system

Abstract

The system according to this embodiment aims to enable users with low internet literacy to communicate online safely. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a monitoring unit, and a provision unit. The reception unit receives input from the user. The analysis unit analyzes the other party's message based on the information received by the reception unit. The monitoring unit monitors the security of the message analyzed by the analysis unit. The provision unit provides information to the user based on the information monitored by the monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [ In the conventional technology, there is a risk that minors and the elderly with low digital literacy may be involved in crimes on SNS or part-time job recruitment sites.

[0005] The system according to the embodiment aims to enable users with low digital literacy to communicate online safely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a monitoring unit, and a provision unit. The reception unit receives input from the user. The analysis unit analyzes the other party's message based on the information received by the reception unit. The monitoring unit monitors the security of the message analyzed by the analysis unit. The provision unit provides information to the user based on the information monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users with low internet literacy to communicate online safely. [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 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a 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 safety communication support system according to an embodiment of the present invention is a system for reducing the risk of minors and elderly people with low internet literacy becoming involved in crime when using social networking services (SNS) or job recruitment sites. When a user starts communicating on an SNS or job recruitment site, the system has an AI agent that performs initial communication such as greetings and safety checks to confirm the safety of the other party and then hands over the subsequent communication to the user. For example, if a user types "Hello, I'm Yamada!", the AI ​​agent can return a message such as "Mr. / Ms. Yamada, there is a high possibility that the person you are communicating with is a malicious user." Next, the AI ​​agent constantly monitors whether the other party's message is dangerous. For example, if the other party sends a message such as "Let's talk on this site! http: / / ○○", the AI ​​agent can warn, "The site you have provided is a paid site, so please do not access it under any circumstances." The system also includes a function to check whether the content entered by the user is emotional and to confirm sending if necessary. For example, if a user is about to send an emotional message, the AI ​​agent will provide advice such as "This message is too emotional, please reconsider before sending." This system allows minors and elderly people with low internet literacy to safely use social media and job recruitment sites. For example, when a user applies for a job on a job recruitment site, the AI ​​agent can provide advice such as, "This job is clearly suspicious. Searching for cats late at night and paying this much is odd. Also, this company has no track record of recruiting in the past, so it may not be safe," thereby reducing the risk of the user becoming involved in a crime. Furthermore, the AI ​​agent can verify the safety of the linked site and the company recruiting the job. For example, if a user enters, "I want to apply for a job at this company," the AI ​​agent can provide information such as, "This company has no track record of recruiting in the past, so it may not be safe." This allows users to use social media and job recruitment sites with peace of mind.This allows the safe communication support system to enable minors and elderly people with low internet literacy to use social media and job recruitment sites safely.

[0029] The security communication support system according to the embodiment comprises a reception unit, an analysis unit, a monitoring unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text messages, images, and audio. The reception unit can, for example, receive text messages. The reception unit can also receive images and audio. For example, the reception unit can receive an image sent by a user and send it to the analysis unit. The analysis unit analyzes the other party's message based on the information received by the reception unit. The analysis unit can, for example, analyze the security of links included in the other party's message. The analysis unit can also analyze the content of the other party's message and determine whether it poses any danger. For example, the analysis unit analyzes whether a link included in the other party's message is malicious. The monitoring unit monitors the security of the messages analyzed by the analysis unit. The monitoring unit can, for example, constantly monitor the other party's message and notify the user if there is any danger. The monitoring unit can also monitor the content of the other party's message and issue a warning if there is any danger. For example, the monitoring unit sends a warning message to the user if a link included in the other party's message is dangerous. The provision unit provides information to the user based on the information monitored by the monitoring unit. The provision unit can, for example, provide the user with safety advice. The provision unit can also check whether the content entered by the user is emotional and, if necessary, confirm the transmission. For example, if the provision unit sees a user attempting to send an emotional message, it will prompt the user to reconfirm before sending. In this way, the safety communication support system according to the embodiment allows users to use social networking services and job recruitment sites safely.

