those whose impact is expected. 【0065】 "Impact" refers to the quantitative measure of the potential effects and scope of a particular event on a company or individual, and is an indicator used to evaluate risks and losses. 【0066】 "Converting to monetary value" refers to the process of converting the degree of impact into a specific monetary value, which is then used as an evaluation value based on a particular indicator. 【0067】 A "reward system" refers to a structure of profit sharing based on results, which is set according to the degree of influence that is evaluated. 【0068】 "Notifying a suggestion" refers to the act of informing the user of the countermeasures generated by the server, and this is done through electronic communication. 【0069】 "Gathering feedback" refers to the process of obtaining user feedback and information about usage patterns, which is used for system improvement. 【0070】 This invention is a system designed to provide rapid and appropriate responses to misconduct. Specifically, it involves the collaborative operation of a server, terminal, and user. The details are described below. 【0071】 The server first collects past case information and stores it in a database. This information is formatted into a comparable format and stored on a recording medium such as IndexedDB. Information related to current events is automatically collected in real time from the information sharing platform using an API. This allows the server to quickly acquire large amounts of data and make that information readily available for use. 【0072】 Based on the collected data, the server searches for and associates similar past cases. This process utilizes machine learning algorithms to perform pattern matching and clustering of the data to identify the most similar cases. 【0073】 Next, the server evaluates and quantifies the impact of the scandal. This evaluation process utilizes natural language processing technology to extract sentiment and opinion trends from text-based data to determine the magnitude of the impact. Based on the calculated impact level, the server generates the optimal countermeasures. Specifically, these include methods of apology, revisions to public relations activities, and procedures for information disclosure. 【0074】 The server sends these suggestions to the device, making them accessible to the user. The device then immediately delivers the information to the user via push notifications or email. The user receives the suggestions through the device, adjusts the content as needed, and takes appropriate action. 【0075】 Furthermore, the server converts the assessed impact into economic terms and proposes a reward system based on those results. This allows users to economically evaluate the impact of misconduct and make appropriate decisions. 【0076】 As a concrete example, if an organization faces criticism on an information-sharing platform regarding a product defect, this system will quickly gather relevant information, search for similar cases, and suggest appropriate countermeasures. An example of a prompt for the generating AI model could be, "What is the best course of action for a company that has received criticism on an information-sharing platform regarding a product defect?" This would enable the organization to respond quickly and effectively, maintaining brand credibility. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The server collects historical case information and stores it in a database. Historical case data is provided as input. Specifically, the server extracts data from existing digital archives and various reports, processes it, and saves it to a recording medium such as IndexedDB. Formatted case information is then prepared as output. 【0080】 Step 2: 【0081】 The server collects information related to current events in real time from the information sharing platform. The input is current posted data obtained via the API. Specifically, the server monitors specified keywords and hashtags, filters relevant information, and collects the necessary content. The output is data on ongoing events. 【0082】 Step 3: 【0083】 The server searches for similar cases based on collected past cases and current event data. The input consists of organized case information and ongoing event data. The server compares the data attributes, performs similarity analysis using machine learning algorithms, and identifies related cases. The output is a list of similar past cases. 【0084】 Step 4: 【0085】 The server evaluates and quantifies the impact of an event. Inputs include current event data and a list of similar cases. Specifically, the server utilizes natural language processing techniques to perform sentiment and trend analysis on the acquired data. The output generates quantitative indicators showing the degree of impact. 【0086】 Step 5: 【0087】 The server generates optimal countermeasures based on the evaluation results. It takes impact indicators and past success stories as input and selects the most suitable course of action. Specifically, it utilizes a generation AI model to automatically generate proposal and apology letter templates and designs each countermeasure procedure. The output is a detailed document of the generated countermeasures. 【0088】 Step 6: 【0089】 The server sends the generated countermeasures to the terminal and notifies the user. The terminal then displays the suggested information received as input on its user interface. The terminal immediately communicates the information to the user via push notification or email. As an output, rapid notification to the user is completed. 【0090】 Step 7: 【0091】 The user receives proposed countermeasures via the terminal, adjusts them as needed, and makes a decision. The countermeasures displayed on the terminal are used as input. Based on this information, the user customizes them to align with internal procedures and develops a concrete implementation plan. The final output is a revised implementation plan. 【0092】 Step 8: 【0093】 The server converts the degree of impact into monetary value and calculates the reward. The input is a quantified impact index. The server converts the impact into a specific monetary value according to the economic index and proposes a reward structure. As output, a monetary-based reward proposal is generated and provided. 【0094】 (Application Example 1) 【0095】 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." 【0096】 When a scandal occurs within a company or organization, it is crucial to minimize its impact and respond quickly and effectively. Currently, however, finding appropriate case studies for responding to scandals and quantitatively evaluating their impact is difficult, resulting in delays in countermeasures. This carries the risk of damaging brand image and incurring economic losses. 【0097】 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. 【0098】 In this invention, the server includes a storage means for collecting and storing data on past incidents of misconduct, a means for collecting information from a communication medium, and a means for searching for and associating similar past incidents based on the collected information. This enables the rapid generation of countermeasures against incidents of misconduct and improves the efficiency of information management. 【0099】 A "memory device" is a device or system that stores case data related to past misconduct and makes it possible to retrieve it as needed. 【0100】 A "communication medium" refers to a network or platform used to exchange information with other devices or systems, and in most cases, this refers to the internet. 【0101】 A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and uses that learning to evaluate and predict new data. 【0102】 "Impact level" is an indicator that quantitatively shows the magnitude of the impact that an event has on a company or organization. 【0103】 A "countermeasure" is a set of actions or plans taken to address a particular problem or situation. 【0104】 An "information terminal" is an electronic device used by users to view and manipulate information, and in most cases includes smartphones and tablets. 【0105】 To realize this invention, the server processes various data and provides a system that supports rapid and accurate response to misconduct. First, the server uses storage means to accumulate data on past incidents of misconduct. This accumulated data is retrieved as needed and used for analysis. By using means to collect relevant information in real time from communication media, information on ongoing incidents is quickly obtained. This allows the server to form a foundation for comparing current events with past cases. 【0106】 The server utilizes machine learning algorithms to evaluate the impact of collected data. This evaluation involves calculations based on a large amount of historical data, and the results are expressed as quantitative indicators. Based on these results, the optimal countermeasures are proposed. These countermeasures are notified to the user via an information terminal, and the user can take specific actions based on the proposal. This allows the user to quickly take appropriate measures to minimize the impact of the incident. 【0107】 For example, if a company suddenly receives criticism on social media, this system instantly aggregates relevant data and presents analysis results of similar cases. As a result, the person in charge can quickly obtain the most appropriate response options (e.g., issuing a direct statement, dealing with the media, etc.). 【0108】 An example of a prompt using a generative AI model is, "Please suggest the best course of action for a company to quickly respond to criticism on social media." The server manages this information comprehensively and provides users with accurate information and advice. 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The server collects data related to the scandal in real time from communication media. The input is publicly available information such as social media, and the server filters it to extract only the relevant data. The filtered data is then stored in a database. 【0112】 Step 2: 【0113】 The server uses storage methods to search for past incident data and evaluates its similarity to the collected current data. The input is the data stored in step 1 and past incident data, and the output is a list of similar incidents. Text mining and natural language processing are performed to analyze the similarity. 【0114】 Step 3: 【0115】 The server uses a machine learning algorithm to assess the impact of the current incident. The input is a list of similar cases obtained in step 2, and the impact is output as a numerical value. Here, statistical analysis is performed to calculate the risk and quantify the impact based on the results for each case. 【0116】 Step 4: 【0117】 The server generates the optimal countermeasures based on the impact assessment results. The input is the impact data obtained in step 3, and the output is a list of proposed countermeasures. This process utilizes a generative AI model to provide specific suggestions in response to prompts presented to the user. 【0118】 Step 5: 【0119】 The server notifies the user of the generated countermeasures via an information terminal. The user receives this notification and can choose a specific action. The input is a list of proposed countermeasures, and the output is the information notified to the user. It provides an interface to support the user's decision-making. 【0120】 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. 【0121】 This invention is a system for effectively managing misconduct faced by companies and individuals, and aims to provide more personalized responses by incorporating an emotion engine. This system not only includes memory means, data collection means, similar case search means, evaluation means using machine learning algorithms, and suggestion generation means, but also includes an emotion engine for recognizing the user's emotions. 【0122】 First, the user uses a terminal to input information related to the misconduct and sends it to the server. The server then collects relevant data from networks such as social media and compares it with a database of past cases stored in its memory. Next, the server identifies similar past cases of misconduct and uses a machine learning algorithm to evaluate the impact of the current misconduct based on the results. 【0123】 The key feature of this system is its emotion engine, which analyzes emotional states based on direct feedback and input data provided by the user. The emotion engine analyzes emotions from user input, and the server uses the results to generate personalized responses. This makes it possible to propose the most appropriate response for the user and minimize the impact of misconduct. 【0124】 For example, when a company announces a product defect, negative opinions and emotions may spread. In this situation, the emotion engine analyzes employee and customer reactions in real time, detecting emotions such as anger and disappointment. The server can then consider this emotional data and propose countermeasures that require particular attention. This makes it easier for companies to make strategic decisions to minimize damage, while simultaneously contributing to reducing the psychological burden on users. 【0125】 By implementing this invention, it becomes possible to not only analyze facts but also to respond flexibly based on emotional states, thereby achieving more accurate misconduct management. 【0126】 The following describes the processing flow. 【0127】 Step 1: 【0128】 The user uses a terminal to enter detailed information related to the misconduct. This includes the nature of the incident, the people involved, and the scope of its impact. This information is then sent from the terminal to the server. 【0129】 Step 2: 【0130】 The server collects data on scandals from social media and related information sources. It retrieves comments and posts using specific keywords and hashtags and stores them in relevant databases. 【0131】 Step 3: 【0132】 The server uses the collected data to search past databases for similar cases of misconduct. It identifies relevant past cases and analyzes their countermeasures and evaluation results. 【0133】 Step 4: 【0134】 The server uses machine learning algorithms to assess the impact of the current scandal. It quantitatively measures the impact based on indicators derived from the data. 【0135】 Step 5: 【0136】 The server activates an emotion engine based on user input data and recognizes the user's emotional state. It analyzes the input text and feedback to calculate an emotion score. 【0137】 Step 6: 【0138】 The server takes emotional states into account to generate personalized responses. By understanding the user's emotions, it can propose more appropriate apologies and public relations strategies. 【0139】 Step 7: 【0140】 The server sends the generated countermeasures to the terminal and presents them to the user. The user reviews the proposed countermeasures, makes adjustments as needed, and decides on the actual measures to take. 【0141】 Step 8: 【0142】 Users input feedback on the effectiveness of the countermeasures they have taken via their device and send it to the server. The server collects this feedback and uses it to learn from and improve the system. 【0143】 (Example 2) 【0144】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0145】 In the event of a scandal involving a company or individual, accurately understanding its impact and formulating swift and effective countermeasures is crucial. However, conventional methods have limitations in assessing the similarity and impact of different cases, and furthermore, in providing countermeasures that take into account the emotional state of users. 【0146】 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. 【0147】 In this invention, the server includes a recording device for collecting and storing past case information, means for collecting information related to currently ongoing cases from an information sharing network, means for searching for and associating similar past cases based on the collected information, a processing device for executing an automated learning algorithm for evaluating the impact of cases, means for generating an optimal response based on the degree of impact and emotion data, and an emotion recognition device for recognizing and analyzing the emotional state of the user. This makes it possible to generate quick and appropriate case response measures and to realize flexible responses that take the user's emotions into consideration. 【0148】 A "recording device" is a device that has the function of collecting past case information and saving it in a database. 【0149】 An "information sharing network" is an online platform that aggregates and shares information from a wide range of sources, such as social media and news websites. 【0150】 An "automated learning algorithm" is a computational method that uses machine learning techniques to analyze data patterns and evaluate the impact of events. 【0151】 A "processing device" is a device that processes collected data and has the function of evaluating the impact of misconduct and generating optimal countermeasures. 【0152】 An "emotion recognition device" is a system that analyzes emotions from data and feedback provided by the user and quantifies or categorizes those emotions. 【0153】 This invention provides a system that enables companies and individuals to obtain effective countermeasures against misconduct. The system mainly consists of servers, terminals, and generative AI models. 【0154】 The server operates by integrating multiple hardware devices and software components. The server includes a recording device that manages a database and has the function of collecting and storing historical case information. The collected information is compared with data on current cases obtained from information-sharing networks (e.g., social networking services and news sites). This allows the server to identify similar past cases and associate them with information from the information-sharing network. 