system

Abstract

The system according to this embodiment aims to analyze news and social media data, identify political correctness issues, and generate appropriate guidelines. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The collection unit collects news and SNS data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies political correctness issues based on the data analyzed by the analysis unit. The generation unit generates guidelines based on the issues identified by the identification unit. The provision unit provides immediate feedback based on the guidelines generated by the generation unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the problems of political correctness have not been quickly pointed out from news and SNS data, and appropriate guidelines have not been sufficiently generated, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze news and SNS data, point out the problems of political correctness, and generate appropriate guidelines.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The collection unit collects news and social media data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies political correctness issues based on the data analyzed by the analysis unit. The generation unit generates guidelines based on the issues identified by the identification unit. The provision unit provides immediate feedback based on the guidelines generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze news and social media data, identify political correctness issues, and generate appropriate guidelines. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The trend analysis system according to an embodiment of the present invention is a system that monitors news and social media data in real time, identifies trends and risk areas through AI analysis, points out political correctness issues before new services are launched, and generates guidelines to be applied. The trend analysis system monitors news and social media data in real time, and the AI ​​analyzes this data to identify trends and risk areas. For example, if a particular keyword or phrase is rapidly increasing, it is recognized as a trend. Also, if a particular topic may violate political correctness, it is identified as a risk area. Next, the trend analysis system points out political correctness issues before new services are launched. For example, if a new advertising campaign contains expressions that are inappropriate for a particular group, the trend analysis system points out the issue. Furthermore, the trend analysis system generates guidelines to be applied. For example, it provides specific guidelines such as avoiding certain expressions or being considerate of certain groups. Finally, the trend analysis system provides immediate feedback based on the latest data. This allows companies to minimize risks in real time. For example, before a new service or advertising campaign is launched, the trend analysis system can provide immediate feedback and make necessary corrections. This system enables companies to minimize political correctness-related risks and adapt to social trends. This allows companies to prevent damage from legal and compliance violations and mitigate the risks associated with launching and operating services and operations. Thus, the trend analysis system enables companies to minimize political correctness-related risks and adapt to social trends.

[0029] The trend analysis system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The collection unit collects news and SNS data. The collection unit can collect data from, for example, news sites and SNS platforms. The collection unit can also obtain data using APIs. Furthermore, the collection unit can collect data using web scraping technology. For example, the collection unit obtains the latest news articles using RSS feeds from news sites. The collection unit collects posts related to specific keywords using APIs from SNS platforms. The collection unit collects data from specific websites using web scraping technology. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining technology. For example, the analysis unit analyzes the content of collected news articles and SNS posts using natural language processing technology. The analysis unit classifies the collected data by topic using machine learning algorithms. The analysis unit uses data mining techniques to analyze the frequency of occurrence of specific keywords and phrases and identify trends. The feedback unit points out political correctness issues based on the data analyzed by the analysis unit. For example, the feedback unit can determine whether a particular expression is inappropriate. The feedback unit can also determine whether there is a lack of consideration for a particular group. Furthermore, the feedback unit can determine whether a particular expression violates laws or guidelines. For example, the feedback unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. The feedback unit can determine whether a particular service lacks consideration for a particular group. The feedback unit can determine whether a particular expression violates laws or guidelines. The generation unit generates guidelines based on the issues pointed out by the feedback unit. The generation unit can provide specific guidelines, for example, such as certain expressions that should be avoided.The generation unit can also provide specific guidelines, such as that consideration should be given to certain groups. Furthermore, the generation unit can provide specific guidelines, such as that certain laws or guidelines should be followed. For example, the generation unit can provide guidelines, such as that certain expressions should be avoided. The generation unit can provide guidelines, such as that consideration should be given to certain groups. The generation unit can provide guidelines, such as that certain laws or guidelines should be followed. The provision unit provides immediate feedback based on the guidelines generated by the generation unit. The provision unit can, for example, provide guidelines to companies. The provision unit can also provide feedback on specific services or advertising campaigns. Furthermore, the provision unit can also provide feedback on specific expressions or content. For example, the provision unit can provide guidelines to companies. The provision unit can provide feedback on specific services or advertising campaigns. The provision unit can provide feedback on specific expressions or content. Thus, the trend analysis system according to this embodiment minimizes risk by monitoring news and SNS data in real time, analyzing trends and risk areas, identifying political correctness issues, generating applicable guidelines, and providing immediate feedback.

[0030] The data collection unit collects news and social media data. For example, it can collect data from news sites and social media platforms. It can also obtain data using APIs. Furthermore, it can collect data using web scraping techniques. For instance, it can obtain the latest news articles using RSS feeds from news sites. It can collect posts related to specific keywords using social media platform APIs. It can collect data from specific websites using web scraping techniques. Specifically, it regularly checks RSS feeds from news sites and retrieves the content whenever new articles are published. This allows for the collection of the latest news information in real time. Additionally, by utilizing social media platform APIs, it can efficiently collect posts related to specific keywords and hashtags. For example, it can collect posts about specific events or topics to understand trends. Furthermore, by using web scraping techniques, it can automatically extract necessary data from specific websites. This eliminates the need for manual data collection and allows for the efficient collection of large amounts of data. By combining these methods, the data collection unit collects a wide range of data from diverse sources, building a foundation for trend analysis.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze text data using natural language processing techniques. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining techniques. For example, the analysis unit uses natural language processing techniques to analyze the content of collected news articles and social media posts. Specifically, it performs tokenization, morphological analysis, and grammatical analysis of text data to analyze the content of each post and article in detail. In addition, it uses machine learning algorithms to classify the collected data by topic. For example, it uses clustering algorithms to group posts and articles with similar content and identify major topics. It also uses data mining techniques to analyze the frequency of occurrence of specific keywords and phrases and identify trends. For example, by extracting frequently occurring keywords and phrases and analyzing their fluctuations over time, it is possible to grasp changes in trends. By combining these techniques, the analysis unit can extract useful information from the collected data and quickly and accurately identify trends and risk areas.

[0032] The feedback unit identifies political correctness issues based on data analyzed by the analysis unit. For example, the feedback unit can determine whether a particular expression is inappropriate. It can also determine whether a particular group is being insensitive. Furthermore, it can determine whether a particular expression violates laws or guidelines. For example, the feedback unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. Specifically, it uses natural language processing technology to analyze collected text data and detect whether it contains specific expressions or phrases. In addition, it can use machine learning algorithms to automatically identify inappropriate or insensitive expressions based on past data and examples. By utilizing these technologies, the feedback unit can quickly and accurately identify political correctness issues from the collected data.

[0033] The generation unit generates guidelines based on the issues pointed out by the feedback unit. The generation unit can provide specific guidelines, such as avoiding certain expressions. It can also provide specific guidelines, such as considering certain groups. Furthermore, it can provide specific guidelines, such as adhering to certain laws or guidelines. For example, the generation unit can provide guidelines such as avoiding certain expressions. Specifically, it uses natural language generation technology to automatically generate appropriate expressions and countermeasures for the pointed-out issues. In addition, the generation unit can refer to past cases, laws, and guidelines to provide specific countermeasures and recommendations. This allows the generation unit to quickly provide specific and practical guidelines for the pointed-out issues.

[0034] The service provider provides immediate feedback based on the guidelines generated by the generation service provider. For example, the service provider can provide guidelines to companies. The service provider can also provide feedback on specific services or advertising campaigns. Furthermore, the service provider can provide feedback on specific expressions or content. For example, the service provider can provide guidelines to companies. Specifically, it can notify company representatives of the generated guidelines and encourage appropriate action. The service provider can also provide feedback on specific services or advertising campaigns. For example, if the content of an advertising campaign is inappropriate, it can provide specific instructions for correcting that content. In addition, the service provider can provide feedback on specific expressions or content. For example, if a particular expression is inappropriate, it can provide specific instructions for correcting that expression. This allows the service provider to provide quick and appropriate feedback based on the generated guidelines, minimizing risk.

[0035] The data collection unit can collect news and social media data in real time. For example, the data collection unit collects data in real time from news sites and social media platforms. The data collection unit can also obtain data in real time using APIs. Furthermore, the data collection unit can collect data in real time using web scraping technology. For example, the data collection unit can obtain the latest news articles in real time using RSS feeds from news sites. The data collection unit can collect posts related to specific keywords in real time using APIs from social media platforms. The data collection unit can collect data in real time from specific websites using web scraping technology. This allows for immediate analysis of the latest information by collecting news and social media data in real time. The specific definition and criteria of real time include the frequency of data collection and the acceptable range of latency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from news sites and social media platforms into a generating AI, which can analyze the data in real time and dynamically change the collection target.

