system

Abstract

The system according to this embodiment aims to detect harmful information on the internet in real time and promptly notify users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an alert generation unit, and a notification unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The notification unit notifies the user of the alerts generated by the alert generation unit.

Description

Technical Field

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[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, detecting harmful information on the Internet in real time and quickly notifying users have not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to detect harmful information on the Internet in real time and quickly notify users.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, an alert generation unit, and a notification unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The notification unit notifies the user of the alerts generated by the alert generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect harmful information on the internet in real time and quickly notify users. [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 a plurality of computers. Examples of communication standards applied 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 2, 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 AI ​​agent service according to an embodiment of the present invention is a system that detects harmful information such as "illegal jobs" and scams posted on social media and job sites on the web in real time and sends alerts to users. This system starts with the user creating an account and setting the notification frequency and the type of risk alert. Next, it monitors registered social media accounts and job sites in real time and collects information from specified data sources. The generating AI analyzes the collected information, identifies information that is illegal or suspected of being fraudulent, and immediately notifies the user of the detected harmful information. By clicking the alert link, the user can access a detailed page of the risk information and learn how to deal with the risk through educational resources (guides and videos). Furthermore, it collects feedback from users and continuously trains and improves the AI ​​model. It establishes data linkage with major social media platforms and job sites and develops versions that are suited to the characteristics and regulations of each region. For example, when a user creates an account, they can finely set the notification frequency and the type of risk alert. Next, the system monitors registered social media accounts and job sites in real time and collects information from specified data sources. The generating AI analyzes the collected information and identifies information that is illegal or suspected of being fraudulent. For example, the generative AI analyzes information based on specific keywords and phrases to detect illegal content or information suspected of being fraudulent. The detected harmful information immediately alerts the user. By clicking the alert link, the user can access a detailed page of the risk information and learn how to address the risk through educational resources (guides and videos). Furthermore, the system collects user feedback to continuously train and improve the AI ​​model. For instance, the model is retrained to improve the accuracy of the generative AI's analysis based on user feedback. Data integration with major social networking platforms and job sites is established, and versions tailored to the characteristics and regulations of each region are developed.This allows the AI ​​agent service to detect harmful information such as "illegal jobs" and scams posted on social media and online job sites in real time and send alerts to users. This enables the AI ​​agent service to prevent users from becoming victims of the increasing number of illegal job postings and scams on the internet, providing a safe environment for online activities.

[0029] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, an alert generation unit, and a notification unit. The data collection unit collects data. The data collection unit can collect data from, for example, social media or job search websites. The data collection unit can collect data based on, for example, specific keywords or phrases. The data collection unit can collect information from, for example, data sources specified by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. The analysis unit can analyze the data using, for example, machine learning algorithms. The analysis unit can analyze the data using, for example, natural language processing technology. The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The alert generation unit can generate alerts based on, for example, identified harmful information. The alert generation unit can generate alerts to notify the user. The alert generation unit can customize the content of the alerts. The notification unit notifies the user of the alerts generated by the alert generation unit. The notification unit can notify the user of alerts by methods such as email notifications, in-app notifications, and SMS notifications. The notification unit can notify the user of alerts by a notification method specified by the user. The notification unit can notify the user of the content of the alert in an easy-to-understand manner. As a result, the AI ​​agent service according to the embodiment can efficiently collect, analyze, generate alerts, and send notifications.

[0030] The data collection unit collects data. For example, the data collection unit can collect data from social media and job search websites. Specifically, the data collection unit can use social media APIs to collect posts related to specific keywords and hashtags. This allows the data collection unit to obtain real-time information on trends and topics. It can also collect job information related to specific occupations and skills from job search websites. This allows the data collection unit to understand labor market trends and job posting patterns. Furthermore, the data collection unit can collect information from data sources specified by the user. For example, if a user specifies a particular news site or blog, the data collection unit can collect the latest articles and posts from these sites. The data collection unit can also use web scraping technology to extract necessary information from specified web pages. This allows the data collection unit to collect a wide range of information from diverse data sources and collect data according to the user's needs. The data collection unit centrally manages the collected data and makes it accessible to the analysis unit and alert generation unit. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data to identify illegal content or information suspected of being fraudulent. Specifically, the analysis unit can analyze the data using machine learning algorithms. For example, by training models for spam filtering and fraud detection and applying them to the collected data, suspicious content can be automatically detected. Furthermore, the analysis unit can analyze the data using natural language processing techniques. For example, it can perform sentiment analysis of text to classify positive and negative content. It can also use topic modeling to extract key topics and themes from the collected data. By combining these techniques, the analysis unit can analyze the collected data from multiple perspectives and provide information useful to users. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to respond to future predictions and risk assessments. The analysis unit provides the analysis results to the alert generation unit, providing basic information for generating appropriate alerts. This allows the analysis unit to improve the reliability and security of the entire system.

[0032] The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. For example, the alert generation unit can generate alerts based on identified harmful information. Specifically, if the analysis unit identifies information that is illegal or suspected of being fraudulent, the alert generation unit immediately generates an alert and prepares to notify the user. The alert generation unit can customize the content of alerts according to user settings. For example, if information related to specific keywords or phrases is detected, it can generate an alert that describes that information in detail. It can also select different notification methods depending on the importance and urgency of the alert. For example, it is possible to set it so that high-urgency alerts are immediately notified via SMS, and low-priority alerts are notified via email. Furthermore, the alert generation unit can integrate multiple alerts and prioritize notifying the user of the information most important to them. This allows the alert generation unit to provide users with quick and appropriate information, supporting early detection and response to risks. The alert generation unit provides the generated alerts to the notification unit, which prepares them for notification to the user. This allows the alert generation unit to improve the overall efficiency and effectiveness of the system.