【0155】 Furthermore, the server uses a processing unit that executes an automated learning algorithm to quantitatively evaluate the impact of misconduct. This evaluation utilizes machine learning techniques to quantify the impact of the incident. It is equipped with an emotion recognition device to measure emotions from data and feedback provided by users. This device clearly analyzes the user's emotional state. 【0156】 The terminal provides an interface for users to input information about a problem. Users enter detailed case information into the terminal, and this information is securely transmitted to the server. Based on the input information, the server generates the most appropriate countermeasure and presents it to the user through the terminal. 【0157】 For example, if a company discovers a defect in a new product, it can quickly input relevant information into the system using a terminal. The server collects data from social media in real time and uses a generative AI model to perform sentiment analysis and impact assessment. As a result, it generates specific strategies for problem resolution, such as an "action plan for rapid customer response and apology." 【0158】 An example of a prompt for a generative AI model would be: "Analyze customer reactions to the new product and use the sentiment engine to evaluate the level of dissatisfaction. Based on that evaluation, propose specific customer response measures." This prompt allows the AI ​​model to quickly analyze the data and propose appropriate solutions. 【0159】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0160】 Step 1: 【0161】 Users use a terminal to input detailed information about the misconduct that occurred. This information includes specific details and chronological information about the incident. This information is transmitted from the terminal to a server and securely recorded. The input data is formatted as attribute data, including the type of misconduct, those involved, and the date of occurrence. 【0162】 Step 2: 【0163】 The server collects relevant data from the information-sharing network based on the information it receives. For example, it retrieves relevant posts and comments from social media using an API. Here, a prompt is sent to the generating AI model: "Please collect recent social media posts related to the scandal." The collected data is stored in a database as case information. 【0164】 Step 3: 【0165】 The server searches past case data stored in its storage device to identify cases similar to the current incident. A similarity calculation algorithm is used to compare the current incident with past data. The input data is information about the current incident, and the output is a list of related past cases. 【0166】 Step 4: 【0167】 The server executes an automated learning algorithm to assess the impact of the incident based on collected data and similar cases. In this process, a model trained on past impact patterns is used to calculate an impact score. The input consists of similar cases and current situation data, and the output is the impact score. 【0168】 Step 5: 【0169】 The server uses an emotion recognition device to analyze the user's emotional state. It analyzes user feedback and emotional expressions on social media, and quantifies emotions using natural language processing technology. The input is collected emotional data, and the output is an emotional score categorized as positive, negative, or neutral. 【0170】 Step 6: 【0171】 The server integrates the collected data, impact assessment, and sentiment analysis results, and uses a generative AI model to generate the optimal response. In this step, the generated prompt "Please propose specific measures to solve the problem" is sent to the AI, and a proposal is generated from the results. The input is the integrated analysis data, and the output is specific response measures. 【0172】 Step 7: 【0173】 The terminal provides the user with suggestions from the server. The user reviews the suggested solutions, selects an actionable solution, and takes action. The suggested information is displayed visually through the terminal's interface. The output is a visual presentation of the solutions to the user. 【0174】 (Application Example 2) 【0175】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0176】 In modern society, businesses and organizations sometimes face unethical behavior and crises. While a swift and effective response is required in such situations, traditional methods often fail to provide flexible responses to individual circumstances. Furthermore, the involvement of emotional factors increases the complexity of the problem, making it difficult to implement appropriate countermeasures. To resolve these issues and minimize the impact of unethical behavior, personalized responses based on up-to-date information are essential. 【0177】 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. 【0178】 In this invention, the server includes a storage means for collecting and accumulating data on past cases of unethical behavior; a means for collecting information related to ongoing unethical behavior from an information sharing platform; a means for identifying and associating common past cases based on the collected information; a processing means for executing a machine learning algorithm to evaluate the impact of unethical behavior; a means for recommending the optimal course of action based on the evaluation results; and an emotion recognition means for analyzing a person's emotional state and providing individualized countermeasures. This makes it possible to quickly provide personalized countermeasures that take emotions into account depending on the situation. 【0179】 "Past unethical behavior" refers to inappropriate actions or incidents that have occurred in the past, and data related to these is accumulated and used as a reference for future countermeasures. 【0180】 "Case data" refers to a collection of information about specific situations or events recorded in the past, and serves as the basis for analysis and comparison. 【0181】 A "memory device" is a system or device for storing data and information, making it possible to retrieve and use them when needed. 【0182】 An "information sharing platform" refers to a digital environment or application that allows multiple users to send, exchange, and receive information. 【0183】 "Means of collection" refer to methods and technologies designed to collect necessary information and data, thereby efficiently gathering relevant information. 【0184】 "Common past examples" are events that occurred before the current problem or situation and share similarities, providing clues for solving new problems. 【0185】 A "machine learning algorithm" is a computational method used to learn patterns and rules from vast amounts of data and perform predictions and classifications. 【0186】 A "processing means" is a system or method for analyzing received information or input and manipulating or transforming it according to a specific purpose. 【0187】 "Optimal course of action" refers to the most effective method or solution for dealing with a particular problem situation. 【0188】 A "method of recommendation" refers to a system or process for proposing and presenting the most appropriate option based on the analyzed data. 【0189】 "Emotion recognition methods" refer to technologies and algorithms that identify and analyze a person's emotional state at any given time, based on their facial expressions, voice, written text, etc. 【0190】 To implement this invention, the server first accumulates data on past instances of unethical behavior and stores it in a memory device. Next, it collects information related to ongoing unethical behavior from an information sharing platform. Based on this data, the server identifies and associates common past cases. 【0191】 In this process, the server uses machine learning algorithms to evaluate the impact of the collected data. For example, it uses software such as Python or Tensorflow® to perform data calculations and quantify the impact on a particular problem. 【0192】 Subsequently, the server recommends the optimal strategy based on the evaluation results. In this process, emotion recognition is used to take the user's emotions into consideration. This is achieved by using natural language processing libraries (such as NLTK or spaCy) to identify human emotions from speech and text. 【0193】 As a concrete example, if a security incident occurs in a company's office, the server immediately searches for data on related unethical behavior and compares it to similar past cases. Then, using emotion recognition, it assesses the degree of distress among employees and presents guidelines for calming down to the user via their terminal. 【0194】 The generative AI model is used to improve the accuracy of data analysis and sentiment recognition in this process. An example of a prompt message would be: "A security incident has occurred in the office. Employees are panicking. Analyze their emotional state and suggest specific actions to calm them down." 【0195】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0196】 Step 1: 【0197】 The server stores data on past instances of unethical behavior in its storage system. In this step, it receives existing database information sent from the terminal, organizes it, and saves it. The input is data on instances of unethical behavior, and the output is the stored data. During this process, data processing such as data cleansing and duplicate removal is performed. 【0198】 Step 2: 【0199】 The server collects information related to ongoing unethical behavior from an information-sharing platform. The input is external information collected in real time, and the output is the latest collected data on unethical behavior. Specifically, it accesses the platform via an API, performs keyword searches, and automatically crawls related information. 【0200】 Step 3: 【0201】 The server searches for and associates common past cases based on the collected information. The input is the data obtained in Step 1 and Step 2, and the output is a list of similar past cases. For data processing, text mining techniques are used to identify cases with high similarity. 【0202】 Step 4: 【0203】 The server uses a machine learning algorithm to evaluate the impact of the collected data. The input is a list of similar past cases obtained in step 3, and the output is an evaluation score for each case. The evaluation is performed by running the trained model using TensorFlow and quantifying the impact. 【0204】 Step 5: 【0205】 The server recommends the optimal strategy based on the evaluation results. The input is the evaluation score from step 4, and the output is a list of the most effective solutions. The process involves applying a ranking algorithm to select the top-ranked proposal. 【0206】 Step 6: 【0207】 The device analyzes the user's emotions using emotion recognition technology and provides personalized responses. Input is user data such as voice and text, and output is a customized response that reflects the emotions. Emotion analysis is performed using a natural language processing engine (NLTK or spaCy). 【0208】 Step 7: 【0209】 The server executes the final process to improve the accuracy of data analysis and sentiment recognition using a generative AI model. The input is the analysis results up to step 6, and the output is the optimized individual response. It takes prompt text as input to the generative AI model and generates specific solutions. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 [Second Embodiment] 【0214】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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". 【0226】 This invention relates to a system that enables companies and individuals to take swift and appropriate countermeasures when misconduct occurs. Specifically, it has means for storing past misconduct data in a storage device and for collecting and analyzing data related to current misconduct in real time from social media. 【0227】 The server searches its database for similar past cases based on the collected information. This process involves analyzing data attributes and patterns and executing algorithms to identify relevant cases. Subsequently, machine learning algorithms are used to calculate and quantify the impact of the current scandal. This allows the server to quantitatively evaluate the impact and provide objective indicators. 【0228】 Furthermore, based on the evaluation results, the server generates optimal countermeasures, including methods of apology, information disclosure, and public relations activities. These countermeasures are designed to minimize damage to the user's brand image and economic costs. The server sends the generated countermeasures to the terminal, and the user receives the proposed countermeasures through the terminal. The user can then take actual action based on these measures and adjust the content as needed. 【0229】 Furthermore, the server has a function to convert the degree of impact into a monetary value in order to calculate performance-based compensation. Based on this calculated amount, users can consider an appropriate compensation system. Through these steps, users can obtain concrete and practical guidelines to minimize damage. 【0230】 As a concrete example, if a company receives a sudden surge of criticism on social media due to a product defect, this system will quickly collect data, find similar cases, and suggest appropriate countermeasures. For instance, it will refer to data from companies that have experienced similar problems in the past and propose specific steps based on insights into what responses were successful and unsuccessful. This will enable companies to quickly and effectively resolve the situation and protect their brand image. 【0231】 The following describes the processing flow. 【0232】 Step 1: 【0233】 Users use a terminal to input information about the incident. This includes details of the incident, the people involved, and the scope of the impact. This data is sent from the terminal to the server. 【0234】 Step 2: 【0235】 The server activates an SNS data collection module to gather additional data on the scandal from the internet. It retrieves relevant posts and comments using specific keywords and hashtags. 【0236】 Step 3: 【0237】 The server compares collected data related to the misconduct with past cases in the database. It extracts similar past incidents and searches for their countermeasures and results. This information is used for subsequent processing. 【0238】 Step 4: 【0239】 The server feeds the acquired data into a machine learning algorithm to analyze the impact of the ongoing scandal. Here, the impact is quantified and output as a specific score. 【0240】 Step 5: 【0241】 The server generates the optimal response plan based on the assessed impact and past response examples. This plan includes specific methods for apologizing, procedures for public relations, and explanations to stakeholders. 【0242】 Step 6: 【0243】 The server sends the generated solution to the terminal and proposes it to the user. The user can review the received solution and customize it or provide feedback as needed. 【0244】 Step 7: 【0245】 The server converts the impact of the misconduct into monetary terms and creates a proposed performance-based compensation plan based on the results. This information is notified to the user and is considered as part of the compensation agreement. 【0246】 Step 8: 【0247】 Users send feedback to the server about the effectiveness of the countermeasures they have taken. The server uses this feedback to update its database and utilize it for learning to improve the accuracy and effectiveness of the system. 【0248】 (Example 1) 【0249】 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." 【0250】 For companies and individuals, responding quickly and appropriately to misconduct is extremely important. However, current methods make it difficult to quickly formulate countermeasures by referring to appropriate past cases. Furthermore, the process of quantitatively evaluating the impact of misconduct and formulating effective countermeasures based on that evaluation is time-consuming and often subjective. Moreover, it is difficult to translate the results of countermeasures into monetary terms and reflect them in compensation systems. There is a need to efficiently solve these problems and minimize losses caused by misconduct. 【0251】 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. 【0252】 In this invention, the server includes a storage means for collecting and storing past case information, a means for collecting information related to the current event from an information sharing platform, and a means for searching for and associating similar past cases based on the collected information. This enables companies and individuals to quickly formulate optimal countermeasures from similar past cases and to construct an appropriate reward system based on the quantitatively evaluated impact. 【0253】 "Case information" refers to data on specific events that have occurred in the past, and includes detailed background information, causes, impacts, and countermeasures. 【0254】 "Memory means" refers to a device or system for storing collected case information, and includes recording media such as databases. 【0255】 An "information sharing platform" refers to any service used on the internet that allows users to publish and view information, and includes social networking services (SNS) and blogs. 【0256】 An "event" refers to an unforeseen situation or incident related to a company or individual, and includes those whose impact is expected. 【0257】 "Impact" refers to the quantitative measure of the potential effects and scope of a particular event on a company or individual, and is an indicator used to evaluate risks and losses. 【0258】 "Converting to monetary value" refers to the process of converting the degree of impact into a specific monetary value, which is then used as an evaluation value based on a particular indicator. 