[0036] The analysis unit can analyze collected data and identify trends and risk areas. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining technology. For example, the analysis unit can analyze the content of collected news articles and social media posts using natural language processing technology. The analysis unit can classify collected data by topic using machine learning algorithms. The analysis unit can identify trends by analyzing the frequency of occurrence of specific keywords and phrases using data mining technology. This allows for the analysis of collected data and the identification of trends and risk areas, enabling appropriate countermeasures to be taken. Specific definitions and criteria for trends include the duration of the trend and the method of detecting the trend. Specific definitions and criteria for risk areas include the type of risk and the criteria for evaluating the risk. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, which can then identify trends and risk areas.

[0037] The reporting unit can identify political correctness issues before a new service is launched. For example, the reporting unit can determine whether a particular expression is inappropriate. The reporting unit can also determine whether there is a lack of consideration for a particular group. Furthermore, the reporting unit can determine whether a particular expression violates laws or guidelines. For example, the reporting unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. The reporting unit can determine whether a particular service lacks consideration for a particular group. The reporting unit can determine whether a particular expression violates laws or guidelines. This allows risks to be prevented by identifying political correctness issues before a new service is launched. The specific scope and criteria of the new service include the type of service and the timing of its launch. Some or all of the processing described above in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the content of the new service into a generating AI, which can then identify political correctness issues.

[0038] The generation unit can generate guidelines to be applied based on the identified problems. The generation unit can provide specific guidelines, such as avoiding certain expressions. The generation unit can also provide specific guidelines, such as considering certain groups. Furthermore, the generation unit can provide specific guidelines, such as adhering to certain laws or guidelines. For example, the generation unit can provide guidelines such as avoiding certain expressions. The generation unit can provide guidelines such as considering certain groups. The generation unit can provide guidelines such as adhering to certain laws or guidelines. This allows for the provision of appropriate countermeasures by generating guidelines to be applied based on the identified problems. The specific content and criteria of the guidelines to be applied include the scope of application of the guidelines and specific instructions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the identified problems into a generation AI, which can then generate guidelines to be applied.

[0039] The service provider can provide immediate feedback based on the generated guidelines. For example, the service provider can provide guidelines to companies. The service provider can also provide feedback on specific services or advertising campaigns. Furthermore, the service provider can provide feedback on specific expressions or content. For example, the service provider can provide guidelines to companies. The service provider can provide feedback on specific services or advertising campaigns. The service provider can provide feedback on specific expressions or content. This enables a rapid response by providing immediate feedback based on the generated guidelines. The specific methods and criteria for immediate feedback include the format, method of provision, and timing of the feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated guidelines into a generating AI, and the generating AI can provide immediate feedback.

[0040] The data collection unit can change the priority of the data it collects based on specific time periods or events. For example, during times when an important event is taking place, the data collection unit will prioritize collecting data related to that event. At night, the data collection unit can also prioritize collecting relaxing content and calming news. On weekends, the data collection unit can also prioritize collecting data related to entertainment and leisure. For example, during times when an important event is taking place, the data collection unit will prioritize collecting data related to that event. At night, the data collection unit will prioritize collecting relaxing content and calming news. On weekends, the data collection unit will prioritize collecting data related to entertainment and leisure. This allows for the priority collection of important data by changing the priority of the data collected based on specific time periods or events. The specific definitions and criteria for specific time periods and events include the type of event and the time range. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the priority of the data to be collected based on specific time periods or events into a generating AI, and the generating AI can change the data priority.

[0041] The data collection unit can monitor the frequency of occurrence of specific keywords and phrases in real time during data collection and dynamically change the data to be collected. For example, if a particular keyword suddenly appears, the data collection unit will prioritize collecting data related to that keyword. If a particular phrase becomes a trend, the data collection unit can also prioritize collecting data related to that phrase. Furthermore, if a particular topic suddenly emerges, the data collection unit can also prioritize collecting data related to that topic. For example, if a particular keyword suddenly appears, the data collection unit will prioritize collecting data related to that keyword. If a particular phrase becomes a trend, the data collection unit will prioritize collecting data related to that phrase. If a particular topic suddenly emerges, the data collection unit will prioritize collecting data related to that topic. This allows for the rapid collection of important data by monitoring the frequency of occurrence of specific keywords and phrases in real time and dynamically changing the data to be collected. The specific definitions and criteria for specific keywords and phrases include keyword selection criteria and phrase detection methods. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the frequency of occurrence of specific keywords or phrases into the generating AI, which can then dynamically change the data to be collected.

[0042] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, if an important event is held in a particular region, the data collection unit will prioritize the collection of data from that region. The data collection unit can also prioritize the collection of data from a particular region if a disaster occurs in that region. Furthermore, the data collection unit can also prioritize the collection of data from a particular region if a trend occurs in that region. For example, if an important event is held in a particular region, the data collection unit will prioritize the collection of data from that region. If a disaster occurs in a particular region, the data collection unit will prioritize the collection of data from that region. If a trend occurs in a particular region, the data collection unit will prioritize the collection of data from that region. This allows for the rapid collection of region-specific information by prioritizing the collection of data from specific regions, taking geographical information into consideration. Specific types and criteria of geographical information include the scope of the region and the method of acquiring geographical information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input geographical information into a generating AI, which can then prioritize the collection of data from specific regions.

[0043] The data collection unit can prioritize collecting data from specific social media platforms. For example, if a trend occurs on a particular platform, the data collection unit will prioritize collecting data from that platform. The data collection unit can also prioritize collecting data from a particular platform if an important topic is being discussed on that platform. Furthermore, the data collection unit can prioritize collecting data related to keywords or phrases that are rapidly increasing on a particular platform. For example, if a trend occurs on a particular platform, the data collection unit will prioritize collecting data from that platform. The data collection unit will prioritize collecting data from a particular platform if an important topic is being discussed on that platform. The data collection unit will prioritize collecting data related to keywords or phrases that are rapidly increasing on a particular platform. This allows for the rapid collection of important information by prioritizing the collection of data from specific social media platforms. The specific definition and criteria for a particular platform include the type of platform and selection criteria. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from a particular platform into a generating AI, which can then prioritize the collection of that data.

[0044] The analysis unit can evaluate the reliability of the data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can also prioritize the analysis of data from reliable social media accounts. Furthermore, the analysis unit can prioritize the analysis of data from reliable databases. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can prioritize the analysis of data from reliable social media accounts. The analysis unit can prioritize the analysis of data from reliable databases. By evaluating the reliability of the data and prioritizing the analysis of highly reliable data, the accuracy of the analysis results is improved. Specific evaluation methods and criteria for data reliability include reliability evaluation criteria and evaluation algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data reliability into a generating AI, which can then prioritize the analysis of highly reliable data.

[0045] The analysis unit can predict current trends by referring to past trend data during analysis. For example, the analysis unit predicts current trends based on past trend data. The analysis unit can also predict future trends based on past trend data. Furthermore, the analysis unit can predict the frequency of occurrence of specific keywords or phrases based on past trend data. For example, the analysis unit predicts current trends based on past trend data. The analysis unit predicts future trends based on past trend data. The analysis unit predicts the frequency of occurrence of specific keywords or phrases based on past trend data. This makes it possible to predict trends more accurately by referring to past trend data to predict current trends. The specific types and criteria of past trend data include the data period and data acquisition method. The specific definitions and criteria of current trends include the trend detection method and evaluation criteria. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI, and the generating AI can predict current trends.

[0046] The analysis unit can improve the accuracy of its analysis by considering the source of the data. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can also prioritize the analysis of data from reliable social media accounts. Furthermore, the analysis unit can prioritize the analysis of data from reliable databases. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can prioritize the analysis of data from reliable social media accounts. The analysis unit can prioritize the analysis of data from reliable databases. By improving the accuracy of the analysis by considering the source of the data, it is possible to provide more reliable analysis results. Specific types and criteria for data sources include the reliability of the source and the method for evaluating the source. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data sources into a generating AI, which can then prioritize the analysis of highly reliable data.

[0047] The analysis unit can supplement its analysis results by referring to relevant news articles and literature during the analysis process. For example, the analysis unit may refer to relevant news articles to supplement its analysis results. The analysis unit may also refer to relevant literature to supplement its analysis results. Furthermore, the analysis unit may also refer to relevant databases to supplement its analysis results. For example, the analysis unit may refer to relevant news articles to supplement its analysis results. The analysis unit may refer to relevant literature to supplement its analysis results. The analysis unit may refer to relevant databases to supplement its analysis results. This allows for the provision of more comprehensive analysis results by supplementing the analysis results by referring to relevant news articles and literature. The specific types and criteria for relevant news articles and literature include criteria for selecting articles and methods for obtaining literature. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant news articles and literature into a generating AI, which can then supplement the analysis results.