[0033] The notification unit notifies users of alerts generated by the alert generation unit. The notification unit can notify users of alerts through methods such as email notifications, in-app notifications, and SMS notifications. Specifically, the notification unit appropriately formats the content of the alert and sends it quickly according to the notification method specified by the user. For example, in the case of email notifications, it automatically generates an email containing detailed information about the alert and sends it to the user's email address. In the case of in-app notifications, it sends a push notification to the user's smartphone or tablet and displays the content of the alert. In the case of SMS notifications, it can send a summary of the alert in a short message format and provide a link to detailed information. It is important for the notification unit to notify users of the content of alerts in an easy-to-understand manner so that users can quickly check the alert and take appropriate action. Furthermore, the notification unit can collect user feedback and use it to improve notification methods and alert content. For example, if a user submits opinions or requests regarding the content of the alert or notification method, the notification unit can analyze this feedback and reflect it in improving the system. This allows the notification unit to provide information to users quickly and reliably, improving the overall reliability of the system and user satisfaction.

[0034] The data collection unit can collect data from social media and job search websites. For example, the data collection unit can collect data from social media. For example, the data collection unit can collect data from job search websites. For example, the data collection unit can collect data from social media and job search websites based on specific keywords or phrases. This makes it possible to detect harmful information by collecting data from social media and job search websites. 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 social media and job search websites into a generating AI, which can then analyze the data.

[0035] The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. For example, the analysis unit can analyze the collected data and identify illegal content. For example, the analysis unit can analyze the collected data and identify information that is suspected of being fraudulent. For example, the analysis unit can analyze the data using machine learning algorithms and identify information that is illegal or suspected of being fraudulent. This allows for the sending of alerts to users by identifying information that is illegal or suspected of being fraudulent. 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 the collected data into a generating AI, which can then analyze the data.

[0036] The alert generation unit can generate alerts based on identified harmful information. For example, the alert generation unit can generate alerts based on identified harmful information. For example, the alert generation unit can generate alerts to notify users. For example, the alert generation unit can customize the content of the alerts. This allows for quick notification to users by generating alerts based on harmful information. Some or all of the above-described processes in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input identified harmful information into a generation AI, and the generation AI can generate alerts.

[0037] The notification unit can notify the user of the generated alert. The notification unit can notify the user of the alert by methods such as email notification, in-app notification, or SMS notification. The notification unit can notify the user of the alert by a notification method specified by the user. The notification unit can notify the user of the content of the alert in an easy-to-understand manner. This allows the user to respond quickly by notifying them of the generated alert. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the generated alert into a generation AI, which can then determine the notification method.

[0038] The educational resource provision unit can provide users with information on how to identify and protect against harmful information. For example, the educational resource provision unit can provide users with information on how to identify harmful information. For example, the educational resource provision unit can provide users with information on how to protect against harmful information. The educational resource provision unit can provide users with information on how to identify and protect against harmful information through guides and videos, for example. This allows users to learn how to identify and protect against harmful information. Some or all of the above processing in the educational resource provision unit may be performed using, for example, generative AI, or without generative AI. For example, the educational resource provision unit can use generative AI to provide users with the most suitable educational resources.

[0039] The feedback collection unit can collect feedback from users and improve the AI ​​model. For example, the feedback collection unit can collect feedback from users. For example, the feedback collection unit can improve the AI ​​model based on the collected feedback. For example, the feedback collection unit can collect feedback from users through surveys or comments. By collecting feedback from users, the accuracy of the AI ​​model is improved. Some or all of the above processing in the feedback collection unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback collection unit can analyze the collected feedback using generative AI and use it to improve the AI ​​model.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize the collection of data sources that the user has frequently collected in the past. For example, the data collection unit can predict the data to be collected at a specific time period based on the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the user's past data collection history and optimize the collection frequency and method. This allows the optimal collection method to be selected by analyzing the user's past data collection history. 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 the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0041] The data collection unit can filter data based on the user's current areas of interest and activities during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current areas of interest. For example, the data collection unit can filter and collect highly relevant data based on the user's current activities. For example, the data collection unit can analyze the user's areas of interest and activities in real time and collect the most relevant data. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and activities. 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 the user's areas of interest and activities into a generating AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 the user's geographical location information into a generating AI, which can then select highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can collect highly relevant data based on the user's social media follow and like history. For example, the data collection unit can analyze the user's social media activity patterns and collect optimal data. In this way, highly relevant data can be collected by analyzing the user's social media activity. 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 the user's social media activity into a generating AI, which can then select relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a fraud detection algorithm to fraudulent information. For example, the analysis unit can apply a job posting analysis algorithm to illegal job postings. The analysis unit can select and apply the most suitable analysis algorithm depending on the data category. By applying the most suitable analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 category into a generating AI, which can then select the most suitable analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data while referring to past data. For example, the analysis unit can dynamically adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0048] The alert generation unit can adjust the level of detail of alerts based on the importance of the data when generating alerts. For example, the alert generation unit can generate detailed alerts for high-importance data. For example, the alert generation unit can generate simplified alerts for low-importance data. The alert generation unit can dynamically adjust the level of detail of alerts according to the importance of the data. This enables efficient alert generation by adjusting the level of detail of alerts based on the importance of the data. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the alerts.

[0049] The alert generation unit can apply different alert generation algorithms depending on the data category when generating alerts. For example, the alert generation unit can apply a fraud detection algorithm to generate alerts for fraudulent information. For example, the alert generation unit can apply a job posting analysis algorithm to generate alerts for illegal job postings. The alert generation unit can select and apply the most suitable alert generation algorithm depending on the data category. This improves the accuracy of alerts by applying the most suitable alert generation algorithm depending on the data category. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the data category into a generation AI, which can then select the most suitable alert generation algorithm.

[0050] The alert generation unit can determine the priority of alerts based on the data collection timing when generating alerts. For example, the alert generation unit can prioritize the generation of alerts based on the latest data. For example, the alert generation unit can generate alerts with emphasis on the latest data while referring to past data. For example, the alert generation unit can dynamically adjust the alert priority according to the data collection timing. This allows for the priority generation of alerts based on the latest data by determining the alert priority based on the data collection timing. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the data collection timing to a generation AI, which can then determine the alert priority.