【0259】 A "reward system" refers to a structure of profit sharing based on results, which is set according to the degree of influence that is evaluated. 【0260】 "Notifying a suggestion" refers to the act of informing the user of the countermeasures generated by the server, and this is done through electronic communication. 【0261】 "Gathering feedback" refers to the process of obtaining user feedback and information about usage patterns, which is used for system improvement. 【0262】 This invention is a system designed to provide rapid and appropriate responses to misconduct. Specifically, it involves the collaborative operation of a server, terminal, and user. The details are described below. 【0263】 The server first collects past case information and stores it in a database. This information is formatted into a comparable format and stored on a recording medium such as IndexedDB. Information related to current events is automatically collected in real time from the information sharing platform using an API. This allows the server to quickly acquire large amounts of data and make that information readily available for use. 【0264】 Based on the collected data, the server searches for and associates similar past cases. This process utilizes machine learning algorithms to perform pattern matching and clustering of the data to identify the most similar cases. 【0265】 Next, the server evaluates and quantifies the impact of the scandal. This evaluation process utilizes natural language processing technology to extract sentiment and opinion trends from text-based data to determine the magnitude of the impact. Based on the calculated impact level, the server generates the optimal countermeasures. Specifically, these include methods of apology, revisions to public relations activities, and procedures for information disclosure. 【0266】 The server sends these suggestions to the device, making them accessible to the user. The device then immediately delivers the information to the user via push notifications or email. The user receives the suggestions through the device, adjusts the content as needed, and takes appropriate action. 【0267】 Furthermore, the server converts the assessed impact into economic terms and proposes a reward system based on those results. This allows users to economically evaluate the impact of misconduct and make appropriate decisions. 【0268】 As a concrete example, if an organization faces criticism on an information-sharing platform regarding a product defect, this system will quickly gather relevant information, search for similar cases, and suggest appropriate countermeasures. An example of a prompt for the generating AI model could be, "What is the best course of action for a company that has received criticism on an information-sharing platform regarding a product defect?" This would enable the organization to respond quickly and effectively, maintaining brand credibility. 【0269】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0270】 Step 1: 【0271】 The server collects historical case information and stores it in a database. Historical case data is provided as input. Specifically, the server extracts data from existing digital archives and various reports, processes it, and saves it to a recording medium such as IndexedDB. Formatted case information is then prepared as output. 【0272】 Step 2: 【0273】 The server collects information related to current events in real time from the information sharing platform. The input is current posted data obtained via the API. Specifically, the server monitors specified keywords and hashtags, filters relevant information, and collects the necessary content. The output is data on ongoing events. 【0274】 Step 3: 【0275】 The server searches for similar cases based on collected past cases and current event data. The input consists of organized case information and ongoing event data. The server compares the data attributes, performs similarity analysis using machine learning algorithms, and identifies related cases. The output is a list of similar past cases. 【0276】 Step 4: 【0277】 The server evaluates and quantifies the impact of the event. As inputs, the current event data and a list of similar cases are used. As specific processing, the server makes full use of natural language processing technology to perform sentiment analysis and trend analysis on the acquired data. As output, a quantitative indicator indicating the impact is generated. 【0278】 Step 5: 【0279】 The server generates an optimal countermeasure based on the evaluation results. Using the impact indicator and past successful cases as inputs, the optimal measure is selected based on them. As specific operations, a generation AI model is utilized to automatically generate proposal documents and apology letter templates, and each response procedure is designed. The output is a detailed document of the generated countermeasure. 【0280】 Step 6: 【0281】 The server transmits the generated countermeasure to the terminal and notifies the user. Specifically, the terminal displays the received proposal information on the user interface. The terminal immediately contacts the user with the information via push notification or email. As output, a prompt notification to the user is completed. 【0282】 Step 7: 【0283】 The user receives the proposed countermeasure via the terminal, adjusts the content as necessary, and makes a decision. As input, the countermeasure displayed on the terminal is utilized. The user customizes this information to match the internal procedures and formulates a specific implementation plan. As the final output, a revised countermeasure execution plan is formulated. 【0284】 Step 8: 【0285】 The server converts the degree of impact into an amount of money and calculates the reward. The input is a quantified impact indicator. The server converts the degree of impact into a specific amount of money according to economic indicators and proposes a reward system. As output, a money-based reward proposal is generated and provided. 【0286】 (Application Example 1) 【0287】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0288】 When an incident occurs in a company or organization, it is required to minimize the impact and respond quickly and effectively. Currently, it is difficult to search for appropriate cases in incident response and quantitatively evaluate the degree of impact, resulting in a problem that the response measures are delayed. As a result, there is a risk of deterioration of the brand image and economic losses. 【0289】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0290】 In this invention, the server includes a storage means for collecting and storing past incident data of incidents, a means for collecting information from a communication medium, and a means for searching for and associating similar past cases based on the collected information. This enables the generation of prompt response measures for incidents and the improvement of information management efficiency. 【0291】 The "storage means" is a device or system that accumulates case data related to past incidents and can retrieve it as needed. 【0292】 The "communication medium" refers to a network or platform for exchanging information with other devices or systems, and in many cases, the Internet is used. 【0293】 A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and uses that learning to evaluate and predict new data. 【0294】 "Impact level" is an indicator that quantitatively shows the magnitude of the impact that an event has on a company or organization. 【0295】 A "countermeasure" is a set of actions or plans taken to address a particular problem or situation. 【0296】 An "information terminal" is an electronic device used by users to view and manipulate information, and in most cases includes smartphones and tablets. 【0297】 To realize this invention, the server processes various data and provides a system that supports rapid and accurate response to misconduct. First, the server uses storage means to accumulate data on past incidents of misconduct. This accumulated data is retrieved as needed and used for analysis. By using means to collect relevant information in real time from communication media, information on ongoing incidents is quickly obtained. This allows the server to form a foundation for comparing current events with past cases. 【0298】 The server utilizes machine learning algorithms to evaluate the impact of collected data. This evaluation involves calculations based on a large amount of historical data, and the results are expressed as quantitative indicators. Based on these results, the optimal countermeasures are proposed. These countermeasures are notified to the user via an information terminal, and the user can take specific actions based on the proposal. This allows the user to quickly take appropriate measures to minimize the impact of the incident. 【0299】 For example, if a company suddenly receives criticism on social media, this system instantly aggregates relevant data and presents analysis results of similar cases. As a result, the person in charge can quickly obtain the most appropriate response options (e.g., issuing a direct statement, dealing with the media, etc.). 【0300】 As an example of a prompt sentence using the generated AI model, "Please propose the optimal measures for quickly responding to criticism on the company's SNS" can be cited. The server comprehensively manages this information and provides accurate information and advice to users. 【0301】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0302】 Step 1: 【0303】 The server collects data related to incidents in real time from the communication medium. The input is public information such as social media, and the server filters this to extract only the relevant data. The filtered data is stored in the database. 【0304】 Step 2: 【0305】 The server utilizes the storage means to search for past incident case data and evaluate the similarity with the currently collected data. The input is the data stored in Step 1 and the past case data, and the output is a list of similar cases. To analyze the similarity, text mining and natural language processing are executed. 【0306】 Step 3: 【0307】 The server uses a machine learning algorithm to evaluate the impact level of the current incident. The input is the list of similar cases obtained in Step 2, and the impact level is output as a numerical value. Here, the risk is calculated based on the results of each case, and a statistical analysis is performed to quantify the impact level. 【0308】 Step 4: 【0309】 The server generates the optimal countermeasures based on the impact assessment results. The input is the impact data obtained in step 3, and the output is a list of proposed countermeasures. This process utilizes a generative AI model to provide specific suggestions in response to prompts presented to the user. 【0310】 Step 5: 【0311】 The server notifies the user of the generated countermeasures via an information terminal. The user receives this notification and can choose a specific action. The input is a list of proposed countermeasures, and the output is the information notified to the user. It provides an interface to support the user's decision-making. 【0312】 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. 【0313】 This invention is a system for effectively managing misconduct faced by companies and individuals, and aims to provide more personalized responses by incorporating an emotion engine. This system not only includes memory means, data collection means, similar case search means, evaluation means using machine learning algorithms, and suggestion generation means, but also includes an emotion engine for recognizing the user's emotions. 【0314】 First, the user uses a terminal to input information related to the misconduct and sends it to the server. The server then collects relevant data from networks such as social media and compares it with a database of past cases stored in its memory. Next, the server identifies similar past cases of misconduct and uses a machine learning algorithm to evaluate the impact of the current misconduct based on the results. 【0315】 The key feature of this system is its emotion engine, which analyzes emotional states based on direct feedback and input data provided by the user. The emotion engine analyzes emotions from user input, and the server uses the results to generate personalized responses. This makes it possible to propose the most appropriate response for the user and minimize the impact of misconduct. 【0316】 For example, when a company announces a product defect, negative opinions and emotions may spread. In this situation, the emotion engine analyzes employee and customer reactions in real time, detecting emotions such as anger and disappointment. The server can then consider this emotional data and propose countermeasures that require particular attention. This makes it easier for companies to make strategic decisions to minimize damage, while simultaneously contributing to reducing the psychological burden on users. 【0317】 By implementing this invention, it becomes possible to not only analyze facts but also to respond flexibly based on emotional states, thereby achieving more accurate misconduct management. 【0318】 The following describes the processing flow. 【0319】 Step 1: 【0320】 The user uses a terminal to enter detailed information related to the misconduct. This includes the nature of the incident, the people involved, and the scope of its impact. This information is then sent from the terminal to the server. 【0321】 Step 2: 【0322】 The server collects data on scandals from social media and related information sources. It retrieves comments and posts using specific keywords and hashtags and stores them in relevant databases. 【0323】 Step 3: 【0324】 The server uses the collected data to search past databases for similar cases of misconduct. It identifies relevant past cases and analyzes their countermeasures and evaluation results. 【0325】 Step 4: 【0326】 The server uses machine learning algorithms to assess the impact of the current scandal. It quantitatively measures the impact based on indicators derived from the data. 【0327】 Step 5: 【0328】 The server activates an emotion engine based on user input data and recognizes the user's emotional state. It analyzes the input text and feedback to calculate an emotion score. 【0329】 Step 6: 【0330】 The server takes emotional states into account to generate personalized responses. By understanding the user's emotions, it can propose more appropriate apologies and public relations strategies. 【0331】 Step 7: 【0332】 The server sends the generated countermeasures to the terminal and presents them to the user. The user reviews the proposed countermeasures, makes adjustments as needed, and decides on the actual measures to take. 【0333】 Step 8: 【0334】 Users input feedback on the effectiveness of the countermeasures they have taken via their device and send it to the server. The server collects this feedback and uses it to learn from and improve the system. 【0335】 (Example 2) 【0336】 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". 【0337】 In the event of a scandal involving a company or individual, accurately understanding its impact and formulating swift and effective countermeasures is crucial. However, conventional methods have limitations in assessing the similarity and impact of different cases, and furthermore, in providing countermeasures that take into account the emotional state of users. 【0338】 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. 【0339】 In this invention, the server includes a recording device for collecting and storing past case information, means for collecting information related to currently ongoing cases from an information sharing network, means for searching for and associating similar past cases based on the collected information, a processing device for executing an automated learning algorithm for evaluating the impact of cases, means for generating an optimal response based on the degree of impact and emotion data, and an emotion recognition device for recognizing and analyzing the emotional state of the user. This makes it possible to generate quick and appropriate case response measures and to realize flexible responses that take the user's emotions into consideration. 【0340】 A "recording device" is a device that has the function of collecting past case information and saving it in a database. 【0341】 An "information sharing network" is an online platform that aggregates and shares information from a wide range of sources, such as social media and news websites. 【0342】 An "automated learning algorithm" is a computational method that uses machine learning techniques to analyze data patterns and evaluate the impact of events. 【0343】 A "processing device" is a device that processes collected data and has the function of evaluating the impact of misconduct and generating optimal countermeasures. 【0344】 An "emotion recognition device" is a system that analyzes emotions from data and feedback provided by the user and quantifies or categorizes those emotions. 【0345】 This invention provides a system that enables companies and individuals to obtain effective countermeasures against misconduct. The system mainly consists of servers, terminals, and generative AI models. 【0346】 The server operates by integrating multiple hardware devices and software components. The server includes a recording device that manages a database and has the function of collecting and storing historical case information. The collected information is compared with data on current cases obtained from information-sharing networks (e.g., social networking services and news sites). This allows the server to identify similar past cases and associate them with information from the information-sharing network. 【0347】 Furthermore, the server uses a processing unit that executes an automated learning algorithm to quantitatively evaluate the impact of misconduct. This evaluation utilizes machine learning techniques to quantify the impact of the incident. It is equipped with an emotion recognition device to measure emotions from data and feedback provided by users. This device clearly analyzes the user's emotional state. 