[0048] The feedback function can improve the accuracy of its problem identification by referring to past cases when providing feedback. For example, the feedback function identifies problems based on past cases. The feedback function can also improve the accuracy of its problem identification based on past cases. Furthermore, the feedback function can improve its method of identifying problems based on past cases. For example, the feedback function identifies problems based on past cases. The feedback function improves the accuracy of its problem identification based on past cases. The feedback function improves its method of identifying problems based on past cases. This allows for more accurate feedback by improving the accuracy of problem identification by referring to past cases. The specific types and criteria of past cases include the criteria for selecting cases and the method for acquiring cases. Some or all of the above processing in the feedback function may be performed using AI, for example, or without using AI. For example, the feedback function can input past cases into a generating AI, which can improve the accuracy of its problem identification.

[0049] The feedback function can apply feedback algorithms specific to particular industries or fields when providing feedback. For example, the feedback function can apply feedback algorithms specific to a particular industry. The feedback function can also apply feedback algorithms specific to a particular field. Furthermore, the feedback function can develop feedback algorithms specific to particular industries or fields. For example, the feedback function can apply feedback algorithms specific to a particular industry. The feedback function can apply feedback algorithms specific to a particular field. The feedback function can develop feedback algorithms specific to particular industries or fields. This allows for more appropriate feedback by applying feedback algorithms specific to particular industries or fields. The specific definitions and criteria for a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input a feedback algorithm specific to a particular industry or field into a generating AI, and the generating AI can apply the feedback algorithm.

[0050] The feedback function can adjust the importance of a feedback issue based on the data submission date. For example, if the data is old, the feedback function may set the importance lower. If the data is newer, the feedback function may set the importance higher. The feedback function can also adjust the priority of feedback issues based on the data submission date. For example, if the data is old, the feedback function may set the importance lower. If the data is newer, the feedback function may set the importance higher. The feedback function adjusts the priority of feedback issues based on the data submission date. This allows for more appropriate feedback by adjusting the importance of feedback issues based on the data submission date. The specific definition and criteria for data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the feedback function may be performed using AI, for example, or not using AI. For example, the feedback function can input the data submission date into a generating AI, which can then adjust the importance of the feedback issue.

[0051] The reporting function can improve the accuracy of its reports by referring to relevant laws, regulations, and guidelines. For example, the reporting function can improve the accuracy of its reports by referring to relevant laws and regulations. The reporting function can also improve the accuracy of its reports by referring to relevant guidelines. Furthermore, the reporting function can improve the accuracy of its reports based on relevant laws, regulations, and guidelines. For example, the reporting function can improve the accuracy of its reports by referring to relevant laws and regulations. The reporting function can improve the accuracy of its reports by referring to relevant guidelines. The reporting function can improve the accuracy of its reports based on relevant laws, regulations, and guidelines. This makes it possible to make more accurate reports by improving the accuracy of reports by referring to relevant laws, regulations, and guidelines. The specific types and criteria of relevant laws, regulations, and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the reporting function may be performed using AI, for example, or not using AI. For example, the reporting function can input relevant laws, regulations, and guidelines into a generating AI, which can then improve the accuracy of its reports.

[0052] The generation unit can generate optimal guidelines by referring to past success stories when generating guidelines. For example, the generation unit generates optimal guidelines based on past success stories. The generation unit can also improve the accuracy of the guidelines based on past success stories. Furthermore, the generation unit can improve the way the guidelines are expressed based on past success stories. For example, the generation unit generates optimal guidelines based on past success stories. The generation unit improves the accuracy of the guidelines based on past success stories. The generation unit improves the way the guidelines are expressed based on past success stories. As a result, by generating optimal guidelines by referring to past success stories, more effective guidelines can be provided. The specific types and criteria of past success stories include the criteria for selecting cases and the methods for obtaining cases. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past success stories into a generation AI, and the generation AI can generate optimal guidelines.

[0053] The generation unit can provide guidelines specific to particular industries or fields when generating guidelines. For example, the generation unit can provide guidelines specific to a particular industry. The generation unit can also provide guidelines specific to a particular field. Furthermore, the generation unit can develop guidelines specific to particular industries or fields. For example, the generation unit can provide guidelines specific to a particular industry. The generation unit can provide guidelines specific to a particular field. The generation unit can develop guidelines specific to particular industries or fields. By providing guidelines specific to particular industries or fields, more appropriate guidelines can be provided. The specific definitions and criteria of a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input guidelines specific to a particular industry or field into a generation AI, and the generation AI can provide the guidelines.

[0054] The generation unit can adjust the importance of guidelines based on the data submission date when generating guidelines. For example, the generation unit may set a lower importance if the data submission date is old, and a higher importance if the data submission date is new. The generation unit can also adjust the priority of guidelines based on the data submission date. For example, the generation unit may set a lower importance if the data submission date is old, and a higher importance if the data submission date is new. The generation unit adjusts the priority of guidelines based on the data submission date. This allows for the provision of more appropriate guidelines by adjusting the importance of guidelines based on the data submission date. The specific definition and criteria for the data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data submission date into a generation AI, and the generation AI can adjust the importance of the guidelines.

[0055] The generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations and guidelines when generating them. For example, the generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations. The generation unit can also improve the accuracy of the guidelines by referring to relevant guidelines. Furthermore, the generation unit can improve the accuracy of the guidelines based on relevant laws and regulations and guidelines. For example, the generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations. The generation unit can improve the accuracy of the guidelines by referring to relevant guidelines. The generation unit can improve the accuracy of the guidelines based on relevant laws and regulations and guidelines. This allows for the provision of more accurate guidelines by improving the accuracy of the guidelines by referring to relevant laws and regulations and guidelines. The specific types and standards of relevant laws and regulations and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant laws and regulations and guidelines into a generation AI, which can then improve the accuracy of the guidelines.

[0056] The service provider can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the service provider can provide optimal feedback based on the user's past feedback history. The service provider can also improve the accuracy of the feedback based on the user's past feedback history. Furthermore, the service provider can improve the way the feedback is expressed based on the user's past feedback history. For example, the service provider can provide optimal feedback based on the user's past feedback history. The service provider can improve the accuracy of the feedback based on the user's past feedback history. The service provider can improve the way the feedback is expressed based on the user's past feedback history. This allows for more effective feedback by referring to the user's past feedback history to provide optimal feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback history into a generating AI, which can then provide optimal feedback.

[0057] The service provider can provide feedback that is specific to a particular industry or field when providing feedback. For example, the service provider can provide feedback specific to a particular industry. The service provider can also provide feedback specific to a particular field. Furthermore, the service provider can develop feedback specific to a particular industry or field. For example, the service provider can provide feedback specific to a particular industry. The service provider can provide feedback specific to a particular field. The service provider can develop feedback specific to a particular industry or field. This allows for the provision of more appropriate feedback by providing feedback specific to a particular industry or field. The specific definition and criteria of a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback specific to a particular industry or field into a generating AI, and the generating AI can provide the feedback.

[0058] The data provider can adjust the importance of feedback based on the data submission date when providing feedback. For example, the provider may set the importance lower if the data is old, and higher if the data is new. The provider can also adjust the priority of feedback based on the data submission date. For example, the provider may set the importance lower if the data is old, and higher if the data is new. The provider adjusts the priority of feedback based on the data submission date. This allows for the provision of more appropriate feedback by adjusting the importance of feedback based on the data submission date. The specific definition and criteria for data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the data submission date into a generating AI, which can then adjust the importance of the feedback.

[0059] The service provider can improve the accuracy of feedback by referring to relevant laws, regulations, and guidelines when providing feedback. For example, the service provider can improve the accuracy of feedback by referring to relevant laws and regulations. The service provider can also improve the accuracy of feedback by referring to relevant guidelines. Furthermore, the service provider can improve the accuracy of feedback based on relevant laws, regulations, and guidelines. For example, the service provider can improve the accuracy of feedback by referring to relevant laws and regulations. The service provider can improve the accuracy of feedback by referring to relevant guidelines. The service provider can improve the accuracy of feedback based on relevant laws, regulations, and guidelines. This allows for the provision of more accurate feedback by improving the accuracy of feedback by referring to relevant laws, regulations, and guidelines. The specific types and criteria of relevant laws, regulations, and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input relevant laws, regulations, and guidelines into a generating AI, which can then improve the accuracy of the feedback.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can change the priority of data collected based on specific time periods or events. For example, during times when important events are taking place, data related to those events can be prioritized. At night, relaxing content and calming news can be prioritized. On weekends, data related to entertainment and leisure can be prioritized. This allows for the priority collection of important data by changing the priority of data collected based on specific time periods or events. The specific definitions and criteria for specific time periods and events include the type of event and the time range. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the priority of data to be collected based on specific time periods or events into a generating AI, which can then change the data priority.