[0030] The reception unit receives input from the user. User input includes, but is not limited to, text messages, images, and audio. The reception unit can, for example, receive text messages. It can also receive images and audio. For example, the reception unit can receive images sent by the user and send them to the analysis unit. Specifically, the reception unit has the function of receiving text messages entered by the user in real time and immediately forwarding them to the analysis unit. In the case of images, the reception unit checks the image format and resolution, converts it to an appropriate format, and then sends it to the analysis unit. For audio input, it can convert it to text using speech recognition technology and send the content to the analysis unit. Furthermore, the reception unit can temporarily store the user's input and resend or correct it as needed. For example, if a user wants to correct a message that was sent by mistake, the reception unit will accept the request and send the corrected message to the analysis unit. The reception unit can also manage the user's input history and refer to past messages, images, and audio data. This allows the user to enter new messages while reviewing past interactions. The reception unit is designed for intuitive operation through its user interface, allowing users to easily send messages, images, and audio. For example, users can easily upload images using drag-and-drop functionality, or record and send voice messages by simply pressing a voice input button. This enables the reception unit to efficiently receive diverse inputs from users and quickly forward them to the analysis unit.

[0031] The analysis unit analyzes the sender's message based on the information received by the reception unit. For example, the analysis unit can analyze the security of links included in the sender's message. The analysis unit can also analyze the content of the sender's message and determine if it poses any danger. For example, the analysis unit can analyze whether a link included in the sender's message is malicious. Specifically, the analysis unit uses AI to analyze the content of text messages and detect the possibility of spam or phishing. The AI ​​uses natural language processing technology to understand the context of the message and identify dangerous keywords and phrases. It also uses image analysis technology to detect inappropriate content or dangerous elements in received images. For example, by extracting text from an image using OCR technology and analyzing its content, dangerous links or messages can be identified. For voice messages, speech recognition technology is used to convert them into text and analyze the content. Based on these analysis results, the analysis unit evaluates the security of the message and issues warnings as necessary. Furthermore, the analysis unit can also predict the danger level of a message by utilizing past data and statistical information. For example, if similar messages have been reported as spam or phishing in the past, that information can be used to evaluate the danger level of the current message. Furthermore, the analysis unit can analyze the user's behavior history and message sending patterns to detect abnormal behavior. This allows the analysis unit to enhance the security of messages received by users, enabling them to communicate with peace of mind.

[0032] The monitoring unit monitors the security of messages analyzed by the analysis unit. For example, the monitoring unit can constantly monitor the sender's messages and notify the user if there is any danger. The monitoring unit can also monitor the content of the sender's messages and issue warnings if there is any danger. For example, if the monitoring unit finds a dangerous link in the sender's message, it will send a warning message to the user. Specifically, the monitoring unit monitors the security of messages in real time based on the analysis results provided by the analysis unit. The monitoring unit uses AI to continuously evaluate the content of messages and immediately notifies the user if any dangerous elements are detected. For example, if a message containing a phishing link or malware is detected, the monitoring unit will issue a warning to the user and urge them not to click the link. The monitoring unit can also monitor the user's message sending history and detect unusual patterns or suspicious behavior. For example, if a large number of messages are sent at an unusual time, the monitoring unit will detect the anomaly and ask the user for confirmation. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy of its monitoring algorithms. For example, if a user reports a dangerous message, the monitoring algorithm is adjusted based on that information to detect similar messages more quickly in the future. This allows the monitoring unit to enhance the security of messages users receive, enabling them to communicate with peace of mind.

[0033] The service provider provides information to users based on data monitored by the monitoring unit. For example, the service provider can provide users with security advice. It can also check whether user input is emotionally charged and, if necessary, confirm its submission. For instance, if a user attempts to send an emotionally charged message, the service provider may prompt them to reconsider before sending. Specifically, the service provider uses AI to analyze user input and detect emotional expressions or aggressive language. The AI ​​uses natural language processing to understand the context of the message and identify emotional tones and aggressive phrases. For example, if a user uses language expressing anger or frustration, the service provider may temporarily hold the message and prompt the user to reconsider. Furthermore, the service provider can advise users on safe communication methods and precautions. For example, it may provide information on how to identify phishing emails and how to set secure passwords. In addition, the service provider can collect user feedback to continuously improve the accuracy and usefulness of the advice it provides. For example, users can evaluate the advice provided, and the advice can be improved based on that evaluation. Furthermore, the service provider can analyze users' behavioral history and message sending patterns to provide individually customized advice. This allows the service provider to support users in communicating safely and enable them to use social media and job recruitment sites with peace of mind.