【0348】 The terminal provides an interface for users to input information about a problem. Users enter detailed case information into the terminal, and this information is securely transmitted to the server. Based on the input information, the server generates the most appropriate countermeasure and presents it to the user through the terminal. 【0349】 For example, if a company discovers a defect in a new product, it can quickly input relevant information into the system using a terminal. The server collects data from social media in real time and uses a generative AI model to perform sentiment analysis and impact assessment. As a result, it generates specific strategies for problem resolution, such as an "action plan for rapid customer response and apology." 【0350】 An example of a prompt for a generative AI model would be: "Analyze customer reactions to the new product and use the sentiment engine to evaluate the level of dissatisfaction. Based on that evaluation, propose specific customer response measures." This prompt allows the AI ​​model to quickly analyze the data and propose appropriate solutions. 【0351】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0352】 Step 1: 【0353】 Users use a terminal to input detailed information about the misconduct that occurred. This information includes specific details and chronological information about the incident. This information is transmitted from the terminal to a server and securely recorded. The input data is formatted as attribute data, including the type of misconduct, those involved, and the date of occurrence. 【0354】 Step 2: 【0355】 The server collects relevant data from the information-sharing network based on the information it receives. For example, it retrieves relevant posts and comments from social media using an API. Here, a prompt is sent to the generating AI model: "Please collect recent social media posts related to the scandal." The collected data is stored in a database as case information. 【0356】 Step 3: 【0357】 The server searches past case data stored in its storage device to identify cases similar to the current incident. A similarity calculation algorithm is used to compare the current incident with past data. The input data is information about the current incident, and the output is a list of related past cases. 【0358】 Step 4: 【0359】 The server executes an automated learning algorithm to assess the impact of the incident based on collected data and similar cases. In this process, a model trained on past impact patterns is used to calculate an impact score. The input consists of similar cases and current situation data, and the output is the impact score. 【0360】 Step 5: 【0361】 The server uses an emotion recognition device to analyze the user's emotional state. It analyzes user feedback and emotional expressions on social media, and quantifies emotions using natural language processing technology. The input is collected emotional data, and the output is an emotional score categorized as positive, negative, or neutral. 【0362】 Step 6: 【0363】 The server integrates the collected data, impact assessment, and sentiment analysis results, and uses a generative AI model to generate the optimal response. In this step, the generated prompt "Please propose specific measures to solve the problem" is sent to the AI, and a proposal is generated from the results. The input is the integrated analysis data, and the output is specific response measures. 【0364】 Step 7: 【0365】 The terminal provides the user with suggestions from the server. The user reviews the suggested solutions, selects an actionable solution, and takes action. The suggested information is displayed visually through the terminal's interface. The output is a visual presentation of the solutions to the user. 【0366】 (Application Example 2) 【0367】 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." 【0368】 In modern society, businesses and organizations sometimes face unethical behavior and crises. While a swift and effective response is required in such situations, traditional methods often fail to provide flexible responses to individual circumstances. Furthermore, the involvement of emotional factors increases the complexity of the problem, making it difficult to implement appropriate countermeasures. To resolve these issues and minimize the impact of unethical behavior, personalized responses based on up-to-date information are essential. 【0369】 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. 【0370】 In this invention, the server includes a storage means for collecting and accumulating data on past cases of unethical behavior; a means for collecting information related to ongoing unethical behavior from an information sharing platform; a means for identifying and associating common past cases based on the collected information; a processing means for executing a machine learning algorithm to evaluate the impact of unethical behavior; a means for recommending the optimal course of action based on the evaluation results; and an emotion recognition means for analyzing a person's emotional state and providing individualized countermeasures. This makes it possible to quickly provide personalized countermeasures that take emotions into account depending on the situation. 【0371】 "Past unethical behavior" refers to inappropriate actions or incidents that have occurred in the past, and data related to these is accumulated and used as a reference for future countermeasures. 【0372】 "Case data" refers to a collection of information about specific situations or events recorded in the past, and serves as the basis for analysis and comparison. 【0373】 A "memory device" is a system or device for storing data and information, making it possible to retrieve and use them when needed. 【0374】 An "information sharing platform" refers to a digital environment or application that allows multiple users to send, exchange, and receive information. 【0375】 "Means of collection" refer to methods and technologies designed to collect necessary information and data, thereby efficiently gathering relevant information. 【0376】 "Common past examples" are events that occurred before the current problem or situation and share similarities, providing clues for solving new problems. 【0377】 A "machine learning algorithm" is a computational method used to learn patterns and rules from vast amounts of data and perform predictions and classifications. 【0378】 A "processing means" is a system or method for analyzing received information or input and manipulating or transforming it according to a specific purpose. 【0379】 "Optimal course of action" refers to the most effective method or solution for dealing with a particular problem situation. 【0380】 A "method of recommendation" refers to a system or process for proposing and presenting the most appropriate option based on the analyzed data. 【0381】 "Emotion recognition methods" refer to technologies and algorithms that identify and analyze a person's emotional state at any given time, based on their facial expressions, voice, written text, etc. 【0382】 To implement this invention, the server first accumulates data on past instances of unethical behavior and stores it in a memory device. Next, it collects information related to ongoing unethical behavior from an information sharing platform. Based on this data, the server identifies and associates common past cases. 【0383】 In this process, the server uses machine learning algorithms to evaluate the impact of the collected data. For example, it uses software such as Python or TensorFlow to perform data calculations and quantify the impact on a specific problem. 【0384】 Subsequently, the server recommends the optimal strategy based on the evaluation results. In this process, emotion recognition is used to take the user's emotions into consideration. This is achieved by using natural language processing libraries (such as NLTK or spaCy) to identify human emotions from speech and text. 【0385】 As a concrete example, if a security incident occurs in a company's office, the server immediately searches for data on related unethical behavior and compares it to similar past cases. Then, using emotion recognition, it assesses the degree of distress among employees and presents guidelines for calming down to the user via their terminal. 【0386】 The generative AI model is used to improve the accuracy of data analysis and sentiment recognition in this process. An example of a prompt message would be: "A security incident has occurred in the office. Employees are panicking. Analyze their emotional state and suggest specific actions to calm them down." 【0387】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0388】 Step 1: 【0389】 The server stores data on past instances of unethical behavior in its storage system. In this step, it receives existing database information sent from the terminal, organizes it, and saves it. The input is data on instances of unethical behavior, and the output is the stored data. During this process, data processing such as data cleansing and duplicate removal is performed. 【0390】 Step 2: 【0391】 The server collects information related to ongoing unethical behavior from an information-sharing platform. The input is external information collected in real time, and the output is the latest collected data on unethical behavior. Specifically, it accesses the platform via an API, performs keyword searches, and automatically crawls related information. 【0392】 Step 3: 【0393】 The server searches for and associates common past cases based on the collected information. The input is the data obtained in Step 1 and Step 2, and the output is a list of similar past cases. For data processing, text mining techniques are used to identify cases with high similarity. 【0394】 Step 4: 【0395】 The server uses a machine learning algorithm to evaluate the impact of the collected data. The input is a list of similar past cases obtained in step 3, and the output is an evaluation score for each case. The evaluation is performed by running the trained model using TensorFlow and quantifying the impact. 【0396】 Step 5: 【0397】 The server recommends the optimal strategy based on the evaluation results. The input is the evaluation score from step 4, and the output is a list of the most effective solutions. The process involves applying a ranking algorithm to select the top-ranked proposal. 【0398】 Step 6: 【0399】 The device analyzes the user's emotions using emotion recognition technology and provides personalized responses. Input is user data such as voice and text, and output is a customized response that reflects the emotions. Emotion analysis is performed using a natural language processing engine (NLTK or spaCy). 【0400】 Step 7: 【0401】 The server executes the final process to improve the accuracy of data analysis and sentiment recognition using a generative AI model. The input is the analysis results up to step 6, and the output is the optimized individual response. It takes prompt text as input to the generative AI model and generates specific solutions. 【0402】 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. 【0403】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0404】 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. 【0405】 [Third Embodiment] 【0406】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0407】 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. 【0408】 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). 【0409】 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. 【0410】 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. 【0411】 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). 【0412】 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. 【0413】 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. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 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". 【0418】 This invention relates to a system that enables companies and individuals to take swift and appropriate countermeasures when misconduct occurs. Specifically, it has means for storing past misconduct data in a storage device and for collecting and analyzing data related to current misconduct in real time from social media. 【0419】 The server searches its database for similar past cases based on the collected information. This process involves analyzing data attributes and patterns and executing algorithms to identify relevant cases. Subsequently, machine learning algorithms are used to calculate and quantify the impact of the current scandal. This allows the server to quantitatively evaluate the impact and provide objective indicators. 【0420】 Furthermore, based on the evaluation results, the server generates optimal countermeasures, including methods of apology, information disclosure, and public relations activities. These countermeasures are designed to minimize damage to the user's brand image and economic costs. The server sends the generated countermeasures to the terminal, and the user receives the proposed countermeasures through the terminal. The user can then take actual action based on these measures and adjust the content as needed. 【0421】 Furthermore, the server has a function to convert the degree of impact into a monetary value in order to calculate performance-based compensation. Based on this calculated amount, users can consider an appropriate compensation system. Through these steps, users can obtain concrete and practical guidelines to minimize damage. 【0422】 As a concrete example, if a company receives a sudden surge of criticism on social media due to a product defect, this system will quickly collect data, find similar cases, and suggest appropriate countermeasures. For instance, it will refer to data from companies that have experienced similar problems in the past and propose specific steps based on insights into what responses were successful and unsuccessful. This will enable companies to quickly and effectively resolve the situation and protect their brand image. 【0423】 The following describes the processing flow. 【0424】 Step 1: 【0425】 Users use a terminal to input information about the incident. This includes details of the incident, the people involved, and the scope of the impact. This data is sent from the terminal to the server. 【0426】 Step 2: 【0427】 The server activates an SNS data collection module to gather additional data on the scandal from the internet. It retrieves relevant posts and comments using specific keywords and hashtags. 【0428】 Step 3: 【0429】 The server compares collected data related to the misconduct with past cases in the database. It extracts similar past incidents and searches for their countermeasures and results. This information is used for subsequent processing. 【0430】 Step 4: 【0431】 The server feeds the acquired data into a machine learning algorithm to analyze the impact of the ongoing scandal. Here, the impact is quantified and output as a specific score. 【0432】 Step 5: 【0433】 The server generates the optimal response plan based on the assessed impact and past response examples. This plan includes specific methods for apologizing, procedures for public relations, and explanations to stakeholders. 【0434】 Step 6: 【0435】 The server sends the generated solution to the terminal and proposes it to the user. The user can review the received solution and customize it or provide feedback as needed. 【0436】 Step 7: 【0437】 The server converts the impact of the misconduct into monetary terms and creates a proposed performance-based compensation plan based on the results. This information is notified to the user and is considered as part of the compensation agreement. 【0438】 Step 8: 【0439】 Users send feedback to the server about the effectiveness of the countermeasures they have taken. The server uses this feedback to update its database and utilize it for learning to improve the accuracy and effectiveness of the system. 【0440】 (Example 1) 【0441】 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." 【0442】 For companies and individuals, responding quickly and appropriately to misconduct is extremely important. However, current methods make it difficult to quickly formulate countermeasures by referring to appropriate past cases. Furthermore, the process of quantitatively evaluating the impact of misconduct and formulating effective countermeasures based on that evaluation is time-consuming and often subjective. Moreover, it is difficult to translate the results of countermeasures into monetary terms and reflect them in compensation systems. There is a need to efficiently solve these problems and minimize losses caused by misconduct. 【0443】 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. 【0444】 In this invention, the server includes a storage means for collecting and storing past case information, a means for collecting information related to the current event from an information sharing platform, and a means for searching for and associating similar past cases based on the collected information. This enables companies and individuals to quickly formulate optimal countermeasures from similar past cases and to construct an appropriate reward system based on the quantitatively evaluated impact. 【0445】 "Case information" refers to data on specific events that have occurred in the past, and includes detailed background information, causes, impacts, and countermeasures. 【0446】 "Memory means" refers to a device or system for storing collected case information, and includes recording media such as databases. 【0447】 An "information sharing platform" refers to any service used on the internet that allows users to publish and view information, and includes social networking services (SNS) and blogs. 【0448】 An "event" refers to an unforeseen situation or incident related to a company or individual, and includes those whose impact is expected. 