[0051] The alert generation unit can adjust the order of alerts based on the relevance of the data when generating alerts. For example, the alert generation unit can prioritize the generation of alerts based on highly relevant data. For example, the alert generation unit can postpone the generation of alerts based on less relevant data. For example, the alert generation unit can dynamically adjust the order of alerts according to the relevance of the data. This enables efficient alert generation by adjusting the order of alerts based on the relevance of the data. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the relevance of the data into a generation AI, which can then adjust the order of alerts.

[0052] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has previously preferred (e.g., push notifications, email). For example, the notification unit can select the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and optimize the notification frequency and method. This allows the system to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0053] The notification unit can adjust the timing of notifications based on the user's current activity status. For example, if the user is working, the notification unit can be discreet in sending notifications and only send important ones. For example, if the user is on a break, the notification unit can actively send notifications to facilitate information reception. For example, the notification unit can analyze the user's current activity status in real time and select the optimal notification timing. This allows notifications to be delivered at the appropriate time by adjusting the timing based on the user's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current activity status into a generating AI, which can then adjust the timing of notifications.

[0054] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can prioritize notifying information related to the user's current location. For example, the notification unit can notify region-specific information based on the user's geographical location information. For example, the notification unit can notify highly relevant information by taking into account the user's travel history. This allows for the provision of highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI, which can then select the optimal notification method.

[0055] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can prioritize sending push notifications. For example, if the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. In this way, the optimal notification method can be provided by taking into account the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then select the optimal notification method.

[0056] The educational resource provision unit can provide the most suitable resources by referring to the user's past learning history when providing educational resources. For example, the educational resource provision unit can provide relevant educational resources based on what the user has learned in the past. For example, the educational resource provision unit can provide resources related to a specific field from the user's past learning history. For example, the educational resource provision unit can analyze the user's past learning history and select the most suitable educational resources. This allows the system to provide the most suitable educational resources by referring to the user's past learning history. Some or all of the above processes in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's past learning history into a generating AI, which can then select the most suitable educational resources.

[0057] The educational resource provision unit can customize resources based on the user's current areas of interest when providing educational resources. For example, the educational resource provision unit can provide educational resources related to the user's current areas of interest. For example, the educational resource provision unit can provide customized educational resources based on the user's current areas of interest. For example, the educational resource provision unit can analyze the user's areas of interest in real time and provide the most suitable educational resources. This allows for the provision of highly relevant educational resources by customizing resources based on the user's current areas of interest. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's areas of interest into a generating AI, which can then select the most suitable educational resources.

[0058] The educational resource provision unit can provide optimal resources by considering the user's geographical location information when providing educational resources. For example, the educational resource provision unit can prioritize providing educational resources related to the user's current location. For example, the educational resource provision unit can provide region-specific educational resources based on the user's geographical location information. For example, the educational resource provision unit can provide highly relevant educational resources by considering the user's travel history. In this way, highly relevant educational resources can be provided by considering the user's geographical location information. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal educational resources.

[0059] The educational resource provision unit can provide optimal resources by considering the user's device information when providing educational resources. For example, if the user is using a smartphone, the educational resource provision unit can provide mobile-optimized educational resources. For example, if the user is using a tablet, the educational resource provision unit can provide educational resources optimized for a large screen. For example, if the user is using a smartwatch, the educational resource provision unit can provide concise and highly visible educational resources. In this way, the optimal educational resources can be provided by considering the user's device information. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's device information into a generating AI, and the generating AI can select the optimal educational resources.

[0060] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can prioritize selecting feedback collection methods that the user has preferred to use in the past (such as surveys or comments). For example, the feedback collection unit can select the optimal collection method for a specific time period based on the user's past feedback history. For example, the feedback collection unit can analyze the user's past feedback history and optimize the collection frequency and method. This allows the optimal collection method to be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's past feedback history into a generating AI, which can then select the optimal collection method.

[0061] The feedback collection unit can adjust the timing of feedback collection based on the user's current activity status. For example, if the user is working, the feedback collection unit can limit feedback collection and collect only important feedback. For example, if the user is on a break, the feedback collection unit can actively collect feedback to facilitate information reception. For example, the feedback collection unit can analyze the user's current activity status in real time and select the optimal timing for feedback collection. This allows feedback to be collected at the appropriate time by adjusting the timing of collection based on the user's current activity status. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's current activity status into a generating AI, which can then adjust the timing of collection.

[0062] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback related to the user's current location. For example, the feedback collection unit can collect region-specific feedback based on the user's geographical location information. For example, the feedback collection unit can collect highly relevant feedback by taking into account the user's travel history. This allows for the collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI, which can then select the optimal collection method.

[0063] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit can provide a mobile-optimized feedback collection method. For example, if the user is using a tablet, the feedback collection unit can provide a feedback collection method optimized for a large screen. For example, if the user is using a smartwatch, the feedback collection unit can provide a concise and highly visible feedback collection method. This allows the optimal feedback collection method to be provided by taking into account the user's device information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's device information into a generating AI, which can then select the optimal collection method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize the collection of data sources that the user has frequently collected in the past. Based on the user's past data collection history, it can predict the data to be collected at a specific time period and select the optimal collection method. By analyzing the user's past data collection history, it can optimize the collection frequency and method. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis can be performed on data with high importance, while a simplified analysis can be performed on data with low importance. The level of detail of the analysis can be dynamically adjusted according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0067] The alert generation unit can apply different alert generation algorithms depending on the data category when generating alerts. For example, for fraudulent information, a fraud detection algorithm can be applied to generate an alert. For illegal job postings, a job posting analysis algorithm can be applied to generate an alert. The optimal alert generation algorithm can be selected and applied depending on the data category. This improves the accuracy of alerts by applying the optimal alert generation algorithm according to the data category. Some or all of the above processing in the alert generation unit may be performed using AI or not. For example, the alert generation unit can input the data category into a generation AI, which can then select the optimal alert generation algorithm.