[0034] The analysis unit can analyze whether the recipient's message poses any risks. For example, the analysis unit can analyze whether a link included in the recipient's message is malicious. For example, the analysis unit can analyze whether a link included in the recipient's message leads to a phishing site. The analysis unit can also analyze whether a link included in the recipient's message contains malware. For example, the analysis unit can analyze whether a link included in the recipient's message contains a virus. By analyzing the risks of the recipient's message, the user's safety can be ensured. 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 a link included in the recipient's message into a generating AI and have the generating AI perform an analysis of the link's safety.

[0035] The monitoring unit can constantly monitor the other party's messages and notify the user if there is any danger. For example, the monitoring unit can monitor the other party's messages in real time and immediately notify the user if there is any danger. The monitoring unit can also periodically scan the other party's messages and warn the user if there is any danger. For example, the monitoring unit can scan the other party's messages daily and notify the user if there is any danger. The monitoring unit can also constantly monitor the other party's messages and display a pop-up notification if there is any danger. For example, the monitoring unit can display a pop-up notification on the user's screen if there is any danger in the other party's messages. This ensures the user's safety by constantly monitoring the other party's messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the other party's messages into a generating AI and have the generating AI perform message monitoring.

[0036] The service provider can check whether the content entered by the user is emotional and, if necessary, confirm its transmission. For example, if the service provider attempts to send an emotional message, it may prompt the user to reconfirm before sending. For example, if the service provider attempts to send a message containing emotions such as anger or sadness, it may prompt the user to reconfirm before sending. The service provider can also analyze the emotions using an emotion analysis algorithm when the user attempts to send an emotional message and prompt the user to reconfirm before sending. For example, if the service provider attempts to send an emotional message, it may analyze the emotions using an emotion analysis algorithm and provide advice such as, "This message is too emotional, please reconfirm before sending." This allows for appropriate communication by checking whether the user's input is emotional. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's input into a generating AI and have the generating AI perform the emotion analysis.

[0037] The service provider can verify the safety of linked sites and job-recruiting companies. For example, the service provider can evaluate the reliability of linked sites to verify their safety. The service provider can also perform security scans on linked sites to verify their safety. For example, the service provider can analyze the URL of a linked site to check whether it is a phishing site or a site containing malware. The service provider can also evaluate the past recruitment record of job-recruiting companies to verify their safety. For example, the service provider can investigate the past recruitment record of job-recruiting companies to verify their safety. In this way, user safety can be ensured by verifying the safety of linked sites and job-recruiting companies. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or without AI. For example, the service provider can input the URL of a linked site into a generating AI and have the generating AI perform an analysis of the safety of the linked site.

[0038] The service provider can provide users with safety advice. For example, the service provider can provide users with security guidelines. The service provider can also send users with warning messages. For example, the service provider can send users with warning messages such as, "This link is dangerous. Do not access it." The service provider can also provide users with detailed reports. For example, the service provider can provide users with detailed reports such as, "This job recruitment company has no prior recruitment history, so it may not be safe." By providing users with safety advice, the service provider can ensure their safety. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of the advice to be provided to the user into a generating AI and have the generating AI generate the advice.

[0039] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete similar input content by referring to content that the user has entered in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter input content based on the user's current situation and areas of interest when receiving it. For example, the reception unit can suggest appropriate input content based on the user's current situation (e.g., working, on break, etc.). The reception unit can also prioritize receiving relevant input content based on the user's areas of interest (e.g., hobbies, work, etc.). The reception unit can also filter out unnecessary input content based on the user's current situation and areas of interest. This allows the reception unit to receive appropriate input content by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving highly relevant content by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, if the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, by considering the user's geographical location, highly relevant content can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant content.