【0449】 "Impact" refers to the quantitative measure of the potential effects and scope of a particular event on a company or individual, and is an indicator used to evaluate risks and losses. 【0450】 "Converting to monetary value" refers to the process of converting the degree of impact into a specific monetary value, which is then used as an evaluation value based on a particular indicator. 【0451】 A "reward system" refers to a structure of profit sharing based on results, which is set according to the degree of influence that is evaluated. 【0452】 "Notifying a suggestion" refers to the act of informing the user of the countermeasures generated by the server, and this is done through electronic communication. 【0453】 "Gathering feedback" refers to the process of obtaining user feedback and information about usage patterns, which is used for system improvement. 【0454】 This invention is a system designed to provide rapid and appropriate responses to misconduct. Specifically, it involves the collaborative operation of a server, terminal, and user. The details are described below. 【0455】 The server first collects past case information and stores it in a database. This information is formatted into a comparable format and stored on a recording medium such as IndexedDB. Information related to current events is automatically collected in real time from the information sharing platform using an API. This allows the server to quickly acquire large amounts of data and make that information readily available for use. 【0456】 Based on the collected data, the server searches for and associates similar past cases. This process utilizes machine learning algorithms to perform pattern matching and clustering of the data to identify the most similar cases. 【0457】 Next, the server evaluates and quantifies the impact of the scandal. This evaluation process utilizes natural language processing technology to extract sentiment and opinion trends from text-based data to determine the magnitude of the impact. Based on the calculated impact level, the server generates the optimal countermeasures. Specifically, these include methods of apology, revisions to public relations activities, and procedures for information disclosure. 【0458】 The server sends these suggestions to the device, making them accessible to the user. The device then immediately delivers the information to the user via push notifications or email. The user receives the suggestions through the device, adjusts the content as needed, and takes appropriate action. 【0459】 Furthermore, the server converts the assessed impact into economic terms and proposes a reward system based on those results. This allows users to economically evaluate the impact of misconduct and make appropriate decisions. 【0460】 As a concrete example, if an organization faces criticism on an information-sharing platform regarding a product defect, this system will quickly gather relevant information, search for similar cases, and suggest appropriate countermeasures. An example of a prompt for the generating AI model could be, "What is the best course of action for a company that has received criticism on an information-sharing platform regarding a product defect?" This would enable the organization to respond quickly and effectively, maintaining brand credibility. 【0461】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0462】 Step 1: 【0463】 The server collects historical case information and stores it in a database. Historical case data is provided as input. Specifically, the server extracts data from existing digital archives and various reports, processes it, and saves it to a recording medium such as IndexedDB. Formatted case information is then prepared as output. 【0464】 Step 2: 【0465】 The server collects information related to current events in real time from the information sharing platform. The input is current posted data obtained via the API. Specifically, the server monitors specified keywords and hashtags, filters relevant information, and collects the necessary content. The output is data on ongoing events. 【0466】 Step 3: 【0467】 The server searches for similar cases based on collected past cases and current event data. The input consists of organized case information and ongoing event data. The server compares the data attributes, performs similarity analysis using machine learning algorithms, and identifies related cases. The output is a list of similar past cases. 【0468】 Step 4: 【0469】 The server evaluates and quantifies the impact of an event. Inputs include current event data and a list of similar cases. Specifically, the server utilizes natural language processing techniques to perform sentiment and trend analysis on the acquired data. The output generates quantitative indicators showing the degree of impact. 【0470】 Step 5: 【0471】 The server generates optimal countermeasures based on the evaluation results. It takes impact indicators and past success stories as input and selects the most suitable course of action. Specifically, it utilizes a generation AI model to automatically generate proposal and apology letter templates and designs each countermeasure procedure. The output is a detailed document of the generated countermeasures. 【0472】 Step 6: 【0473】 The server sends the generated countermeasures to the terminal and notifies the user. The terminal then displays the suggested information received as input on its user interface. The terminal immediately communicates the information to the user via push notification or email. As an output, rapid notification to the user is completed. 【0474】 Step 7: 【0475】 The user receives proposed countermeasures via the terminal, adjusts them as needed, and makes a decision. The countermeasures displayed on the terminal are used as input. Based on this information, the user customizes them to align with internal procedures and develops a concrete implementation plan. The final output is a revised implementation plan. 【0476】 Step 8: 【0477】 The server converts the degree of impact into monetary value and calculates the reward. The input is a quantified impact index. The server converts the impact into a specific monetary value according to the economic index and proposes a reward structure. As output, a monetary-based reward proposal is generated and provided. 【0478】 (Application Example 1) 【0479】 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." 【0480】 When a scandal occurs within a company or organization, it is crucial to minimize its impact and respond quickly and effectively. Currently, however, finding appropriate case studies for responding to scandals and quantitatively evaluating their impact is difficult, resulting in delays in countermeasures. This carries the risk of damaging brand image and incurring economic losses. 【0481】 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. 【0482】 In this invention, the server includes a storage means for collecting and storing data on past incidents of misconduct, a means for collecting information from a communication medium, and a means for searching for and associating similar past incidents based on the collected information. This enables the rapid generation of countermeasures against incidents of misconduct and improves the efficiency of information management. 【0483】 A "memory device" is a device or system that stores case data related to past misconduct and makes it possible to retrieve it as needed. 【0484】 A "communication medium" refers to a network or platform used to exchange information with other devices or systems, and in most cases, this refers to the internet. 【0485】 A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and uses that learning to evaluate and predict new data. 【0486】 "Impact level" is an indicator that quantitatively shows the magnitude of the impact that an event has on a company or organization. 【0487】 A "countermeasure" is a set of actions or plans taken to address a particular problem or situation. 【0488】 An "information terminal" is an electronic device used by users to view and manipulate information, and in most cases includes smartphones and tablets. 【0489】 To realize this invention, the server processes various data and provides a system that supports rapid and accurate response to misconduct. First, the server uses storage means to accumulate data on past incidents of misconduct. This accumulated data is retrieved as needed and used for analysis. By using means to collect relevant information in real time from communication media, information on ongoing incidents is quickly obtained. This allows the server to form a foundation for comparing current events with past cases. 【0490】 The server utilizes machine learning algorithms to evaluate the impact of collected data. This evaluation involves calculations based on a large amount of historical data, and the results are expressed as quantitative indicators. Based on these results, the optimal countermeasures are proposed. These countermeasures are notified to the user via an information terminal, and the user can take specific actions based on the proposal. This allows the user to quickly take appropriate measures to minimize the impact of the incident. 【0491】 For example, if a company suddenly receives criticism on social media, this system instantly aggregates relevant data and presents analysis results of similar cases. As a result, the person in charge can quickly obtain the most appropriate response options (e.g., issuing a direct statement, dealing with the media, etc.). 【0492】 An example of a prompt using a generative AI model is, "Please suggest the best course of action for a company to quickly respond to criticism on social media." The server manages this information comprehensively and provides users with accurate information and advice. 【0493】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0494】 Step 1: 【0495】 The server collects data related to the scandal in real time from communication media. The input is publicly available information such as social media, and the server filters it to extract only the relevant data. The filtered data is then stored in a database. 【0496】 Step 2: 【0497】 The server uses storage methods to search for past incident data and evaluates its similarity to the collected current data. The input is the data stored in step 1 and past incident data, and the output is a list of similar incidents. Text mining and natural language processing are performed to analyze the similarity. 【0498】 Step 3: 【0499】 The server uses a machine learning algorithm to assess the impact of the current incident. The input is a list of similar cases obtained in step 2, and the impact is output as a numerical value. Here, statistical analysis is performed to calculate the risk and quantify the impact based on the results for each case. 【0500】 Step 4: 【0501】 The server generates the optimal countermeasures based on the impact assessment results. The input is the impact data obtained in step 3, and the output is a list of proposed countermeasures. This process utilizes a generative AI model to provide specific suggestions in response to prompts presented to the user. 【0502】 Step 5: 【0503】 The server notifies the user of the generated countermeasures via an information terminal. The user receives this notification and can choose a specific action. The input is a list of proposed countermeasures, and the output is the information notified to the user. It provides an interface to support the user's decision-making. 【0504】 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. 【0505】 This invention is a system for effectively managing misconduct faced by companies and individuals, and aims to provide more personalized responses by incorporating an emotion engine. This system not only includes memory means, data collection means, similar case search means, evaluation means using machine learning algorithms, and suggestion generation means, but also includes an emotion engine for recognizing the user's emotions. 【0506】 First, the user uses a terminal to input information related to the misconduct and sends it to the server. The server then collects relevant data from networks such as social media and compares it with a database of past cases stored in its memory. Next, the server identifies similar past cases of misconduct and uses a machine learning algorithm to evaluate the impact of the current misconduct based on the results. 【0507】 The key feature of this system is its emotion engine, which analyzes emotional states based on direct feedback and input data provided by the user. The emotion engine analyzes emotions from user input, and the server uses the results to generate personalized responses. This makes it possible to propose the most appropriate response for the user and minimize the impact of misconduct. 【0508】 For example, when a company announces a product defect, negative opinions and emotions may spread. In this situation, the emotion engine analyzes employee and customer reactions in real time, detecting emotions such as anger and disappointment. The server can then consider this emotional data and propose countermeasures that require particular attention. This makes it easier for companies to make strategic decisions to minimize damage, while simultaneously contributing to reducing the psychological burden on users. 【0509】 By implementing this invention, it becomes possible to not only analyze facts but also to respond flexibly based on emotional states, thereby achieving more accurate misconduct management. 【0510】 The following describes the processing flow. 【0511】 Step 1: 【0512】 The user uses a terminal to enter detailed information related to the misconduct. This includes the nature of the incident, the people involved, and the scope of its impact. This information is then sent from the terminal to the server. 【0513】 Step 2: 【0514】 The server collects data on scandals from social media and related information sources. It retrieves comments and posts using specific keywords and hashtags and stores them in relevant databases. 【0515】 Step 3: 【0516】 The server uses the collected data to search past databases for similar cases of misconduct. It identifies relevant past cases and analyzes their countermeasures and evaluation results. 【0517】 Step 4: 【0518】 The server uses machine learning algorithms to assess the impact of the current scandal. It quantitatively measures the impact based on indicators derived from the data. 【0519】 Step 5: 【0520】 The server activates an emotion engine based on user input data and recognizes the user's emotional state. It analyzes the input text and feedback to calculate an emotion score. 【0521】 Step 6: 【0522】 The server takes emotional states into account to generate personalized responses. By understanding the user's emotions, it can propose more appropriate apologies and public relations strategies. 【0523】 Step 7: 【0524】 The server sends the generated countermeasures to the terminal and presents them to the user. The user reviews the proposed countermeasures, makes adjustments as needed, and decides on the actual measures to take. 【0525】 Step 8: 【0526】 Users input feedback on the effectiveness of the countermeasures they have taken via their device and send it to the server. The server collects this feedback and uses it to learn from and improve the system. 【0527】 (Example 2) 【0528】 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." 【0529】 In the event of a scandal involving a company or individual, accurately understanding its impact and formulating swift and effective countermeasures is crucial. However, conventional methods have limitations in assessing the similarity and impact of different cases, and furthermore, in providing countermeasures that take into account the emotional state of users. 【0530】 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. 【0531】 In this invention, the server includes a recording device for collecting and storing past case information, means for collecting information related to currently ongoing cases from an information sharing network, means for searching for and associating similar past cases based on the collected information, a processing device for executing an automated learning algorithm for evaluating the impact of cases, means for generating an optimal response based on the degree of impact and emotion data, and an emotion recognition device for recognizing and analyzing the emotional state of the user. This makes it possible to generate quick and appropriate case response measures and to realize flexible responses that take the user's emotions into consideration. 【0532】 A "recording device" is a device that has the function of collecting past case information and saving it in a database. 【0533】 An "information sharing network" is an online platform that aggregates and shares information from a wide range of sources, such as social media and news websites. 【0534】 An "automated learning algorithm" is a computational method that uses machine learning techniques to analyze data patterns and evaluate the impact of events. 【0535】 A "processing device" is a device that processes collected data and has the function of evaluating the impact of misconduct and generating optimal countermeasures. 【0536】 An "emotion recognition device" is a system that analyzes emotions from data and feedback provided by the user and quantifies or categorizes those emotions. 【0537】 This invention provides a system that enables companies and individuals to obtain effective countermeasures against misconduct. The system mainly consists of servers, terminals, and generative AI models. 【0538】 The server operates by integrating multiple hardware devices and software components. The server includes a recording device that manages a database and has the function of collecting and storing historical case information. The collected information is compared with data on current cases obtained from information-sharing networks (e.g., social networking services and news sites). This allows the server to identify similar past cases and associate them with information from the information-sharing network. 