[0068] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, it can prioritize notification methods that the user has previously preferred (push notifications, email, etc.). It can also select the optimal notification method for a specific time period based on the user's past notification history. By analyzing the user's past notification history, it can optimize the notification frequency and method. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0069] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, it can prioritize selecting feedback collection methods that the user has preferred in the past (such as surveys or comments). It can also select the optimal collection method for a specific time period based on the user's past feedback history. By analyzing the user's past feedback history, it can optimize the collection frequency and method. This allows the optimal collection method to be selected by referring to the user's past feedback history. Some or all of the above processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into a generating AI, which can then select the optimal collection method.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The collection unit collects data. The collection unit can collect data from sources such as social media and job search websites. The collection unit can collect data based on specific keywords or phrases, and can collect information from data sources specified by the user. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. The analysis unit can analyze the data using machine learning algorithms and natural language processing techniques. Step 3: The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The alert generation unit can generate alerts based on identified harmful information and notify the user. The content of the alerts can also be customized. Step 4: The notification unit notifies the user of the alert generated by the alert generation unit. The notification unit can notify the user of the alert via methods such as email notification, in-app notification, or SMS notification. The notification unit can notify the user of the alert using the notification method specified by the user and can clearly communicate the content of the alert to the user.

[0072] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system that detects harmful information such as "illegal jobs" and scams posted on social media and job sites on the web in real time and sends alerts to users. This system starts with the user creating an account and setting the notification frequency and the type of risk alert. Next, it monitors registered social media accounts and job sites in real time and collects information from specified data sources. The generating AI analyzes the collected information, identifies information that is illegal or suspected of being fraudulent, and immediately notifies the user of the detected harmful information. By clicking the alert link, the user can access a detailed page of the risk information and learn how to deal with the risk through educational resources (guides and videos). Furthermore, it collects feedback from users and continuously trains and improves the AI ​​model. It establishes data linkage with major social media platforms and job sites and develops versions that are suited to the characteristics and regulations of each region. For example, when a user creates an account, they can finely set the notification frequency and the type of risk alert. Next, the system monitors registered social media accounts and job sites in real time and collects information from specified data sources. The generating AI analyzes the collected information and identifies information that is illegal or suspected of being fraudulent. For example, the generative AI analyzes information based on specific keywords and phrases to detect illegal content or information suspected of being fraudulent. The detected harmful information immediately alerts the user. By clicking the alert link, the user can access a detailed page of the risk information and learn how to address the risk through educational resources (guides and videos). Furthermore, the system collects user feedback to continuously train and improve the AI ​​model. For instance, the model is retrained to improve the accuracy of the generative AI's analysis based on user feedback. Data integration with major social networking platforms and job sites is established, and versions tailored to the characteristics and regulations of each region are developed.This allows the AI ​​agent service to detect harmful information such as "illegal jobs" and scams posted on social media and online job sites in real time and send alerts to users. This enables the AI ​​agent service to prevent users from becoming victims of the increasing number of illegal job postings and scams on the internet, providing a safe environment for online activities.

[0073] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, an alert generation unit, and a notification unit. The data collection unit collects data. The data collection unit can collect data from, for example, social media or job search websites. The data collection unit can collect data based on, for example, specific keywords or phrases. The data collection unit can collect information from, for example, data sources specified by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. The analysis unit can analyze the data using, for example, machine learning algorithms. The analysis unit can analyze the data using, for example, natural language processing technology. The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The alert generation unit can generate alerts based on, for example, identified harmful information. The alert generation unit can generate alerts to notify the user. The alert generation unit can customize the content of the alerts. The notification unit notifies the user of the alerts generated by the alert generation unit. The notification unit can notify the user of alerts by methods such as email notifications, in-app notifications, and SMS notifications. The notification unit can notify the user of alerts by a notification method specified by the user. The notification unit can notify the user of the content of the alert in an easy-to-understand manner. As a result, the AI ​​agent service according to the embodiment can efficiently collect, analyze, generate alerts, and send notifications.

[0074] The data collection unit collects data. For example, the data collection unit can collect data from social media and job search websites. Specifically, the data collection unit can use social media APIs to collect posts related to specific keywords and hashtags. This allows the data collection unit to obtain real-time information on trends and topics. It can also collect job information related to specific occupations and skills from job search websites. This allows the data collection unit to understand labor market trends and job posting patterns. Furthermore, the data collection unit can collect information from data sources specified by the user. For example, if a user specifies a particular news site or blog, the data collection unit can collect the latest articles and posts from these sites. The data collection unit can also use web scraping technology to extract necessary information from specified web pages. This allows the data collection unit to collect a wide range of information from diverse data sources and collect data according to the user's needs. The data collection unit centrally manages the collected data and makes it accessible to the analysis unit and alert generation unit. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0075] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data to identify illegal content or information suspected of being fraudulent. Specifically, the analysis unit can analyze the data using machine learning algorithms. For example, by training models for spam filtering and fraud detection and applying them to the collected data, suspicious content can be automatically detected. Furthermore, the analysis unit can analyze the data using natural language processing techniques. For example, it can perform sentiment analysis of text to classify positive and negative content. It can also use topic modeling to extract key topics and themes from the collected data. By combining these techniques, the analysis unit can analyze the collected data from multiple perspectives and provide information useful to users. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to respond to future predictions and risk assessments. The analysis unit provides the analysis results to the alert generation unit, providing basic information for generating appropriate alerts. This allows the analysis unit to improve the reliability and security of the entire system.