[0062] The analysis unit can evaluate the reliability of the data during analysis and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data from reliable news sources. It can also prioritize the analysis of data from reliable social media accounts. Furthermore, it can prioritize the analysis of data from reliable databases. By evaluating the reliability of the data and prioritizing the analysis of highly reliable data, the accuracy of the analysis results is improved. Specific evaluation methods and criteria for data reliability include reliability evaluation criteria and evaluation algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data reliability into a generating AI, which can then prioritize the analysis of highly reliable data.

[0063] The feedback function can improve the accuracy of its problem identification by referring to past cases. For example, it can identify problems based on past cases. It can also improve the accuracy of problem identification based on past cases. Furthermore, it can improve the method of identifying problems based on past cases. By improving the accuracy of problem identification by referring to past cases, more accurate identification becomes possible. The specific types and criteria of past cases include the criteria for selecting cases and the methods for acquiring cases. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input past cases into a generating AI, which can then improve the accuracy of its problem identification.

[0064] The analysis unit can predict current trends by referring to past trend data during analysis. For example, it can predict current trends based on past trend data. It can also predict future trends based on past trend data. Furthermore, it can predict the frequency of occurrence of specific keywords or phrases based on past trend data. This allows for more accurate trend prediction by referring to past trend data to predict current trends. The specific types and criteria of past trend data include the data period and data acquisition method. The specific definition and criteria of current trends include the trend detection method and evaluation criteria. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI, and the generating AI can predict current trends.

[0065] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, if an important event is held in a particular region, data from that region can be prioritized. If a disaster occurs in a particular region, data from that region can also be prioritized. Furthermore, if a trend occurs in a particular region, data from that region can also be prioritized. This allows for the rapid collection of region-specific information by prioritizing data collection from specific regions, taking geographical information into consideration. Specific types and criteria of geographical information include the scope of the region and the method of acquiring geographical information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input geographical information into a generating AI, which can then prioritize the collection of data from specific regions.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects news and social media data. The collection unit can collect data from news sites and social media platforms, and can also obtain data using APIs. It can also collect data using web scraping techniques. For example, it can use RSS feeds from news sites to obtain the latest news articles and use APIs from social media platforms to collect posts related to specific keywords. Furthermore, it can use web scraping techniques to collect data from specific websites. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze text data using natural language processing techniques and classify data using machine learning algorithms. It can also identify trends and risk areas using data mining techniques. For example, it can analyze the content of collected news articles and social media posts, classify the collected data by topic, and identify trends by analyzing the frequency of occurrence of specific keywords and phrases. Step 3: The reporting team identifies political correctness issues based on the data analyzed by the analysis team. The reporting team can determine whether a particular expression is inappropriate, lacks consideration for a particular group, or violates laws or guidelines. For example, they might determine whether a particular advertising campaign contains inappropriate language for a particular group, whether a particular service lacks consideration for a particular group, or whether a particular expression violates laws or guidelines. Step 4: The generating unit generates guidelines based on the issues identified by the identifying unit. The generating unit can provide specific guidelines such as avoiding certain expressions, being considerate of certain groups, or following specific laws and guidelines. For example, it can provide guidelines such as avoiding certain expressions, being considerate of certain groups, or following specific laws and guidelines. Step 5: The providing unit provides immediate feedback based on the guidelines generated by the generating unit. The providing unit can provide guidelines to companies, provide feedback on specific services or advertising campaigns, and provide feedback on specific expressions or content. For example, it can provide guidelines to companies, provide feedback on specific services or advertising campaigns, and provide feedback on specific expressions or content.

[0068] (Example of form 2) The trend analysis system according to an embodiment of the present invention is a system that monitors news and social media data in real time, identifies trends and risk areas through AI analysis, points out political correctness issues before new services are launched, and generates guidelines to be applied. The trend analysis system monitors news and social media data in real time, and the AI ​​analyzes this data to identify trends and risk areas. For example, if a particular keyword or phrase is rapidly increasing, it is recognized as a trend. Also, if a particular topic may violate political correctness, it is identified as a risk area. Next, the trend analysis system points out political correctness issues before new services are launched. For example, if a new advertising campaign contains expressions that are inappropriate for a particular group, the trend analysis system points out the issue. Furthermore, the trend analysis system generates guidelines to be applied. For example, it provides specific guidelines such as avoiding certain expressions or being considerate of certain groups. Finally, the trend analysis system provides immediate feedback based on the latest data. This allows companies to minimize risks in real time. For example, before a new service or advertising campaign is launched, the trend analysis system can provide immediate feedback and make necessary corrections. This system enables companies to minimize political correctness-related risks and adapt to social trends. This allows companies to prevent damage from legal and compliance violations and mitigate the risks associated with launching and operating services and operations. Thus, the trend analysis system enables companies to minimize political correctness-related risks and adapt to social trends.

[0069] The trend analysis system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The collection unit collects news and SNS data. The collection unit can collect data from, for example, news sites and SNS platforms. The collection unit can also obtain data using APIs. Furthermore, the collection unit can collect data using web scraping technology. For example, the collection unit obtains the latest news articles using RSS feeds from news sites. The collection unit collects posts related to specific keywords using APIs from SNS platforms. The collection unit collects data from specific websites using web scraping technology. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining technology. For example, the analysis unit analyzes the content of collected news articles and SNS posts using natural language processing technology. The analysis unit classifies the collected data by topic using machine learning algorithms. The analysis unit uses data mining techniques to analyze the frequency of occurrence of specific keywords and phrases and identify trends. The feedback unit points out political correctness issues based on the data analyzed by the analysis unit. For example, the feedback unit can determine whether a particular expression is inappropriate. The feedback unit can also determine whether there is a lack of consideration for a particular group. Furthermore, the feedback unit can determine whether a particular expression violates laws or guidelines. For example, the feedback unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. The feedback unit can determine whether a particular service lacks consideration for a particular group. The feedback unit can determine whether a particular expression violates laws or guidelines. The generation unit generates guidelines based on the issues pointed out by the feedback unit. The generation unit can provide specific guidelines, for example, such as certain expressions that should be avoided.The generation unit can also provide specific guidelines, such as that consideration should be given to certain groups. Furthermore, the generation unit can provide specific guidelines, such as that certain laws or guidelines should be followed. For example, the generation unit can provide guidelines, such as that certain expressions should be avoided. The generation unit can provide guidelines, such as that consideration should be given to certain groups. The generation unit can provide guidelines, such as that certain laws or guidelines should be followed. The provision unit provides immediate feedback based on the guidelines generated by the generation unit. The provision unit can, for example, provide guidelines to companies. The provision unit can also provide feedback on specific services or advertising campaigns. Furthermore, the provision unit can also provide feedback on specific expressions or content. For example, the provision unit can provide guidelines to companies. The provision unit can provide feedback on specific services or advertising campaigns. The provision unit can provide feedback on specific expressions or content. Thus, the trend analysis system according to this embodiment minimizes risk by monitoring news and SNS data in real time, analyzing trends and risk areas, identifying political correctness issues, generating applicable guidelines, and providing immediate feedback.

[0070] The data collection unit collects news and social media data. For example, it can collect data from news sites and social media platforms. It can also obtain data using APIs. Furthermore, it can collect data using web scraping techniques. For instance, it can obtain the latest news articles using RSS feeds from news sites. It can collect posts related to specific keywords using social media platform APIs. It can collect data from specific websites using web scraping techniques. Specifically, it regularly checks RSS feeds from news sites and retrieves the content whenever new articles are published. This allows for the collection of the latest news information in real time. Additionally, by utilizing social media platform APIs, it can efficiently collect posts related to specific keywords and hashtags. For example, it can collect posts about specific events or topics to understand trends. Furthermore, by using web scraping techniques, it can automatically extract necessary data from specific websites. This eliminates the need for manual data collection and allows for the efficient collection of large amounts of data. By combining these methods, the data collection unit collects a wide range of data from diverse sources, building a foundation for trend analysis.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze text data using natural language processing techniques. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining techniques. For example, the analysis unit uses natural language processing techniques to analyze the content of collected news articles and social media posts. Specifically, it performs tokenization, morphological analysis, and grammatical analysis of text data to analyze the content of each post and article in detail. In addition, it uses machine learning algorithms to classify the collected data by topic. For example, it uses clustering algorithms to group posts and articles with similar content and identify major topics. It also uses data mining techniques to analyze the frequency of occurrence of specific keywords and phrases and identify trends. For example, by extracting frequently occurring keywords and phrases and analyzing their fluctuations over time, it is possible to grasp changes in trends. By combining these techniques, the analysis unit can extract useful information from the collected data and quickly and accurately identify trends and risk areas.

[0072] The feedback unit identifies political correctness issues based on data analyzed by the analysis unit. For example, the feedback unit can determine whether a particular expression is inappropriate. It can also determine whether a particular group is being insensitive. Furthermore, it can determine whether a particular expression violates laws or guidelines. For example, the feedback unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. Specifically, it uses natural language processing technology to analyze collected text data and detect whether it contains specific expressions or phrases. In addition, it can use machine learning algorithms to automatically identify inappropriate or insensitive expressions based on past data and examples. By utilizing these technologies, the feedback unit can quickly and accurately identify political correctness issues from the collected data.