【0539】 Furthermore, the server uses a processing unit that executes an automated learning algorithm to quantitatively evaluate the impact of misconduct. This evaluation utilizes machine learning techniques to quantify the impact of the incident. It is equipped with an emotion recognition device to measure emotions from data and feedback provided by users. This device clearly analyzes the user's emotional state. 【0540】 The terminal provides an interface for users to input information about a problem. Users enter detailed case information into the terminal, and this information is securely transmitted to the server. Based on the input information, the server generates the most appropriate countermeasure and presents it to the user through the terminal. 【0541】 For example, if a company discovers a defect in a new product, it can quickly input relevant information into the system using a terminal. The server collects data from social media in real time and uses a generative AI model to perform sentiment analysis and impact assessment. As a result, it generates specific strategies for problem resolution, such as an "action plan for rapid customer response and apology." 【0542】 An example of a prompt for a generative AI model would be: "Analyze customer reactions to the new product and use the sentiment engine to evaluate the level of dissatisfaction. Based on that evaluation, propose specific customer response measures." This prompt allows the AI ​​model to quickly analyze the data and propose appropriate solutions. 【0543】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0544】 Step 1: 【0545】 Users use a terminal to input detailed information about the misconduct that occurred. This information includes specific details and chronological information about the incident. This information is transmitted from the terminal to a server and securely recorded. The input data is formatted as attribute data, including the type of misconduct, those involved, and the date of occurrence. 【0546】 Step 2: 【0547】 The server collects relevant data from the information-sharing network based on the information it receives. For example, it retrieves relevant posts and comments from social media using an API. Here, a prompt is sent to the generating AI model: "Please collect recent social media posts related to the scandal." The collected data is stored in a database as case information. 【0548】 Step 3: 【0549】 The server searches past case data stored in its storage device to identify cases similar to the current incident. A similarity calculation algorithm is used to compare the current incident with past data. The input data is information about the current incident, and the output is a list of related past cases. 【0550】 Step 4: 【0551】 The server executes an automated learning algorithm to assess the impact of the incident based on collected data and similar cases. In this process, a model trained on past impact patterns is used to calculate an impact score. The input consists of similar cases and current situation data, and the output is the impact score. 【0552】 Step 5: 【0553】 The server uses an emotion recognition device to analyze the user's emotional state. It analyzes user feedback and emotional expressions on social media, and quantifies emotions using natural language processing technology. The input is collected emotional data, and the output is an emotional score categorized as positive, negative, or neutral. 【0554】 Step 6: 【0555】 The server integrates the collected data, impact assessment, and sentiment analysis results, and uses a generative AI model to generate the optimal response. In this step, the generated prompt "Please propose specific measures to solve the problem" is sent to the AI, and a proposal is generated from the results. The input is the integrated analysis data, and the output is specific response measures. 【0556】 Step 7: 【0557】 The terminal provides the user with suggestions from the server. The user reviews the suggested solutions, selects an actionable solution, and takes action. The suggested information is displayed visually through the terminal's interface. The output is a visual presentation of the solutions to the user. 【0558】 (Application Example 2) 【0559】 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." 【0560】 In modern society, businesses and organizations sometimes face unethical behavior and crises. While a swift and effective response is required in such situations, traditional methods often fail to provide flexible responses to individual circumstances. Furthermore, the involvement of emotional factors increases the complexity of the problem, making it difficult to implement appropriate countermeasures. To resolve these issues and minimize the impact of unethical behavior, personalized responses based on up-to-date information are essential. 【0561】 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. 【0562】 In this invention, the server includes a storage means for collecting and accumulating data on past cases of unethical behavior; a means for collecting information related to ongoing unethical behavior from an information sharing platform; a means for identifying and associating common past cases based on the collected information; a processing means for executing a machine learning algorithm to evaluate the impact of unethical behavior; a means for recommending the optimal course of action based on the evaluation results; and an emotion recognition means for analyzing a person's emotional state and providing individualized countermeasures. This makes it possible to quickly provide personalized countermeasures that take emotions into account depending on the situation. 【0563】 "Past unethical behavior" refers to inappropriate actions or incidents that have occurred in the past, and data related to these is accumulated and used as a reference for future countermeasures. 【0564】 "Case data" refers to a collection of information about specific situations or events recorded in the past, and serves as the basis for analysis and comparison. 【0565】 A "memory device" is a system or device for storing data and information, making it possible to retrieve and use them when needed. 【0566】 An "information sharing platform" refers to a digital environment or application that allows multiple users to send, exchange, and receive information. 【0567】 "Means of collection" refer to methods and technologies designed to collect necessary information and data, thereby efficiently gathering relevant information. 【0568】 "Common past examples" are events that occurred before the current problem or situation and share similarities, providing clues for solving new problems. 【0569】 A "machine learning algorithm" is a computational method used to learn patterns and rules from vast amounts of data and perform predictions and classifications. 【0570】 A "processing means" is a system or method for analyzing received information or input and manipulating or transforming it according to a specific purpose. 【0571】 "Optimal course of action" refers to the most effective method or solution for dealing with a particular problem situation. 【0572】 A "method of recommendation" refers to a system or process for proposing and presenting the most appropriate option based on the analyzed data. 【0573】 "Emotion recognition methods" refer to technologies and algorithms that identify and analyze a person's emotional state at any given time, based on their facial expressions, voice, written text, etc. 【0574】 To implement this invention, the server first accumulates data on past instances of unethical behavior and stores it in a memory device. Next, it collects information related to ongoing unethical behavior from an information sharing platform. Based on this data, the server identifies and associates common past cases. 【0575】 In this process, the server uses machine learning algorithms to evaluate the impact of the collected data. For example, it uses software such as Python or TensorFlow to perform data calculations and quantify the impact on a specific problem. 【0576】 Subsequently, the server recommends the optimal strategy based on the evaluation results. In this process, emotion recognition is used to take the user's emotions into consideration. This is achieved by using natural language processing libraries (such as NLTK or spaCy) to identify human emotions from speech and text. 【0577】 As a concrete example, if a security incident occurs in a company's office, the server immediately searches for data on related unethical behavior and compares it to similar past cases. Then, using emotion recognition, it assesses the degree of distress among employees and presents guidelines for calming down to the user via their terminal. 【0578】 The generative AI model is used to improve the accuracy of data analysis and sentiment recognition in this process. An example of a prompt message would be: "A security incident has occurred in the office. Employees are panicking. Analyze their emotional state and suggest specific actions to calm them down." 【0579】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0580】 Step 1: 【0581】 The server stores data on past instances of unethical behavior in its storage system. In this step, it receives existing database information sent from the terminal, organizes it, and saves it. The input is data on instances of unethical behavior, and the output is the stored data. During this process, data processing such as data cleansing and duplicate removal is performed. 【0582】 Step 2: 【0583】 The server collects information related to ongoing unethical behavior from an information-sharing platform. The input is external information collected in real time, and the output is the latest collected data on unethical behavior. Specifically, it accesses the platform via an API, performs keyword searches, and automatically crawls related information. 【0584】 Step 3: 【0585】 The server searches for and associates common past cases based on the collected information. The input is the data obtained in Step 1 and Step 2, and the output is a list of similar past cases. For data processing, text mining techniques are used to identify cases with high similarity. 【0586】 Step 4: 【0587】 The server uses a machine learning algorithm to evaluate the impact of the collected data. The input is a list of similar past cases obtained in step 3, and the output is an evaluation score for each case. The evaluation is performed by running the trained model using TensorFlow and quantifying the impact. 【0588】 Step 5: 【0589】 The server recommends the optimal strategy based on the evaluation results. The input is the evaluation score from step 4, and the output is a list of the most effective solutions. The process involves applying a ranking algorithm to select the top-ranked proposal. 【0590】 Step 6: 【0591】 The device analyzes the user's emotions using emotion recognition technology and provides personalized responses. Input is user data such as voice and text, and output is a customized response that reflects the emotions. Emotion analysis is performed using a natural language processing engine (NLTK or spaCy). 【0592】 Step 7: 【0593】 The server executes the final process to improve the accuracy of data analysis and sentiment recognition using a generative AI model. The input is the analysis results up to step 6, and the output is the optimized individual response. It takes prompt text as input to the generative AI model and generates specific solutions. 【0594】 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. 【0595】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0596】 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. 【0597】 [Fourth Embodiment] 【0598】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0599】 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. 【0600】 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). 【0601】 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. 【0602】 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. 【0603】 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). 【0604】 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. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 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". 【0611】 This invention relates to a system that enables companies and individuals to take swift and appropriate countermeasures when misconduct occurs. Specifically, it has means for storing past misconduct data in a storage device and for collecting and analyzing data related to current misconduct in real time from social media. 【0612】 The server searches its database for similar past cases based on the collected information. This process involves analyzing data attributes and patterns and executing algorithms to identify relevant cases. Subsequently, machine learning algorithms are used to calculate and quantify the impact of the current scandal. This allows the server to quantitatively evaluate the impact and provide objective indicators. 【0613】 Furthermore, based on the evaluation results, the server generates optimal countermeasures, including methods of apology, information disclosure, and public relations activities. These countermeasures are designed to minimize damage to the user's brand image and economic costs. The server sends the generated countermeasures to the terminal, and the user receives the proposed countermeasures through the terminal. The user can then take actual action based on these measures and adjust the content as needed. 【0614】 Furthermore, the server has a function to convert the degree of impact into a monetary value in order to calculate performance-based compensation. Based on this calculated amount, users can consider an appropriate compensation system. Through these steps, users can obtain concrete and practical guidelines to minimize damage. 【0615】 As a concrete example, if a company receives a sudden surge of criticism on social media due to a product defect, this system will quickly collect data, find similar cases, and suggest appropriate countermeasures. For instance, it will refer to data from companies that have experienced similar problems in the past and propose specific steps based on insights into what responses were successful and unsuccessful. This will enable companies to quickly and effectively resolve the situation and protect their brand image. 【0616】 The following describes the processing flow. 【0617】 Step 1: 【0618】 Users use a terminal to input information about the incident. This includes details of the incident, the people involved, and the scope of the impact. This data is sent from the terminal to the server. 【0619】 Step 2: 【0620】 The server activates an SNS data collection module to gather additional data on the scandal from the internet. It retrieves relevant posts and comments using specific keywords and hashtags. 【0621】 Step 3: 【0622】 The server compares collected data related to the misconduct with past cases in the database. It extracts similar past incidents and searches for their countermeasures and results. This information is used for subsequent processing. 【0623】 Step 4: 【0624】 The server feeds the acquired data into a machine learning algorithm to analyze the impact of the ongoing scandal. Here, the impact is quantified and output as a specific score. 【0625】 Step 5: 【0626】 The server generates the optimal response plan based on the assessed impact and past response examples. This plan includes specific methods for apologizing, procedures for public relations, and explanations to stakeholders. 【0627】 Step 6: 【0628】 The server sends the generated solution to the terminal and proposes it to the user. The user can review the received solution and customize it or provide feedback as needed. 【0629】 Step 7: 【0630】 The server converts the impact of the misconduct into monetary terms and creates a proposed performance-based compensation plan based on the results. This information is notified to the user and is considered as part of the compensation agreement. 【0631】 Step 8: 【0632】 Users send feedback to the server about the effectiveness of the countermeasures they have taken. The server uses this feedback to update its database and utilize it for learning to improve the accuracy and effectiveness of the system. 【0633】 (Example 1) 【0634】 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". 【0635】 For companies and individuals, responding quickly and appropriately to misconduct is extremely important. However, current methods make it difficult to quickly formulate countermeasures by referring to appropriate past cases. Furthermore, the process of quantitatively evaluating the impact of misconduct and formulating effective countermeasures based on that evaluation is time-consuming and often subjective. Moreover, it is difficult to translate the results of countermeasures into monetary terms and reflect them in compensation systems. There is a need to efficiently solve these problems and minimize losses caused by misconduct. 【0636】 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. 【0637】 In this invention, the server includes a storage means for collecting and storing past case information, a means for collecting information related to the current event from an information sharing platform, and a means for searching for and associating similar past cases based on the collected information. This enables companies and individuals to quickly formulate optimal countermeasures from similar past cases and to construct an appropriate reward system based on the quantitatively evaluated impact. 【0638】 "Case information" refers to data on specific events that have occurred in the past, and includes detailed background information, causes, impacts, and countermeasures. 【0639】 "Memory means" refers to a device or system for storing collected case information, and includes recording media such as databases. 【0640】 An "information sharing platform" refers to any service used on the internet that allows users to publish and view information, and includes social networking services (SNS) and blogs. 