[0076] The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. For example, the alert generation unit can generate alerts based on identified harmful information. Specifically, if the analysis unit identifies information that is illegal or suspected of being fraudulent, the alert generation unit immediately generates an alert and prepares to notify the user. The alert generation unit can customize the content of alerts according to user settings. For example, if information related to specific keywords or phrases is detected, it can generate an alert that describes that information in detail. It can also select different notification methods depending on the importance and urgency of the alert. For example, it is possible to set it so that high-urgency alerts are immediately notified via SMS, and low-priority alerts are notified via email. Furthermore, the alert generation unit can integrate multiple alerts and prioritize notifying the user of the information most important to them. This allows the alert generation unit to provide users with quick and appropriate information, supporting early detection and response to risks. The alert generation unit provides the generated alerts to the notification unit, which prepares them for notification to the user. This allows the alert generation unit to improve the overall efficiency and effectiveness of the system.

[0077] The notification unit notifies users of alerts generated by the alert generation unit. The notification unit can notify users of alerts through methods such as email notifications, in-app notifications, and SMS notifications. Specifically, the notification unit appropriately formats the content of the alert and sends it quickly according to the notification method specified by the user. For example, in the case of email notifications, it automatically generates an email containing detailed information about the alert and sends it to the user's email address. In the case of in-app notifications, it sends a push notification to the user's smartphone or tablet and displays the content of the alert. In the case of SMS notifications, it can send a summary of the alert in a short message format and provide a link to detailed information. It is important for the notification unit to notify users of the content of alerts in an easy-to-understand manner so that users can quickly check the alert and take appropriate action. Furthermore, the notification unit can collect user feedback and use it to improve notification methods and alert content. For example, if a user submits opinions or requests regarding the content of the alert or notification method, the notification unit can analyze this feedback and reflect it in improving the system. This allows the notification unit to provide information to users quickly and reliably, improving the overall reliability of the system and user satisfaction.

[0078] The data collection unit can collect data from social media and job search websites. For example, the data collection unit can collect data from social media. For example, the data collection unit can collect data from job search websites. For example, the data collection unit can collect data from social media and job search websites based on specific keywords or phrases. This makes it possible to detect harmful information by collecting data from social media and job search websites. 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 social media and job search websites into a generating AI, which can then analyze the data.

[0079] The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. For example, the analysis unit can analyze the collected data and identify illegal content. For example, the analysis unit can analyze the collected data and identify information that is suspected of being fraudulent. For example, the analysis unit can analyze the data using machine learning algorithms and identify information that is illegal or suspected of being fraudulent. This allows for the sending of alerts to users by identifying information that is illegal or suspected of being fraudulent. 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 the collected data into a generating AI, which can then analyze the data.

[0080] The alert generation unit can generate alerts based on identified harmful information. For example, the alert generation unit can generate alerts based on identified harmful information. For example, the alert generation unit can generate alerts to notify users. For example, the alert generation unit can customize the content of the alerts. This allows for quick notification to users by generating alerts based on harmful information. Some or all of the above-described processes in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input identified harmful information into a generation AI, and the generation AI can generate alerts.

[0081] The notification unit can notify the user of the generated alert. The notification unit can notify the user of the alert by methods such as email notification, in-app notification, or SMS notification. The notification unit can notify the user of the alert by a notification method specified by the user. The notification unit can notify the user of the content of the alert in an easy-to-understand manner. This allows the user to respond quickly by notifying them of the generated alert. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the generated alert into a generation AI, which can then determine the notification method.

[0082] The educational resource provision unit can provide users with information on how to identify and protect against harmful information. For example, the educational resource provision unit can provide users with information on how to identify harmful information. For example, the educational resource provision unit can provide users with information on how to protect against harmful information. The educational resource provision unit can provide users with information on how to identify and protect against harmful information through guides and videos, for example. This allows users to learn how to identify and protect against harmful information. Some or all of the above processing in the educational resource provision unit may be performed using, for example, generative AI, or without generative AI. For example, the educational resource provision unit can use generative AI to provide users with the most suitable educational resources.

[0083] The feedback collection unit can collect feedback from users and improve the AI ​​model. For example, the feedback collection unit can collect feedback from users. For example, the feedback collection unit can improve the AI ​​model based on the collected feedback. For example, the feedback collection unit can collect feedback from users through surveys or comments. By collecting feedback from users, the accuracy of the AI ​​model is improved. Some or all of the above processing in the feedback collection unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback collection unit can analyze the collected feedback using generative AI and use it to improve the AI ​​model.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more information. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary information. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. 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 user emotion data into a generative AI, which can then adjust the timing of data collection.

[0085] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize the collection of data sources that the user has frequently collected in the past. For example, the data collection unit can predict the data to be collected at a specific time period based on the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the user's past data collection history and optimize the collection frequency and method. This allows the optimal collection method to be selected by analyzing the user's past data collection history. 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 the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0086] The data collection unit can filter data based on the user's current areas of interest and activities during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current areas of interest. For example, the data collection unit can filter and collect highly relevant data based on the user's current activities. For example, the data collection unit can analyze the user's areas of interest and activities in real time and collect the most relevant data. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and activities. 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 the user's areas of interest and activities into a generating AI, which can then filter the data.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can collect a wide range of data to ensure information diversity. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. 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 user emotion data into a generative AI, and the generative AI can determine the priority of the data.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 the user's geographical location information into a generating AI, which can then select highly relevant data.