[0073] The generation unit generates guidelines based on the issues pointed out by the feedback unit. The generation unit can provide specific guidelines, such as avoiding certain expressions. It can also provide specific guidelines, such as considering certain groups. Furthermore, it can provide specific guidelines, such as adhering to certain laws or guidelines. For example, the generation unit can provide guidelines such as avoiding certain expressions. Specifically, it uses natural language generation technology to automatically generate appropriate expressions and countermeasures for the pointed-out issues. In addition, the generation unit can refer to past cases, laws, and guidelines to provide specific countermeasures and recommendations. This allows the generation unit to quickly provide specific and practical guidelines for the pointed-out issues.

[0074] The service provider provides immediate feedback based on the guidelines generated by the generation service provider. For example, the service provider can provide guidelines to companies. The service provider can also provide feedback on specific services or advertising campaigns. Furthermore, the service provider can provide feedback on specific expressions or content. For example, the service provider can provide guidelines to companies. Specifically, it can notify company representatives of the generated guidelines and encourage appropriate action. The service provider can also provide feedback on specific services or advertising campaigns. For example, if the content of an advertising campaign is inappropriate, it can provide specific instructions for correcting that content. In addition, the service provider can provide feedback on specific expressions or content. For example, if a particular expression is inappropriate, it can provide specific instructions for correcting that expression. This allows the service provider to provide quick and appropriate feedback based on the generated guidelines, minimizing risk.

[0075] The data collection unit can collect news and social media data in real time. For example, the data collection unit collects data in real time from news sites and social media platforms. The data collection unit can also obtain data in real time using APIs. Furthermore, the data collection unit can collect data in real time using web scraping technology. For example, the data collection unit can obtain the latest news articles in real time using RSS feeds from news sites. The data collection unit can collect posts related to specific keywords in real time using APIs from social media platforms. The data collection unit can collect data in real time from specific websites using web scraping technology. This allows for immediate analysis of the latest information by collecting news and social media data in real time. The specific definition and criteria of real time include the frequency of data collection and the acceptable range of latency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from news sites and social media platforms into a generating AI, which can analyze the data in real time and dynamically change the collection target.

[0076] The analysis unit can analyze collected data and identify trends and risk areas. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also classify data using machine learning algorithms. Furthermore, the analysis unit can identify trends and risk areas using data mining technology. For example, the analysis unit can analyze the content of collected news articles and social media posts using natural language processing technology. The analysis unit can classify collected data by topic using machine learning algorithms. The analysis unit can identify trends by analyzing the frequency of occurrence of specific keywords and phrases using data mining technology. This allows for the analysis of collected data and the identification of trends and risk areas, enabling appropriate countermeasures to be taken. Specific definitions and criteria for trends include the duration of the trend and the method of detecting the trend. Specific definitions and criteria for risk areas include the type of risk and the criteria for evaluating the risk. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, which can then identify trends and risk areas.

[0077] The reporting unit can identify political correctness issues before a new service is launched. For example, the reporting unit can determine whether a particular expression is inappropriate. The reporting unit can also determine whether there is a lack of consideration for a particular group. Furthermore, the reporting unit can determine whether a particular expression violates laws or guidelines. For example, the reporting unit can determine whether a particular advertising campaign contains expressions that are inappropriate for a particular group. The reporting unit can determine whether a particular service lacks consideration for a particular group. The reporting unit can determine whether a particular expression violates laws or guidelines. This allows risks to be prevented by identifying political correctness issues before a new service is launched. The specific scope and criteria of the new service include the type of service and the timing of its launch. Some or all of the processing described above in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the content of the new service into a generating AI, which can then identify political correctness issues.

[0078] The generation unit can generate guidelines to be applied based on the identified problems. The generation unit can provide specific guidelines, such as avoiding certain expressions. The generation unit can also provide specific guidelines, such as considering certain groups. Furthermore, the generation unit can provide specific guidelines, such as adhering to certain laws or guidelines. For example, the generation unit can provide guidelines such as avoiding certain expressions. The generation unit can provide guidelines such as considering certain groups. The generation unit can provide guidelines such as adhering to certain laws or guidelines. This allows for the provision of appropriate countermeasures by generating guidelines to be applied based on the identified problems. The specific content and criteria of the guidelines to be applied include the scope of application of the guidelines and specific instructions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the identified problems into a generation AI, which can then generate guidelines to be applied.

[0079] The service provider can provide immediate feedback based on the generated guidelines. For example, the service provider can provide guidelines to companies. The service provider can also provide feedback on specific services or advertising campaigns. Furthermore, the service provider can provide feedback on specific expressions or content. For example, the service provider can provide guidelines to companies. The service provider can provide feedback on specific services or advertising campaigns. The service provider can provide feedback on specific expressions or content. This enables a rapid response by providing immediate feedback based on the generated guidelines. The specific methods and criteria for immediate feedback include the format, method of provision, and timing of the feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated guidelines into a generating AI, and the generating AI can provide immediate feedback.

[0080] The data collection unit can estimate the user's emotions and adjust the types of data it collects based on those emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting positive news and social media posts. If the user is excited, the data collection unit can also prioritize collecting the latest trends and hot topics. Furthermore, if the user is relaxed, the data collection unit can prioritize collecting relaxing content and calming news. This allows for the collection of more appropriate data by adjusting the types of data collected based on the user's emotions. Specific methods and criteria for estimating user emotions include the type of emotion and estimation algorithms. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the collection unit can input user sentiment data into the generation AI and adjust the types of data that the generation AI collects.

[0081] The data collection unit can change the priority of the data it collects based on specific time periods or events. For example, during times when an important event is taking place, the data collection unit will prioritize collecting data related to that event. At night, the data collection unit can also prioritize collecting relaxing content and calming news. On weekends, the data collection unit can also prioritize collecting data related to entertainment and leisure. For example, during times when an important event is taking place, the data collection unit will prioritize collecting data related to that event. At night, the data collection unit will prioritize collecting relaxing content and calming news. On weekends, the data collection unit will prioritize collecting data related to entertainment and leisure. This allows for the priority collection of important data by changing the priority of the data collected based on specific time periods or events. The specific definitions and criteria for specific time periods and events include the type of event and the time range. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the priority of the data to be collected based on specific time periods or events into a generating AI, and the generating AI can change the data priority.

[0082] The data collection unit can monitor the frequency of occurrence of specific keywords and phrases in real time during data collection and dynamically change the data to be collected. For example, if a particular keyword suddenly appears, the data collection unit will prioritize collecting data related to that keyword. If a particular phrase becomes a trend, the data collection unit can also prioritize collecting data related to that phrase. Furthermore, if a particular topic suddenly emerges, the data collection unit can also prioritize collecting data related to that topic. For example, if a particular keyword suddenly appears, the data collection unit will prioritize collecting data related to that keyword. If a particular phrase becomes a trend, the data collection unit will prioritize collecting data related to that phrase. If a particular topic suddenly emerges, the data collection unit will prioritize collecting data related to that topic. This allows for the rapid collection of important data by monitoring the frequency of occurrence of specific keywords and phrases in real time and dynamically changing the data to be collected. The specific definitions and criteria for specific keywords and phrases include keyword selection criteria and phrase detection methods. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the frequency of occurrence of specific keywords or phrases into the generating AI, which can then dynamically change the data to be collected.

[0083] The data collection unit can estimate the user's emotions and set filtering criteria for the data to be collected based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can filter out negative news and social media posts. If the user is feeling excited, the data collection unit can also filter out the latest trends and hot topics. Furthermore, if the user is relaxed, the data collection unit can filter out relaxing content and calming news. For example, if the user is feeling anxious, the data collection unit can filter out negative news and social media posts. If the user is feeling excited, the data collection unit can filter out the latest trends and hot topics. If the user is relaxed, the data collection unit can filter out relaxing content and calming news. This allows for the collection of more appropriate data by setting filtering criteria for the data to be collected based on the user's emotions. Specific methods and criteria for estimating user emotions include the type of emotion and estimation algorithms. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the data collection unit can input user emotion data into the generating AI and set filtering criteria for the data collected by the generating AI.

[0084] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, if an important event is held in a particular region, the data collection unit will prioritize the collection of data from that region. The data collection unit can also prioritize the collection of data from a particular region if a disaster occurs in that region. Furthermore, the data collection unit can also prioritize the collection of data from a particular region if a trend occurs in that region. For example, if an important event is held in a particular region, the data collection unit will prioritize the collection of data from that region. If a disaster occurs in a particular region, the data collection unit will prioritize the collection of data from that region. If a trend occurs in a particular region, the data collection unit will prioritize the collection of data from that region. This allows for the rapid collection of region-specific information by prioritizing the collection of data from specific regions, taking geographical information into consideration. Specific types and criteria of geographical information include the scope of the region and the method of acquiring geographical information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input geographical information into a generating AI, which can then prioritize the collection of data from specific regions.