【0641】 An "event" refers to an unforeseen situation or incident related to a company or individual, and includes those whose impact is expected. 【0642】 "Impact" refers to the quantitative measure of the potential effects and scope of a particular event on a company or individual, and is an indicator used to evaluate risks and losses. 【0643】 "Converting to monetary value" refers to the process of converting the degree of impact into a specific monetary value, which is then used as an evaluation value based on a particular indicator. 【0644】 A "reward system" refers to a structure of profit sharing based on results, which is set according to the degree of influence that is evaluated. 【0645】 "Notifying a suggestion" refers to the act of informing the user of the countermeasures generated by the server, and this is done through electronic communication. 【0646】 "Gathering feedback" refers to the process of obtaining user feedback and information about usage patterns, which is used for system improvement. 【0647】 This invention is a system designed to provide rapid and appropriate responses to misconduct. Specifically, it involves the collaborative operation of a server, terminal, and user. The details are described below. 【0648】 The server first collects past case information and stores it in a database. This information is formatted into a comparable format and stored on a recording medium such as IndexedDB. Information related to current events is automatically collected in real time from the information sharing platform using an API. This allows the server to quickly acquire large amounts of data and make that information readily available for use. 【0649】 Based on the collected data, the server searches for and associates similar past cases. This process utilizes machine learning algorithms to perform pattern matching and clustering of the data to identify the most similar cases. 【0650】 Next, the server evaluates and quantifies the impact of the scandal. This evaluation process utilizes natural language processing technology to extract sentiment and opinion trends from text-based data to determine the magnitude of the impact. Based on the calculated impact level, the server generates the optimal countermeasures. Specifically, these include methods of apology, revisions to public relations activities, and procedures for information disclosure. 【0651】 The server sends these suggestions to the device, making them accessible to the user. The device then immediately delivers the information to the user via push notifications or email. The user receives the suggestions through the device, adjusts the content as needed, and takes appropriate action. 【0652】 Furthermore, the server converts the assessed impact into economic terms and proposes a reward system based on those results. This allows users to economically evaluate the impact of misconduct and make appropriate decisions. 【0653】 As a concrete example, if an organization faces criticism on an information-sharing platform regarding a product defect, this system will quickly gather relevant information, search for similar cases, and suggest appropriate countermeasures. An example of a prompt for the generating AI model could be, "What is the best course of action for a company that has received criticism on an information-sharing platform regarding a product defect?" This would enable the organization to respond quickly and effectively, maintaining brand credibility. 【0654】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0655】 Step 1: 【0656】 The server collects historical case information and stores it in a database. Historical case data is provided as input. Specifically, the server extracts data from existing digital archives and various reports, processes it, and saves it to a recording medium such as IndexedDB. Formatted case information is then prepared as output. 【0657】 Step 2: 【0658】 The server collects information related to current events in real time from the information sharing platform. The input is current posted data obtained via the API. Specifically, the server monitors specified keywords and hashtags, filters relevant information, and collects the necessary content. The output is data on ongoing events. 【0659】 Step 3: 【0660】 The server searches for similar cases based on collected past cases and current event data. The input consists of organized case information and ongoing event data. The server compares the data attributes, performs similarity analysis using machine learning algorithms, and identifies related cases. The output is a list of similar past cases. 【0661】 Step 4: 【0662】 The server evaluates and quantifies the impact of an event. Inputs include current event data and a list of similar cases. Specifically, the server utilizes natural language processing techniques to perform sentiment and trend analysis on the acquired data. The output generates quantitative indicators showing the degree of impact. 【0663】 Step 5: 【0664】 The server generates optimal countermeasures based on the evaluation results. It takes impact indicators and past success stories as input and selects the most suitable course of action. Specifically, it utilizes a generation AI model to automatically generate proposal and apology letter templates and designs each countermeasure procedure. The output is a detailed document of the generated countermeasures. 【0665】 Step 6: 【0666】 The server sends the generated countermeasures to the terminal and notifies the user. The terminal then displays the suggested information received as input on its user interface. The terminal immediately communicates the information to the user via push notification or email. As an output, rapid notification to the user is completed. 【0667】 Step 7: 【0668】 The user receives proposed countermeasures via the terminal, adjusts them as needed, and makes a decision. The countermeasures displayed on the terminal are used as input. Based on this information, the user customizes them to align with internal procedures and develops a concrete implementation plan. The final output is a revised implementation plan. 【0669】 Step 8: 【0670】 The server converts the degree of impact into monetary value and calculates the reward. The input is a quantified impact index. The server converts the impact into a specific monetary value according to the economic index and proposes a reward structure. As output, a monetary-based reward proposal is generated and provided. 【0671】 (Application Example 1) 【0672】 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". 【0673】 When a scandal occurs within a company or organization, it is crucial to minimize its impact and respond quickly and effectively. Currently, however, finding appropriate case studies for responding to scandals and quantitatively evaluating their impact is difficult, resulting in delays in countermeasures. This carries the risk of damaging brand image and incurring economic losses. 【0674】 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. 【0675】 In this invention, the server includes a storage means for collecting and storing data on past incidents of misconduct, a means for collecting information from a communication medium, and a means for searching for and associating similar past incidents based on the collected information. This enables the rapid generation of countermeasures against incidents of misconduct and improves the efficiency of information management. 【0676】 A "memory device" is a device or system that stores case data related to past misconduct and makes it possible to retrieve it as needed. 【0677】 A "communication medium" refers to a network or platform used to exchange information with other devices or systems, and in most cases, this refers to the internet. 【0678】 A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and uses that learning to evaluate and predict new data. 【0679】 "Impact level" is an indicator that quantitatively shows the magnitude of the impact that an event has on a company or organization. 【0680】 A "countermeasure" is a set of actions or plans taken to address a particular problem or situation. 【0681】 An "information terminal" is an electronic device used by users to view and manipulate information, and in most cases includes smartphones and tablets. 【0682】 To realize this invention, the server processes various data and provides a system that supports rapid and accurate response to misconduct. First, the server uses storage means to accumulate data on past incidents of misconduct. This accumulated data is retrieved as needed and used for analysis. By using means to collect relevant information in real time from communication media, information on ongoing incidents is quickly obtained. This allows the server to form a foundation for comparing current events with past cases. 【0683】 The server utilizes machine learning algorithms to evaluate the impact of collected data. This evaluation involves calculations based on a large amount of historical data, and the results are expressed as quantitative indicators. Based on these results, the optimal countermeasures are proposed. These countermeasures are notified to the user via an information terminal, and the user can take specific actions based on the proposal. This allows the user to quickly take appropriate measures to minimize the impact of the incident. 【0684】 For example, if a company suddenly receives criticism on social media, this system instantly aggregates relevant data and presents analysis results of similar cases. As a result, the person in charge can quickly obtain the most appropriate response options (e.g., issuing a direct statement, dealing with the media, etc.). 【0685】 An example of a prompt using a generative AI model is, "Please suggest the best course of action for a company to quickly respond to criticism on social media." The server manages this information comprehensively and provides users with accurate information and advice. 【0686】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0687】 Step 1: 【0688】 The server collects data related to the scandal in real time from communication media. The input is publicly available information such as social media, and the server filters it to extract only the relevant data. The filtered data is then stored in a database. 【0689】 Step 2: 【0690】 The server uses storage methods to search for past incident data and evaluates its similarity to the collected current data. The input is the data stored in step 1 and past incident data, and the output is a list of similar incidents. Text mining and natural language processing are performed to analyze the similarity. 【0691】 Step 3: 【0692】 The server uses a machine learning algorithm to assess the impact of the current incident. The input is a list of similar cases obtained in step 2, and the impact is output as a numerical value. Here, statistical analysis is performed to calculate the risk and quantify the impact based on the results for each case. 【0693】 Step 4: 【0694】 The server generates the optimal countermeasures based on the impact assessment results. The input is the impact data obtained in step 3, and the output is a list of proposed countermeasures. This process utilizes a generative AI model to provide specific suggestions in response to prompts presented to the user. 【0695】 Step 5: 【0696】 The server notifies the user of the generated countermeasures via an information terminal. The user receives this notification and can choose a specific action. The input is a list of proposed countermeasures, and the output is the information notified to the user. It provides an interface to support the user's decision-making. 【0697】 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. 【0698】 This invention is a system for effectively managing misconduct faced by companies and individuals, and aims to provide more personalized responses by incorporating an emotion engine. This system not only includes memory means, data collection means, similar case search means, evaluation means using machine learning algorithms, and suggestion generation means, but also includes an emotion engine for recognizing the user's emotions. 【0699】 First, the user uses a terminal to input information related to the misconduct and sends it to the server. The server then collects relevant data from networks such as social media and compares it with a database of past cases stored in its memory. Next, the server identifies similar past cases of misconduct and uses a machine learning algorithm to evaluate the impact of the current misconduct based on the results. 【0700】 The key feature of this system is its emotion engine, which analyzes emotional states based on direct feedback and input data provided by the user. The emotion engine analyzes emotions from user input, and the server uses the results to generate personalized responses. This makes it possible to propose the most appropriate response for the user and minimize the impact of misconduct. 【0701】 For example, when a company announces a product defect, negative opinions and emotions may spread. In this situation, the emotion engine analyzes employee and customer reactions in real time, detecting emotions such as anger and disappointment. The server can then consider this emotional data and propose countermeasures that require particular attention. This makes it easier for companies to make strategic decisions to minimize damage, while simultaneously contributing to reducing the psychological burden on users. 【0702】 By implementing this invention, it becomes possible to not only analyze facts but also to respond flexibly based on emotional states, thereby achieving more accurate misconduct management. 【0703】 The following describes the processing flow. 【0704】 Step 1: 【0705】 The user uses a terminal to enter detailed information related to the misconduct. This includes the nature of the incident, the people involved, and the scope of its impact. This information is then sent from the terminal to the server. 【0706】 Step 2: 【0707】 The server collects data on scandals from social media and related information sources. It retrieves comments and posts using specific keywords and hashtags and stores them in relevant databases. 【0708】 Step 3: 【0709】 The server uses the collected data to search past databases for similar cases of misconduct. It identifies relevant past cases and analyzes their countermeasures and evaluation results. 【0710】 Step 4: 【0711】 The server uses machine learning algorithms to assess the impact of the current scandal. It quantitatively measures the impact based on indicators derived from the data. 【0712】 Step 5: 【0713】 The server activates an emotion engine based on user input data and recognizes the user's emotional state. It analyzes the input text and feedback to calculate an emotion score. 【0714】 Step 6: 【0715】 The server takes emotional states into account to generate personalized responses. By understanding the user's emotions, it can propose more appropriate apologies and public relations strategies. 【0716】 Step 7: 【0717】 The server sends the generated countermeasures to the terminal and presents them to the user. The user reviews the proposed countermeasures, makes adjustments as needed, and decides on the actual measures to take. 【0718】 Step 8: 【0719】 Users input feedback on the effectiveness of the countermeasures they have taken via their device and send it to the server. The server collects this feedback and uses it to learn from and improve the system. 【0720】 (Example 2) 【0721】 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". 【0722】 In the event of a scandal involving a company or individual, accurately understanding its impact and formulating swift and effective countermeasures is crucial. However, conventional methods have limitations in assessing the similarity and impact of different cases, and furthermore, in providing countermeasures that take into account the emotional state of users. 【0723】 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. 【0724】 In this invention, the server includes a recording device for collecting and storing past case information, means for collecting information related to currently ongoing cases from an information sharing network, means for searching for and associating similar past cases based on the collected information, a processing device for executing an automated learning algorithm for evaluating the impact of cases, means for generating an optimal response based on the degree of impact and emotion data, and an emotion recognition device for recognizing and analyzing the emotional state of the user. This makes it possible to generate quick and appropriate case response measures and to realize flexible responses that take the user's emotions into consideration. 【0725】 A "recording device" is a device that has the function of collecting past case information and saving it in a database. 【0726】 An "information sharing network" is an online platform that aggregates and shares information from a wide range of sources, such as social media and news websites. 【0727】 An "automated learning algorithm" is a computational method that uses machine learning techniques to analyze data patterns and evaluate the impact of events. 【0728】 A "processing device" is a device that processes collected data and has the function of evaluating the impact of misconduct and generating optimal countermeasures. 【0729】 An "emotion recognition device" is a system that analyzes emotions from data and feedback provided by the user and quantifies or categorizes those emotions. 【0730】 This invention provides a system that enables companies and individuals to obtain effective countermeasures against misconduct. The system mainly consists of servers, terminals, and generative AI models. 【0731】 The server operates by integrating multiple hardware devices and software components. The server includes a recording device that manages a database and has the function of collecting and storing historical case information. The collected information is compared with data on current cases obtained from information-sharing networks (e.g., social networking services and news sites). This allows the server to identify similar past cases and associate them with information from the information-sharing network. 