[0089] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can collect highly relevant data based on the user's social media follow and like history. For example, the data collection unit can analyze the user's social media activity patterns and collect optimal data. In this way, highly relevant data can be collected by analyzing the user's social media activity. 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 the user's social media activity into a generating AI, which can then select relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, 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. 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 the user's emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a fraud detection algorithm to fraudulent information. For example, the analysis unit can apply a job posting analysis algorithm to illegal job postings. The analysis unit can select and apply the most suitable analysis algorithm depending on the data category. By applying the most suitable analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 category into a generating AI, which can then select the most suitable analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an appropriate result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data while referring to past data. For example, the analysis unit can dynamically adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0096] The alert generation unit can estimate the user's emotions and adjust the alert generation method based on the estimated emotions. For example, if the user is nervous, the alert generation unit can generate a simple and highly visible alert. For example, if the user is relaxed, the alert generation unit can generate an alert containing detailed information. For example, if the user is in a hurry, the alert generation unit can generate a concise alert. In this way, by adjusting the alert generation method according to the user's emotions, the system can provide the user with an appropriate alert. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the alert generation unit may be performed using AI, for example, or not using AI. For example, the alert generation unit can input user emotion data into the generative AI, which can then adjust the alert generation method.

[0097] The alert generation unit can adjust the level of detail of alerts based on the importance of the data when generating alerts. For example, the alert generation unit can generate detailed alerts for high-importance data. For example, the alert generation unit can generate simplified alerts for low-importance data. The alert generation unit can dynamically adjust the level of detail of alerts according to the importance of the data. This enables efficient alert generation by adjusting the level of detail of alerts based on the importance of the data. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the alerts.

[0098] The alert generation unit can apply different alert generation algorithms depending on the data category when generating alerts. For example, the alert generation unit can apply a fraud detection algorithm to generate alerts for fraudulent information. For example, the alert generation unit can apply a job posting analysis algorithm to generate alerts for illegal job postings. The alert generation unit can select and apply the most suitable alert generation algorithm depending on the data category. This improves the accuracy of alerts by applying the most suitable alert generation algorithm depending on the data category. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the data category into a generation AI, which can then select the most suitable alert generation algorithm.

[0099] The alert generation unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert generation unit can prioritize the generation of high-priority alerts. For example, if the user is relaxed, the alert generation unit can generate a wide range of alerts to ensure information diversity. For example, if the user is in a hurry, the alert generation unit can prioritize the generation of alerts that require immediate attention. In this way, important alerts can be prioritized by determining the priority of alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the alert generation unit may be performed using AI, or not using AI. For example, the alert generation unit can input user emotion data into a generative AI, which can then determine the priority of alerts.

[0100] The alert generation unit can determine the priority of alerts based on the data collection timing when generating alerts. For example, the alert generation unit can prioritize the generation of alerts based on the latest data. For example, the alert generation unit can generate alerts with emphasis on the latest data while referring to past data. For example, the alert generation unit can dynamically adjust the alert priority according to the data collection timing. This allows for the priority generation of alerts based on the latest data by determining the alert priority based on the data collection timing. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the data collection timing to a generation AI, which can then determine the alert priority.

[0101] The alert generation unit can adjust the order of alerts based on the relevance of the data when generating alerts. For example, the alert generation unit can prioritize the generation of alerts based on highly relevant data. For example, the alert generation unit can postpone the generation of alerts based on less relevant data. For example, the alert generation unit can dynamically adjust the order of alerts according to the relevance of the data. This enables efficient alert generation by adjusting the order of alerts based on the relevance of the data. Some or all of the above processing in the alert generation unit may be performed using AI, for example, or without AI. For example, the alert generation unit can input the relevance of the data into a generation AI, which can then adjust the order of alerts.

[0102] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the notification unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the notification unit can provide a notification method that gets straight to the point. In this way, by adjusting the notification method according to the user's emotions, the system can provide notifications that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into the generative AI, and the generative AI can adjust the notification method.

[0103] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has previously preferred (e.g., push notifications, email). For example, the notification unit can select the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and optimize the notification frequency and method. This allows the system to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0104] The notification unit can adjust the timing of notifications based on the user's current activity status. For example, if the user is working, the notification unit can be discreet in sending notifications and only send important ones. For example, if the user is on a break, the notification unit can actively send notifications to facilitate information reception. For example, the notification unit can analyze the user's current activity status in real time and select the optimal notification timing. This allows notifications to be delivered at the appropriate time by adjusting the timing based on the user's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current activity status into a generating AI, which can then adjust the timing of notifications.

[0105] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can prioritize sending high-priority notifications. For example, if the user is relaxed, the notification unit can send a wide range of notifications to ensure information diversity. For example, if the user is in a hurry, the notification unit can prioritize sending notifications that require immediate attention. This ensures that important notifications are delivered preferentially by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can then determine the priority of notifications.

[0106] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can prioritize notifying information related to the user's current location. For example, the notification unit can notify region-specific information based on the user's geographical location information. For example, the notification unit can notify highly relevant information by taking into account the user's travel history. This allows for the provision of highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI, which can then select the optimal notification method.

[0107] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can prioritize sending push notifications. For example, if the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. In this way, the optimal notification method can be provided by taking into account the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then select the optimal notification method.

[0108] The educational resource provider can estimate the user's emotions and adjust the method of providing educational resources based on the estimated emotions. For example, if the user is nervous, the educational resource provider can provide simple and highly visible educational resources. For example, if the user is relaxed, the educational resource provider can provide educational resources that include detailed information. For example, if the user is in a hurry, the educational resource provider can provide educational resources that get straight to the point. In this way, by adjusting the method of providing educational resources according to the user's emotions, appropriate educational resources can be provided to the user. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the educational resource provider may be performed using AI, for example, or not using AI. For example, the educational resource provider can input user emotion data into a generative AI, and the generative AI can adjust the method of providing educational resources.

[0109] The educational resource provision unit can provide the most suitable resources by referring to the user's past learning history when providing educational resources. For example, the educational resource provision unit can provide relevant educational resources based on what the user has learned in the past. For example, the educational resource provision unit can provide resources related to a specific field from the user's past learning history. For example, the educational resource provision unit can analyze the user's past learning history and select the most suitable educational resources. This allows the system to provide the most suitable educational resources by referring to the user's past learning history. Some or all of the above processes in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's past learning history into a generating AI, which can then select the most suitable educational resources.