[0085] The data collection unit can prioritize collecting data from specific social media platforms. For example, if a trend occurs on a particular platform, the data collection unit will prioritize collecting data from that platform. The data collection unit can also prioritize collecting data from a particular platform if an important topic is being discussed on that platform. Furthermore, the data collection unit can prioritize collecting data related to keywords or phrases that are rapidly increasing on a particular platform. For example, if a trend occurs on a particular platform, the data collection unit will prioritize collecting data from that platform. The data collection unit will prioritize collecting data from a particular platform if an important topic is being discussed on that platform. The data collection unit will prioritize collecting data related to keywords or phrases that are rapidly increasing on a particular platform. This allows for the rapid collection of important information by prioritizing the collection of data from specific social media platforms. The specific definition and criteria for a particular platform include the type of platform and selection criteria. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from a particular platform into a generating AI, which can then prioritize the collection of that data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize negative data in its analysis. If the user is excited, the analysis unit can also prioritize the latest trends and hot topics in its analysis. Furthermore, if the user is relaxed, the analysis unit can also prioritize relaxing content and calming news in its analysis. For example, if the user is feeling anxious, the analysis unit will prioritize negative data in its analysis. If the user is excited, the analysis unit will prioritize the latest trends and hot topics in its analysis. If the user is relaxed, the analysis unit will prioritize relaxing content and calming news in its analysis. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided. Specific methods and criteria for estimating user emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the analysis algorithm.

[0087] The analysis unit can evaluate the reliability of the data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can also prioritize the analysis of data from reliable social media accounts. Furthermore, the analysis unit can prioritize the analysis of data from reliable databases. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can prioritize the analysis of data from reliable social media accounts. The analysis unit can prioritize the analysis of data from reliable databases. By evaluating the reliability of the data and prioritizing the analysis of highly reliable data, the accuracy of the analysis results is improved. Specific evaluation methods and criteria for data reliability include reliability evaluation criteria and evaluation algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data reliability into a generating AI, which can then prioritize the analysis of highly reliable data.

[0088] The analysis unit can predict current trends by referring to past trend data during analysis. For example, the analysis unit predicts current trends based on past trend data. The analysis unit can also predict future trends based on past trend data. Furthermore, the analysis unit can predict the frequency of occurrence of specific keywords or phrases based on past trend data. For example, the analysis unit predicts current trends based on past trend data. The analysis unit predicts future trends based on past trend data. The analysis unit predicts the frequency of occurrence of specific keywords or phrases based on past trend data. This makes it possible to predict trends more accurately by referring to past trend data to predict current trends. The specific types and criteria of past trend data include the data period and data acquisition method. The specific definitions and criteria of current trends include the trend detection method and evaluation criteria. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI, and the generating AI can predict current trends.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit provides a simple and highly visible display method. If the user is excited, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes relaxing content or calming news. For example, if the user is feeling anxious, the analysis unit provides a simple and highly visible display method. If the user is excited, the analysis unit provides a display method that includes detailed information. If the user is relaxed, the analysis unit provides a display method that includes relaxing content or calming news. This allows for a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust how the analysis results are displayed.

[0090] The analysis unit can improve the accuracy of its analysis by considering the source of the data. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can also prioritize the analysis of data from reliable social media accounts. Furthermore, the analysis unit can prioritize the analysis of data from reliable databases. For example, the analysis unit can prioritize the analysis of data from reliable news sources. The analysis unit can prioritize the analysis of data from reliable social media accounts. The analysis unit can prioritize the analysis of data from reliable databases. By improving the accuracy of the analysis by considering the source of the data, it is possible to provide more reliable analysis results. Specific types and criteria for data sources include the reliability of the source and the method for evaluating the source. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data sources into a generating AI, which can then prioritize the analysis of highly reliable data.

[0091] The analysis unit can supplement its analysis results by referring to relevant news articles and literature during the analysis process. For example, the analysis unit may refer to relevant news articles to supplement its analysis results. The analysis unit may also refer to relevant literature to supplement its analysis results. Furthermore, the analysis unit may also refer to relevant databases to supplement its analysis results. For example, the analysis unit may refer to relevant news articles to supplement its analysis results. The analysis unit may refer to relevant literature to supplement its analysis results. The analysis unit may refer to relevant databases to supplement its analysis results. This allows for the provision of more comprehensive analysis results by supplementing the analysis results by referring to relevant news articles and literature. The specific types and criteria for relevant news articles and literature include criteria for selecting articles and methods for obtaining literature. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant news articles and literature into a generating AI, which can then supplement the analysis results.

[0092] The feedback function can estimate the user's emotions and adjust the way it expresses its feedback based on those emotions. For example, if the user is feeling anxious, the feedback function will use gentle language. If the user is agitated, the feedback function may also provide detailed explanations. Furthermore, if the user is relaxed, the feedback function may use relaxing language. This allows for more appropriate feedback by adjusting the way feedback is expressed based on the user's emotions. The specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the feedback function is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the feedback function can input user emotion data into the generative AI, which can then adjust the way the feedback is expressed.

[0093] The feedback function can improve the accuracy of its problem identification by referring to past cases when providing feedback. For example, the feedback function identifies problems based on past cases. The feedback function can also improve the accuracy of its problem identification based on past cases. Furthermore, the feedback function can improve its method of identifying problems based on past cases. For example, the feedback function identifies problems based on past cases. The feedback function improves the accuracy of its problem identification based on past cases. The feedback function improves its method of identifying problems based on past cases. This allows for more accurate feedback by improving the accuracy of problem identification by referring to past cases. The specific types and criteria of past cases include the criteria for selecting cases and the method for acquiring cases. Some or all of the above processing in the feedback function may be performed using AI, for example, or without using AI. For example, the feedback function can input past cases into a generating AI, which can improve the accuracy of its problem identification.

[0094] The feedback function can apply feedback algorithms specific to particular industries or fields when providing feedback. For example, the feedback function can apply feedback algorithms specific to a particular industry. The feedback function can also apply feedback algorithms specific to a particular field. Furthermore, the feedback function can develop feedback algorithms specific to particular industries or fields. For example, the feedback function can apply feedback algorithms specific to a particular industry. The feedback function can apply feedback algorithms specific to a particular field. The feedback function can develop feedback algorithms specific to particular industries or fields. This allows for more appropriate feedback by applying feedback algorithms specific to particular industries or fields. The specific definitions and criteria for a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input a feedback algorithm specific to a particular industry or field into a generating AI, and the generating AI can apply the feedback algorithm.

[0095] The feedback function can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is feeling anxious, the feedback function will prioritize highlighting important issues. If the user is agitated, the feedback function may prioritize highlighting detailed issues. If the user is relaxed, the feedback function may prioritize highlighting issues that promote relaxation. For example, if the user is feeling anxious, the feedback function will prioritize highlighting important issues. If the user is agitated, the feedback function will prioritize highlighting detailed issues. If the user is relaxed, the feedback function will prioritize highlighting issues that promote relaxation. This allows for prioritizing feedback based on the user's emotions, thereby prioritizing more important issues. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the feedback function is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the feedback function can input user emotion data into a generating AI, which can then determine the priority of the feedback.

[0096] The feedback function can adjust the importance of a feedback issue based on the data submission date. For example, if the data is old, the feedback function may set the importance lower. If the data is newer, the feedback function may set the importance higher. The feedback function can also adjust the priority of feedback issues based on the data submission date. For example, if the data is old, the feedback function may set the importance lower. If the data is newer, the feedback function may set the importance higher. The feedback function adjusts the priority of feedback issues based on the data submission date. This allows for more appropriate feedback by adjusting the importance of feedback issues based on the data submission date. The specific definition and criteria for data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the feedback function may be performed using AI, for example, or not using AI. For example, the feedback function can input the data submission date into a generating AI, which can then adjust the importance of the feedback issue.

[0097] The reporting function can improve the accuracy of its reports by referring to relevant laws, regulations, and guidelines. For example, the reporting function can improve the accuracy of its reports by referring to relevant laws and regulations. The reporting function can also improve the accuracy of its reports by referring to relevant guidelines. Furthermore, the reporting function can improve the accuracy of its reports based on relevant laws, regulations, and guidelines. For example, the reporting function can improve the accuracy of its reports by referring to relevant laws and regulations. The reporting function can improve the accuracy of its reports by referring to relevant guidelines. The reporting function can improve the accuracy of its reports based on relevant laws, regulations, and guidelines. This makes it possible to make more accurate reports by improving the accuracy of reports by referring to relevant laws, regulations, and guidelines. The specific types and criteria of relevant laws, regulations, and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the reporting function may be performed using AI, for example, or not using AI. For example, the reporting function can input relevant laws, regulations, and guidelines into a generating AI, which can then improve the accuracy of its reports.