【0732】 Furthermore, the server uses a processing unit that executes an automated learning algorithm to quantitatively evaluate the impact of misconduct. This evaluation utilizes machine learning techniques to quantify the impact of the incident. It is equipped with an emotion recognition device to measure emotions from data and feedback provided by users. This device clearly analyzes the user's emotional state. 【0733】 The terminal provides an interface for users to input information about a problem. Users enter detailed case information into the terminal, and this information is securely transmitted to the server. Based on the input information, the server generates the most appropriate countermeasure and presents it to the user through the terminal. 【0734】 For example, if a company discovers a defect in a new product, it can quickly input relevant information into the system using a terminal. The server collects data from social media in real time and uses a generative AI model to perform sentiment analysis and impact assessment. As a result, it generates specific strategies for problem resolution, such as an "action plan for rapid customer response and apology." 【0735】 An example of a prompt for a generative AI model would be: "Analyze customer reactions to the new product and use the sentiment engine to evaluate the level of dissatisfaction. Based on that evaluation, propose specific customer response measures." This prompt allows the AI ​​model to quickly analyze the data and propose appropriate solutions. 【0736】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0737】 Step 1: 【0738】 Users use a terminal to input detailed information about the misconduct that occurred. This information includes specific details and chronological information about the incident. This information is transmitted from the terminal to a server and securely recorded. The input data is formatted as attribute data, including the type of misconduct, those involved, and the date of occurrence. 【0739】 Step 2: 【0740】 The server collects relevant data from the information-sharing network based on the information it receives. For example, it retrieves relevant posts and comments from social media using an API. Here, a prompt is sent to the generating AI model: "Please collect recent social media posts related to the scandal." The collected data is stored in a database as case information. 【0741】 Step 3: 【0742】 The server searches past case data stored in its storage device to identify cases similar to the current incident. A similarity calculation algorithm is used to compare the current incident with past data. The input data is information about the current incident, and the output is a list of related past cases. 【0743】 Step 4: 【0744】 The server executes an automated learning algorithm to assess the impact of the incident based on collected data and similar cases. In this process, a model trained on past impact patterns is used to calculate an impact score. The input consists of similar cases and current situation data, and the output is the impact score. 【0745】 Step 5: 【0746】 The server uses an emotion recognition device to analyze the user's emotional state. It analyzes user feedback and emotional expressions on social media, and quantifies emotions using natural language processing technology. The input is collected emotional data, and the output is an emotional score categorized as positive, negative, or neutral. 【0747】 Step 6: 【0748】 The server integrates the collected data, impact assessment, and sentiment analysis results, and uses a generative AI model to generate the optimal response. In this step, the generated prompt "Please propose specific measures to solve the problem" is sent to the AI, and a proposal is generated from the results. The input is the integrated analysis data, and the output is specific response measures. 【0749】 Step 7: 【0750】 The terminal provides the user with suggestions from the server. The user reviews the suggested solutions, selects an actionable solution, and takes action. The suggested information is displayed visually through the terminal's interface. The output is a visual presentation of the solutions to the user. 【0751】 (Application Example 2) 【0752】 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". 【0753】 In modern society, businesses and organizations sometimes face unethical behavior and crises. While a swift and effective response is required in such situations, traditional methods often fail to provide flexible responses to individual circumstances. Furthermore, the involvement of emotional factors increases the complexity of the problem, making it difficult to implement appropriate countermeasures. To resolve these issues and minimize the impact of unethical behavior, personalized responses based on up-to-date information are essential. 【0754】 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. 【0755】 In this invention, the server includes a storage means for collecting and accumulating data on past cases of unethical behavior; a means for collecting information related to ongoing unethical behavior from an information sharing platform; a means for identifying and associating common past cases based on the collected information; a processing means for executing a machine learning algorithm to evaluate the impact of unethical behavior; a means for recommending the optimal course of action based on the evaluation results; and an emotion recognition means for analyzing a person's emotional state and providing individualized countermeasures. This makes it possible to quickly provide personalized countermeasures that take emotions into account depending on the situation. 【0756】 "Past unethical behavior" refers to inappropriate actions or incidents that have occurred in the past, and data related to these is accumulated and used as a reference for future countermeasures. 【0757】 "Case data" refers to a collection of information about specific situations or events recorded in the past, and serves as the basis for analysis and comparison. 【0758】 A "memory device" is a system or device for storing data and information, making it possible to retrieve and use them when needed. 【0759】 An "information sharing platform" refers to a digital environment or application that allows multiple users to send, exchange, and receive information. 【0760】 "Means of collection" refer to methods and technologies designed to collect necessary information and data, thereby efficiently gathering relevant information. 【0761】 "Common past examples" are events that occurred before the current problem or situation and share similarities, providing clues for solving new problems. 【0762】 A "machine learning algorithm" is a computational method used to learn patterns and rules from vast amounts of data and perform predictions and classifications. 【0763】 A "processing means" is a system or method for analyzing received information or input and manipulating or transforming it according to a specific purpose. 【0764】 "Optimal course of action" refers to the most effective method or solution for dealing with a particular problem situation. 【0765】 A "method of recommendation" refers to a system or process for proposing and presenting the most appropriate option based on the analyzed data. 【0766】 "Emotion recognition methods" refer to technologies and algorithms that identify and analyze a person's emotional state at any given time, based on their facial expressions, voice, written text, etc. 【0767】 To implement this invention, the server first accumulates data on past instances of unethical behavior and stores it in a memory device. Next, it collects information related to ongoing unethical behavior from an information sharing platform. Based on this data, the server identifies and associates common past cases. 【0768】 In this process, the server uses machine learning algorithms to evaluate the impact of the collected data. For example, it uses software such as Python or TensorFlow to perform data calculations and quantify the impact on a specific problem. 【0769】 Subsequently, the server recommends the optimal strategy based on the evaluation results. In this process, emotion recognition is used to take the user's emotions into consideration. This is achieved by using natural language processing libraries (such as NLTK or spaCy) to identify human emotions from speech and text. 【0770】 As a concrete example, if a security incident occurs in a company's office, the server immediately searches for data on related unethical behavior and compares it to similar past cases. Then, using emotion recognition, it assesses the degree of distress among employees and presents guidelines for calming down to the user via their terminal. 【0771】 The generative AI model is used to improve the accuracy of data analysis and sentiment recognition in this process. An example of a prompt message would be: "A security incident has occurred in the office. Employees are panicking. Analyze their emotional state and suggest specific actions to calm them down." 【0772】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0773】 Step 1: 【0774】 The server stores data on past instances of unethical behavior in its storage system. In this step, it receives existing database information sent from the terminal, organizes it, and saves it. The input is data on instances of unethical behavior, and the output is the stored data. During this process, data processing such as data cleansing and duplicate removal is performed. 【0775】 Step 2: 【0776】 The server collects information related to ongoing unethical behavior from an information-sharing platform. The input is external information collected in real time, and the output is the latest collected data on unethical behavior. Specifically, it accesses the platform via an API, performs keyword searches, and automatically crawls related information. 【0777】 Step 3: 【0778】 The server searches for and associates common past cases based on the collected information. The input is the data obtained in Step 1 and Step 2, and the output is a list of similar past cases. For data processing, text mining techniques are used to identify cases with high similarity. 【0779】 Step 4: 【0780】 The server uses a machine learning algorithm to evaluate the impact of the collected data. The input is a list of similar past cases obtained in step 3, and the output is an evaluation score for each case. The evaluation is performed by running the trained model using TensorFlow and quantifying the impact. 【0781】 Step 5: 【0782】 The server recommends the optimal strategy based on the evaluation results. The input is the evaluation score from step 4, and the output is a list of the most effective solutions. The process involves applying a ranking algorithm to select the top-ranked proposal. 【0783】 Step 6: 【0784】 The device analyzes the user's emotions using emotion recognition technology and provides personalized responses. Input is user data such as voice and text, and output is a customized response that reflects the emotions. Emotion analysis is performed using a natural language processing engine (NLTK or spaCy). 【0785】 Step 7: 【0786】 The server executes the final process to improve the accuracy of data analysis and sentiment recognition using a generative AI model. The input is the analysis results up to step 6, and the output is the optimized individual response. It takes prompt text as input to the generative AI model and generates specific solutions. 【0787】 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. 【0788】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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." 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0808】 The following is further disclosed regarding the embodiments described above. 【0809】 (Claim 1) 【0810】 A storage means for collecting and storing data on past scandals, 【0811】 A means of collecting data related to ongoing scandals from social media, 【0812】 A means of searching for and relating similar past cases based on the collected data, 【0813】 A processing means for executing a machine learning algorithm to evaluate the impact of a scandal, 【0814】 A means of proposing the optimal countermeasures based on the evaluation results, 【0815】 A system that integrates the above methods to present countermeasures for misconduct. 【0816】 (Claim 2) 【0817】 The system according to claim 1, which converts the impact of a scandal into a monetary value and calculates compensation based on that impact. 【0818】 (Claim 3) 【0819】 The system according to claim 1, comprising means for collecting user feedback and for learning and improving the system. 【0820】 "Example 1" 【0821】 (Claim 1) 【0822】 A storage means for collecting and storing past case information, 【0823】 A means of collecting information related to current events from an information sharing platform, 【0824】 A means of searching for and relating similar past cases based on the collected information, 【0825】 A processing means for executing a machine learning algorithm to evaluate the impact of an event, 【0826】 A means of proposing the optimal strategy based on the evaluation results, 【0827】 A method for converting the evaluated impact into monetary value and calculating compensation, 【0828】 A means of notifying the user of the proposal and allowing them to adjust its contents, 【0829】 A system that integrates the above methods to present countermeasures for events. 【0830】 (Claim 2) 【0831】 The system according to claim 1, which considers a compensation system based on the evaluated degree of impact. 【0832】 (Claim 3) 【0833】 The system according to claim 1, comprising means for collecting user feedback and learning from and improving the system. 【0834】 "Application Example 1" 【0835】 (Claim 1) 【0836】 A storage means for collecting and storing data on past scandals, 【0837】 Means of collecting information from communication media, 【0838】 A means of searching for and relating similar past cases based on the collected information, 【0839】 A processing means for executing a machine learning algorithm to evaluate the degree of impact, 【0840】 A means of presenting the optimal countermeasure based on the evaluation results and notifying the user's input device, 【0841】 A means that has the function of notifying users of warnings via information terminals and suggesting countermeasures, 【0842】 A system that integrates the above methods to manage information and propose countermeasures. 【0843】 (Claim 2) 【0844】 The system according to claim 1, which converts the degree of influence into a monetary value and calculates compensation based on that degree of influence. 【0845】 (Claim 3) 【0846】 The system according to claim 1, comprising means for collecting user feedback and for learning and improving the system. 【0847】 "Example 2 of combining an emotion engine" 【0848】 (Claim 1) 【0849】 A recording device for collecting and storing past case information, 【0850】 A means of collecting information related to ongoing cases from an information sharing network, 【0851】 A means of searching for and relating similar past cases based on the collected information, 【0852】 A processing unit that executes an automated learning algorithm to evaluate the impact of a case, 【0853】 A means of generating the optimal response based on impact and sentiment data, 【0854】 An emotion recognition device for recognizing and analyzing the emotional state of a user, 【0855】 This system integrates the above devices to propose strategies for addressing specific cases. 【0856】 (Claim 2) 【0857】 The system according to claim 1, which converts the impact of a case into a monetary amount and calculates compensation based on that impact. 【0858】 (Claim 3) 【0859】 The system according to claim 1, comprising means for collecting user feedback and for learning and improving the system. 【0860】 "Application example 2 when combining with an emotional engine" 【0861】 (Claim 1) 【0862】 A means of storing and collecting data on past cases of unethical behavior, 【0863】 Means of collecting information related to ongoing unethical behavior from information sharing platforms, 【0864】 A means of identifying and linking common past cases based on the collected information, 【0865】 A processing means for executing a machine learning algorithm to evaluate the impact of unethical behavior, 【0866】 A means of recommending the optimal strategy based on the evaluation results, 【0867】 A means of recognizing emotions to analyze the state of human emotions and provide individualized countermeasures, 【0868】 A system that integrates the above methods to present countermeasures against unethical behavior. 【0869】 (Claim 2) 【0870】 The system according to claim 1, which converts the impact of unethical behavior into a numerical value and calculates a reward based on that impact. 【0871】 (Claim 3) 【0872】 The system according to claim 1, comprising means for collecting user feedback and for learning and improving the system. [Explanation of symbols] 【0873】 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 storage means for collecting and storing data on past scandals, A means of collecting data related to ongoing scandals from social media, A means of searching for and relating similar past cases based on the collected data, A processing means for executing a machine learning algorithm to evaluate the impact of a scandal, A means of proposing the optimal countermeasures based on the evaluation results, A system that integrates the above methods to present countermeasures for misconduct. [Claim 2] The system according to claim 1, which converts the impact of a scandal into a monetary amount and calculates compensation based on that impact. [Claim 3] The system according to claim 1, comprising means for collecting user feedback and for learning and improving the system.

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