[0110] The educational resource provision unit can customize resources based on the user's current areas of interest when providing educational resources. For example, the educational resource provision unit can provide educational resources related to the user's current areas of interest. For example, the educational resource provision unit can provide customized educational resources based on the user's current areas of interest. For example, the educational resource provision unit can analyze the user's areas of interest in real time and provide the most suitable educational resources. This allows for the provision of highly relevant educational resources by customizing resources based on the user's current areas of interest. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's areas of interest into a generating AI, which can then select the most suitable educational resources.

[0111] The educational resource provision unit can estimate the user's emotions and prioritize educational resources based on the estimated emotions. For example, if the user is stressed, the educational resource provision unit can prioritize providing high-priority educational resources. For example, if the user is relaxed, the educational resource provision unit can provide a wide range of educational resources to ensure information diversity. For example, if the user is in a hurry, the educational resource provision unit can prioritize providing educational resources that require immediate attention. In this way, by prioritizing educational resources according to the user's emotions, important educational resources can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the educational resource provision unit may be performed using AI or not using AI. For example, the educational resource provision unit can input user emotion data into a generative AI, which can then determine the priority of educational resources.

[0112] The educational resource provision unit can provide optimal resources by considering the user's geographical location information when providing educational resources. For example, the educational resource provision unit can prioritize providing educational resources related to the user's current location. For example, the educational resource provision unit can provide region-specific educational resources based on the user's geographical location information. For example, the educational resource provision unit can provide highly relevant educational resources by considering the user's travel history. In this way, highly relevant educational resources can be provided by considering the user's geographical location information. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal educational resources.

[0113] The educational resource provision unit can provide optimal resources by considering the user's device information when providing educational resources. For example, if the user is using a smartphone, the educational resource provision unit can provide mobile-optimized educational resources. For example, if the user is using a tablet, the educational resource provision unit can provide educational resources optimized for a large screen. For example, if the user is using a smartwatch, the educational resource provision unit can provide concise and highly visible educational resources. In this way, the optimal educational resources can be provided by considering the user's device information. Some or all of the above processing in the educational resource provision unit may be performed using AI, for example, or without AI. For example, the educational resource provision unit can input the user's device information into a generating AI, and the generating AI can select the optimal educational resources.

[0114] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is nervous, the feedback collection unit can provide a simple and highly visible feedback collection method. For example, if the user is relaxed, the feedback collection unit can provide a method for collecting detailed feedback. For example, if the user is in a hurry, the feedback collection unit can provide a concise feedback collection method. By adjusting the feedback collection method according to the user's emotions, it becomes possible to collect appropriate feedback for the user. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's emotion data into the generative AI, and the generative AI can adjust the feedback collection method.

[0115] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can prioritize selecting feedback collection methods that the user has preferred to use in the past (such as surveys or comments). For example, the feedback collection unit can select the optimal collection method for a specific time period based on the user's past feedback history. For example, the feedback collection unit can analyze the user's past feedback history and optimize the collection frequency and method. This allows the optimal collection method to be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's past feedback history into a generating AI, which can then select the optimal collection method.

[0116] The feedback collection unit can adjust the timing of feedback collection based on the user's current activity status. For example, if the user is working, the feedback collection unit can limit feedback collection and collect only important feedback. For example, if the user is on a break, the feedback collection unit can actively collect feedback to facilitate information reception. For example, the feedback collection unit can analyze the user's current activity status in real time and select the optimal timing for feedback collection. This allows feedback to be collected at the appropriate time by adjusting the timing of collection based on the user's current activity status. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's current activity status into a generating AI, which can then adjust the timing of collection.

[0117] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can prioritize collecting high-priority feedback. For example, if the user is relaxed, the feedback collection unit can collect a wide range of feedback to ensure information diversity. For example, if the user is in a hurry, the feedback collection unit can prioritize collecting feedback that requires a quick response. This ensures that important feedback is collected preferentially by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above processing in the feedback collection unit may be performed using AI or not using AI. For example, the feedback collection unit can input user emotion data into a generative AI, which can then determine the priority of feedback.

[0118] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback related to the user's current location. For example, the feedback collection unit can collect region-specific feedback based on the user's geographical location information. For example, the feedback collection unit can collect highly relevant feedback by taking into account the user's travel history. This allows for the collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI, which can then select the optimal collection method.

[0119] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit can provide a mobile-optimized feedback collection method. For example, if the user is using a tablet, the feedback collection unit can provide a feedback collection method optimized for a large screen. For example, if the user is using a smartwatch, the feedback collection unit can provide a concise and highly visible feedback collection method. This allows the optimal feedback collection method to be provided by taking into account the user's device information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's device information into a generating AI, which can then select the optimal collection method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, the frequency of data collection can be increased to collect more information. If the user is in a hurry, the timing of data collection can be adjusted to quickly collect the necessary information. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can then adjust the timing of data collection.

[0122] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. If the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0123] The alert generation unit can estimate the user's emotions and adjust the alert generation method based on the estimated emotions. For example, if the user is nervous, it can generate a simple and highly visible alert. If the user is relaxed, it can generate an alert containing detailed information. If the user is in a hurry, it can generate a concise alert. By adjusting the alert generation method according to the user's emotions, it is possible to provide the user with an appropriate alert. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above processing in the alert generation unit may be performed using AI or not. For example, the alert generation unit can input user emotion data into a generation AI, which can then adjust the alert generation method.

[0124] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, a simple and highly visible notification method can be provided. If the user is relaxed, a notification method containing detailed information can be provided. If the user is in a hurry, a notification method that gets straight to the point can be provided. In this way, by adjusting the notification method according to the user's emotions, appropriate notifications can be provided to the user. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI, and the generative AI can adjust the notification method.