[0098] The generation unit can estimate the user's emotions and adjust the way the guidelines are expressed based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will provide guidelines in gentle language. If the user is excited, the generation unit can also provide guidelines with detailed explanations. Furthermore, if the user is relaxed, the generation unit can provide guidelines in relaxing language. For example, if the user is feeling anxious, the generation unit will provide guidelines in gentle language. If the user is excited, the generation unit will provide guidelines with detailed explanations. If the user is relaxed, the generation unit will provide guidelines in relaxing language. This allows for the provision of more appropriate guidelines by adjusting the way the guidelines are expressed based on the user's emotions. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation unit can input user emotion data into the generative AI, which can then adjust the way the guidelines are expressed.

[0099] The generation unit can generate optimal guidelines by referring to past success stories when generating guidelines. For example, the generation unit generates optimal guidelines based on past success stories. The generation unit can also improve the accuracy of the guidelines based on past success stories. Furthermore, the generation unit can improve the way the guidelines are expressed based on past success stories. For example, the generation unit generates optimal guidelines based on past success stories. The generation unit improves the accuracy of the guidelines based on past success stories. The generation unit improves the way the guidelines are expressed based on past success stories. As a result, by generating optimal guidelines by referring to past success stories, more effective guidelines can be provided. The specific types and criteria of past success stories include the criteria for selecting cases and the methods for obtaining cases. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past success stories into a generation AI, and the generation AI can generate optimal guidelines.

[0100] The generation unit can provide guidelines specific to particular industries or fields when generating guidelines. For example, the generation unit can provide guidelines specific to a particular industry. The generation unit can also provide guidelines specific to a particular field. Furthermore, the generation unit can develop guidelines specific to particular industries or fields. For example, the generation unit can provide guidelines specific to a particular industry. The generation unit can provide guidelines specific to a particular field. The generation unit can develop guidelines specific to particular industries or fields. By providing guidelines specific to particular industries or fields, more appropriate guidelines can be provided. The specific definitions and criteria of a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input guidelines specific to a particular industry or field into a generation AI, and the generation AI can provide the guidelines.

[0101] The generation unit can estimate the user's emotions and prioritize guidelines based on those emotions. For example, if the user is feeling anxious, the generation unit will prioritize important guidelines. If the user is excited, the generation unit may also prioritize detailed guidelines. Furthermore, if the user is relaxed, the generation unit may prioritize relaxing guidelines. This allows for prioritizing more important guidelines based on the user's emotions. Specific methods and criteria for estimating user emotions include the type of emotion and estimation algorithms. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then determine the priority of the guidelines.

[0102] The generation unit can adjust the importance of guidelines based on the data submission date when generating guidelines. For example, the generation unit may set a lower importance if the data submission date is old, and a higher importance if the data submission date is new. The generation unit can also adjust the priority of guidelines based on the data submission date. For example, the generation unit may set a lower importance if the data submission date is old, and a higher importance if the data submission date is new. The generation unit adjusts the priority of guidelines based on the data submission date. This allows for the provision of more appropriate guidelines by adjusting the importance of guidelines based on the data submission date. The specific definition and criteria for the data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data submission date into a generation AI, and the generation AI can adjust the importance of the guidelines.

[0103] The generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations and guidelines when generating them. For example, the generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations. The generation unit can also improve the accuracy of the guidelines by referring to relevant guidelines. Furthermore, the generation unit can improve the accuracy of the guidelines based on relevant laws and regulations and guidelines. For example, the generation unit can improve the accuracy of the guidelines by referring to relevant laws and regulations. The generation unit can improve the accuracy of the guidelines by referring to relevant guidelines. The generation unit can improve the accuracy of the guidelines based on relevant laws and regulations and guidelines. This allows for the provision of more accurate guidelines by improving the accuracy of the guidelines by referring to relevant laws and regulations and guidelines. The specific types and standards of relevant laws and regulations and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant laws and regulations and guidelines into a generation AI, which can then improve the accuracy of the guidelines.

[0104] The service provider can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is feeling anxious, the service provider will provide feedback in gentle language. If the user is excited, the service provider may also provide feedback that includes detailed explanations. Furthermore, if the user is relaxed, the service provider may also provide feedback in relaxing language. For example, if the user is feeling anxious, the service provider will provide feedback in gentle language. If the user is excited, the service provider will provide feedback that includes detailed explanations. If the user is relaxed, the service provider will provide feedback in relaxing language. This allows for more appropriate feedback to be provided by adjusting the way feedback is expressed based on the user's emotions. Specific methods and criteria for estimating the user's emotions include the type of emotion and estimation algorithms. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the service provider can input user emotion data into the generative AI, which can then adjust the way feedback is expressed.

[0105] The service provider can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the service provider can provide optimal feedback based on the user's past feedback history. The service provider can also improve the accuracy of the feedback based on the user's past feedback history. Furthermore, the service provider can improve the way the feedback is expressed based on the user's past feedback history. For example, the service provider can provide optimal feedback based on the user's past feedback history. The service provider can improve the accuracy of the feedback based on the user's past feedback history. The service provider can improve the way the feedback is expressed based on the user's past feedback history. This allows for more effective feedback by referring to the user's past feedback history to provide optimal feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback history into a generating AI, which can then provide optimal feedback.

[0106] The service provider can provide feedback that is specific to a particular industry or field when providing feedback. For example, the service provider can provide feedback specific to a particular industry. The service provider can also provide feedback specific to a particular field. Furthermore, the service provider can develop feedback specific to a particular industry or field. For example, the service provider can provide feedback specific to a particular industry. The service provider can provide feedback specific to a particular field. The service provider can develop feedback specific to a particular industry or field. This allows for the provision of more appropriate feedback by providing feedback specific to a particular industry or field. The specific definition and criteria of a particular industry or field include the type of industry and the scope of the field. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback specific to a particular industry or field into a generating AI, and the generating AI can provide the feedback.

[0107] The service provider can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is feeling anxious, the service provider will prioritize important feedback. If the user is excited, the service provider may also prioritize detailed feedback. Furthermore, if the user is relaxed, the service provider may also prioritize relaxing feedback. For example, if the user is feeling anxious, the service provider will prioritize important feedback. If the user is excited, the service provider will prioritize detailed feedback. If the user is relaxed, the service provider will prioritize relaxing feedback. This allows for prioritizing more important feedback by determining the priority of feedback based on the user's emotions. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the service provider can input user emotion data into a generating AI, which can then determine the priority of feedback.

[0108] The data provider can adjust the importance of feedback based on the data submission date when providing feedback. For example, the provider may set the importance lower if the data is old, and higher if the data is new. The provider can also adjust the priority of feedback based on the data submission date. For example, the provider may set the importance lower if the data is old, and higher if the data is new. The provider adjusts the priority of feedback based on the data submission date. This allows for the provision of more appropriate feedback by adjusting the importance of feedback based on the data submission date. The specific definition and criteria for data submission date include the range of submission dates and the evaluation method for submission dates. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the data submission date into a generating AI, which can then adjust the importance of the feedback.

[0109] The service provider can improve the accuracy of feedback by referring to relevant laws, regulations, and guidelines when providing feedback. For example, the service provider can improve the accuracy of feedback by referring to relevant laws and regulations. The service provider can also improve the accuracy of feedback by referring to relevant guidelines. Furthermore, the service provider can improve the accuracy of feedback based on relevant laws, regulations, and guidelines. For example, the service provider can improve the accuracy of feedback by referring to relevant laws and regulations. The service provider can improve the accuracy of feedback by referring to relevant guidelines. The service provider can improve the accuracy of feedback based on relevant laws, regulations, and guidelines. This allows for the provision of more accurate feedback by improving the accuracy of feedback by referring to relevant laws, regulations, and guidelines. The specific types and criteria of relevant laws, regulations, and guidelines include the scope of the laws and regulations and the content of the guidelines. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input relevant laws, regulations, and guidelines into a generating AI, which can then improve the accuracy of the feedback.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, it can provide a simple and highly visible display method. If the user is excited, it can provide a display method that includes detailed information. If the user is relaxed, it can provide a display method that includes relaxing content or calming news. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the display method of the analysis results.

[0112] The data collection unit can change the priority of data collected based on specific time periods or events. For example, during times when important events are taking place, data related to those events can be prioritized. At night, relaxing content and calming news can be prioritized. On weekends, data related to entertainment and leisure can be prioritized. This allows for the priority collection of important data by changing the priority of data collected based on specific time periods or events. The specific definitions and criteria for specific time periods and events include the type of event and the time range. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the priority of data to be collected based on specific time periods or events into a generating AI, which can then change the data priority.