[0125] The educational resource provision unit can estimate the user's emotions and adjust the method of providing educational resources based on the estimated emotions. For example, if the user is nervous, simple and highly visual educational resources can be provided. If the user is relaxed, educational resources containing detailed information can be provided. If the user is in a hurry, educational resources that get straight to the point can be provided. In this way, by adjusting the method of providing educational resources according to the user's emotions, appropriate educational resources can be provided to the user. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the educational resource provision unit may be performed using AI or not using AI. For example, the educational resource provision unit can input user emotion data into a generative AI, and the generative AI can adjust the method of providing educational resources.

[0126] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize the collection of data sources that the user has frequently collected in the past. Based on the user's past data collection history, it can predict the data to be collected at a specific time period and select the optimal collection method. By analyzing the user's past data collection history, it can optimize the collection frequency and method. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0127] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis can be performed on data with high importance, while a simplified analysis can be performed on data with low importance. The level of detail of the analysis can be dynamically adjusted according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0128] The alert generation unit can apply different alert generation algorithms depending on the data category when generating alerts. For example, for fraudulent information, a fraud detection algorithm can be applied to generate an alert. For illegal job postings, a job posting analysis algorithm can be applied to generate an alert. The optimal alert generation algorithm can be selected and applied depending on the data category. This improves the accuracy of alerts by applying the optimal alert generation algorithm according to the data category. Some or all of the above processing in the alert generation unit may be performed using AI or not. For example, the alert generation unit can input the data category into a generation AI, which can then select the optimal alert generation algorithm.

[0129] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, it can prioritize notification methods that the user has previously preferred (push notifications, email, etc.). It can also select the optimal notification method for a specific time period based on the user's past notification history. By analyzing the user's past notification history, it can optimize the notification frequency and method. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0130] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, it can prioritize selecting feedback collection methods that the user has preferred in the past (such as surveys or comments). It can also select the optimal collection method for a specific time period based on the user's past feedback history. By analyzing the user's past feedback history, it can optimize the collection frequency and method. This allows the optimal collection method to be selected by referring to the user's past feedback history. Some or all of the above processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into a generating AI, which can then select the optimal collection method.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects data. The collection unit can collect data from sources such as social media and job search websites. The collection unit can collect data based on specific keywords or phrases, and can collect information from data sources specified by the user. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data and identify information that is illegal or suspected of being fraudulent. The analysis unit can analyze the data using machine learning algorithms and natural language processing techniques. Step 3: The alert generation unit generates alerts based on the analysis results obtained by the analysis unit. The alert generation unit can generate alerts based on identified harmful information and notify the user. The content of the alerts can also be customized. Step 4: The notification unit notifies the user of the alert generated by the alert generation unit. The notification unit can notify the user of the alert via methods such as email notification, in-app notification, or SMS notification. The notification unit can notify the user of the alert using the notification method specified by the user and can clearly communicate the content of the alert to the user.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, alert generation unit, notification unit, educational resource provision unit, and feedback collection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The alert generation unit is implemented by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The educational resource provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The feedback collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, alert generation unit, notification unit, educational resource provision unit, and feedback collection unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing device 12. The alert generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12. The notification unit is implemented by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The educational resource provision unit is implemented by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The feedback collection unit is implemented by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, alert generation unit, notification unit, educational resource provision unit, and feedback collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing device 12. The alert generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The educational resource provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The feedback collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the data collection unit, analysis unit, alert generation unit, notification unit, educational resource provision unit, and feedback collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The alert generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The educational resource provision unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The feedback collection unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An alert generation unit generates an alert based on the analysis results obtained by the analysis unit, The system includes a notification unit that notifies the user of alerts generated by the alert generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from social media and job search websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify information that is illegal or suspected of being fraudulent. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert generation unit, Generate alerts based on identified harmful information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify the user of the generated alerts. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with an educational resources provision department, Provide users with information on how to identify and protect themselves from harmful content. The system described in Appendix 1, characterized by the features described herein. (Note 7) Equipped with a feedback collection unit, We collect user feedback and improve our AI models. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and activities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert generation unit, It estimates the user's emotions and adjusts how alerts are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert generation unit, When generating alerts, adjust the level of detail of the alerts based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert generation unit, When generating alerts, different alert generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert generation unit, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert generation unit, When generating an alert, the alert priority is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert generation unit, When generating alerts, adjust the order of alerts based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The Education Resources Provision Department, We estimate user sentiment and adjust how educational resources are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The Education Resources Provision Department, When providing educational resources, we refer to the user's past learning history to provide the most suitable resources. The system described in Appendix 1, characterized by the features described herein. (Note 34) The Education Resources Provision Department, When providing educational resources, customize the resources based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 35) The Education Resources Provision Department, It estimates user sentiment and prioritizes educational resources based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The Education Resources Provision Department, When providing educational resources, we consider the user's geographical location to provide the most suitable resources. The system described in Appendix 1, characterized by the features described herein. (Note 37) The Education Resources Provision Department, When providing educational resources, we consider the user's device information to provide the most suitable resources. The system described in Appendix 1, characterized by the features described herein. (Note 38) The feedback collection unit is, We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The feedback collection unit is, When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The feedback collection unit is, When collecting feedback, adjust the timing of collection based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 41) The feedback collection 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 42) The feedback collection unit is, When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The feedback collection unit is, When collecting feedback, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 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. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An alert generation unit generates an alert based on the analysis results obtained by the analysis unit, The system includes a notification unit that notifies the user of alerts generated by the alert generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from social media and job search websites. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify information that is illegal or suspected of being fraudulent. The system according to feature 1.

4. The alert generation unit, Generate alerts based on identified harmful information. The system according to feature 1.

5. The aforementioned notification unit, Notify the user of the generated alerts. The system according to feature 1.

6. Equipped with an educational resources provision department, Provide users with information on how to identify and protect themselves from harmful content. The system according to feature 1.

7. Equipped with a feedback collection unit, We collect user feedback to improve our AI models. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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