[0113] The feedback unit can estimate the user's emotions and adjust the way it expresses its feedback based on those emotions. For example, if the user is feeling anxious, it can offer feedback in gentle language. If the user is excited, it can offer feedback that includes detailed explanations. If the user is relaxed, it can offer feedback in a relaxing manner. By adjusting the way feedback is expressed based on the user's emotions, more appropriate feedback can be provided. The specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the feedback unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the feedback unit can input user emotion data into the generative AI, which can then adjust the way it expresses its feedback.

[0114] The analysis unit can evaluate the reliability of the data during analysis and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data from reliable news sources. It can also prioritize the analysis of data from reliable social media accounts. Furthermore, it can prioritize the analysis of data from reliable databases. By evaluating the reliability of the data and prioritizing the analysis of highly reliable data, the accuracy of the analysis results is improved. Specific evaluation methods and criteria for data reliability include reliability evaluation criteria and evaluation algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data reliability into a generating AI, which can then prioritize the analysis of highly reliable data.

[0115] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is feeling anxious, it can prioritize collecting positive news and social media posts. If the user is excited, it can prioritize collecting the latest trends and hot topics. If the user is relaxed, it can prioritize collecting relaxing content and calming news. By adjusting the types of data collected based on the user's emotions, more relevant data can be collected. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the data collection unit can input user emotion data into the generative AI and adjust the types of data the generative AI collects.

[0116] The feedback function can improve the accuracy of its problem identification by referring to past cases. For example, it can identify problems based on past cases. It can also improve the accuracy of problem identification based on past cases. Furthermore, it can improve the method of identifying problems based on past cases. By improving the accuracy of problem identification by referring to past cases, more accurate identification becomes possible. The specific types and criteria of past cases include the criteria for selecting cases and the methods for acquiring cases. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input past cases into a generating AI, which can then improve the accuracy of its problem identification.

[0117] The generation unit can estimate the user's emotions and adjust the way the guidelines are expressed based on those emotions. For example, if the user is feeling anxious, the guidelines can be provided in gentle language. If the user is excited, the guidelines can include detailed explanations. If the user is relaxed, the guidelines can be provided in relaxing language. By adjusting the way the guidelines are expressed based on the user's emotions, more appropriate guidelines can be provided. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation unit can input user emotion data into the generative AI, which can then adjust the way the guidelines are expressed.

[0118] The analysis unit can predict current trends by referring to past trend data during analysis. For example, it can predict current trends based on past trend data. It can also predict future trends based on past trend data. Furthermore, it can predict the frequency of occurrence of specific keywords or phrases based on past trend data. This allows for more accurate trend prediction by referring to past trend data to predict current trends. The specific types and criteria of past trend data include the data period and data acquisition method. The specific definition and criteria of current trends include the trend detection method and evaluation criteria. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI, and the generating AI can predict current trends.

[0119] The service provider can estimate the user's emotions and adjust the way feedback is expressed based on those emotions. For example, if the user is feeling anxious, it can provide feedback in gentle language. If the user is excited, it can provide feedback that includes detailed explanations. If the user is relaxed, it can provide feedback in relaxing language. By adjusting the way feedback is expressed based on the user's emotions, more appropriate feedback can be provided. Specific methods and criteria for estimating the user's emotions include the type of emotion and the estimation algorithm. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the service provider can input user emotion data into the generative AI, which can then adjust the way feedback is expressed.

[0120] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, if an important event is held in a particular region, data from that region can be prioritized. If a disaster occurs in a particular region, data from that region can also be prioritized. Furthermore, if a trend occurs in a particular region, data from that region can also be prioritized. This allows for the rapid collection of region-specific information by prioritizing data collection from specific regions, taking geographical information into consideration. Specific types and criteria of geographical information include the scope of the region and the method of acquiring geographical information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input geographical information into a generating AI, which can then prioritize the collection of data from specific regions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects news and social media data. The collection unit can collect data from news sites and social media platforms, and can also obtain data using APIs. It can also collect data using web scraping techniques. For example, it can use RSS feeds from news sites to obtain the latest news articles and use APIs from social media platforms to collect posts related to specific keywords. Furthermore, it can use web scraping techniques to collect data from specific websites. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze text data using natural language processing techniques and classify data using machine learning algorithms. It can also identify trends and risk areas using data mining techniques. For example, it can analyze the content of collected news articles and social media posts, classify the collected data by topic, and identify trends by analyzing the frequency of occurrence of specific keywords and phrases. Step 3: The reporting team identifies political correctness issues based on the data analyzed by the analysis team. The reporting team can determine whether a particular expression is inappropriate, lacks consideration for a particular group, or violates laws or guidelines. For example, they might determine whether a particular advertising campaign contains inappropriate language for a particular group, whether a particular service lacks consideration for a particular group, or whether a particular expression violates laws or guidelines. Step 4: The generating unit generates guidelines based on the issues identified by the identifying unit. The generating unit can provide specific guidelines such as avoiding certain expressions, being considerate of certain groups, or following specific laws and guidelines. For example, it can provide guidelines such as avoiding certain expressions, being considerate of certain groups, or following specific laws and guidelines. Step 5: The providing unit provides immediate feedback based on the guidelines generated by the generating unit. The providing unit can provide guidelines to companies, provide feedback on specific services or advertising campaigns, and provide feedback on specific expressions or content. For example, it can provide guidelines to companies, provide feedback on specific services or advertising campaigns, and provide feedback on specific expressions or content.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects news and SNS data using the communication I / F 44 of the smart device 14 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies political correctness issues based on the analyzed data. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates guidelines based on the identified issues. The provision unit is implemented in the control unit 46A of the smart device 14 and provides immediate feedback based on the generated guidelines. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects news and SNS data using the communication I / F 44 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies political correctness issues based on the analyzed data. The generation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and generates guidelines based on the identified issues. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides immediate feedback based on the generated guidelines. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects news and SNS data using the communication I / F 44 of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies political correctness issues based on the analyzed data. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates guidelines based on the identified issues. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides immediate feedback based on the generated guidelines. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects news and SNS data using the communication I / F 44 of the robot 414 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies political correctness issues based on the analyzed data. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates guidelines based on the identified issues. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides immediate feedback based on the generated guidelines. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The data collection department collects news and social media data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the identification unit points out problems with political correctness, A generation unit that generates guidelines based on the problems pointed out by the aforementioned pointing unit, The system includes a providing unit that provides immediate feedback based on guidelines generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect news and social media data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data to identify trends and risk areas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned point is, Pointing out political correctness issues before a new service is launched. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate guidelines to be applied based on the identified issues. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide immediate feedback based on the generated guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Change the priority of data collected based on specific time periods or events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the frequency of specific keywords and phrases is monitored in real time, and the data collection targets are dynamically changed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate the user's emotions and set filtering criteria for the data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Taking geographical information into consideration, prioritize the collection of data from specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Prioritize collecting data from specific social media platforms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the reliability of the data is evaluated, and the most reliable data is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, historical trend data is referenced to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing data, consider the source of the data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis results are supplemented by referring to relevant news articles and literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned point is, It estimates the user's emotions and adjusts the way criticisms are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned point is, When pointing out issues, refer to past cases to improve the accuracy of identifying problems. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned point is, When issuing a warning, an error detection algorithm tailored to a specific industry or field is applied. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned point is, It estimates the user's emotions and prioritizes the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned point is, When issuing a complaint, the importance of the complaint will be adjusted based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned point is, When making a complaint, we will improve the accuracy of the complaint by referring to relevant laws, regulations, and guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is We estimate the user's emotions and adjust the wording of the guidelines based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating guidelines, we refer to past success stories to generate the most optimal guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating guidelines, provide guidelines that are specific to particular industries or fields. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is We estimate user sentiment and determine the priority of guidelines based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating guidelines, adjust the importance of the guidelines based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating guidelines, we refer to relevant laws, regulations, and guidelines to improve the accuracy of the guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing feedback, provide feedback that is specific to a particular industry or field. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing feedback, adjust the importance of the feedback based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing feedback, we will refer to relevant laws, regulations, and guidelines to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects news and social media data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the identification unit points out problems with political correctness, A generation unit that generates guidelines based on the problems pointed out by the aforementioned pointing unit, The system includes a providing unit that provides immediate feedback based on guidelines generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect news and social media data in real time. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data to identify trends and risk areas. The system according to feature 1.

4. The aforementioned point is, Pointing out political correctness issues before a new service is launched. The system according to feature 1.

5. The generating unit is Generate guidelines to be applied based on the identified issues. The system according to feature 1.

6. The aforementioned supply unit is, Provide immediate feedback based on the generated guidelines. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Change the priority of data collected based on specific time periods or events. The system according to feature 1.

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