[0042] The reception unit can analyze the user's social media activity when receiving input and accept relevant content. For example, the reception unit can prioritize accepting content related to topics that the user frequently mentions on social media. For example, the reception unit can analyze the user's social media activity history and suggest relevant content. For example, the reception unit can prioritize accepting content related to accounts that the user follows on social media. In this way, relevant content can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant content.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, in the case of an important message, the analysis unit can analyze it in detail and provide important information to the user. For example, in the case of a normal message, the analysis unit can apply a normal analysis method to facilitate smooth communication. For example, in the case of an urgent message, the analysis unit can analyze it quickly and proceed to the next step immediately. This allows for the appropriate analysis of important information by adjusting the level of detail of the analysis based on the importance of the message. 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 message importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, in the case of a business message, the analysis unit can apply a business-specific analysis algorithm to provide appropriate information. For example, in the case of a private message, the analysis unit can apply a private-specific analysis algorithm to provide appropriate information. For example, in the case of an urgent message, the analysis unit can apply an urgent-specific analysis algorithm to respond quickly. This allows for appropriate analysis by applying different analysis algorithms depending on the message category. 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 message category data into a generating AI and have the generating AI select an analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the message transmission timing during analysis. For example, the analysis unit can prioritize the analysis of messages sent at critical times to provide important information to the user. For example, the analysis unit can also analyze messages sent at normal times with normal priority to facilitate smooth communication. For example, the analysis unit can prioritize the analysis of messages sent in emergencies to enable a rapid response. In this way, by determining the priority of analysis based on the message transmission timing, important information can be appropriately analyzed. 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 message transmission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize the analysis of messages with high relevance to provide important information to the user. For example, the analysis unit can also analyze messages with normal relevance in the normal order to facilitate smooth communication. For example, the analysis unit can prioritize the analysis of messages with urgent relevance to enable a quick response. In this way, important information can be appropriately analyzed by adjusting the order of analysis based on the relevance of messages. 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 message relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of messages during monitoring. For example, the monitoring unit can analyze the interrelationships of messages and monitor related messages collectively. For example, the monitoring unit can also prioritize monitoring important messages by considering the interrelationships of messages. For example, the monitoring unit can apply algorithms to improve the accuracy of monitoring based on the interrelationships of messages. This allows for improved monitoring accuracy by considering the interrelationships of messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input message interrelationship data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0048] The monitoring unit can perform monitoring while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, the monitoring unit can perform strict monitoring. For example, if the message sender is a trustworthy user, the monitoring unit can perform normal monitoring. The monitoring unit can also apply algorithms to improve the accuracy of monitoring based on the attribute information of the message sender. This improves the accuracy of monitoring by considering the attribute information of the message sender. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information data of the message sender into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0049] The monitoring unit can perform monitoring while considering the geographical distribution of messages. For example, if the message sources are concentrated in a particular region, the monitoring unit can prioritize monitoring information related to that region. For example, if the message sources are widely distributed, the monitoring unit can also perform monitoring while considering geographical relevance. For example, if the message sources are concentrated in a particular region, the monitoring unit can perform monitoring based on risk information for that region. This allows for prioritizing the monitoring of highly relevant information by considering the geographical distribution of messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of messages into a generating AI and have the generating AI perform the monitoring.

[0050] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature related to the message during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature related to the content of the message. For example, the monitoring unit can also extract important information based on literature related to the content of the message and perform monitoring. For example, the monitoring unit can adjust the monitoring algorithm by referring to literature related to the content of the message. In this way, the accuracy of monitoring can be improved by referring to relevant literature related to the message. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data for the message into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0051] The delivery unit can improve the accuracy of its delivery by considering the interrelationships between messages during delivery. For example, the delivery unit can analyze the interrelationships between messages and provide related information in a single batch. For example, the delivery unit can also prioritize the provision of important information by considering the interrelationships between messages. For example, the delivery unit can apply algorithms to improve the accuracy of its delivery based on the interrelationships between messages. This allows for improved delivery accuracy by considering the interrelationships between messages. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input message interrelationship data into a generating AI and have the generating AI perform the improvement of delivery accuracy.

[0052] The information provider can provide information while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, the information provider can provide strict information. For example, if the message sender is a trustworthy user, the information provider can provide normal information. The information provider can also apply algorithms to improve the accuracy of the information provision based on the attribute information of the message sender. This improves the accuracy of the information provision by considering the attribute information of the message sender. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the attribute information data of the message sender into a generating AI and have the generating AI perform the task of improving the accuracy of the information provision.

[0053] The information delivery unit can provide information while considering the geographical distribution of messages. For example, if the message sources are concentrated in a particular region, the information delivery unit can prioritize providing information related to that region. For example, if the message sources are widely distributed, the information delivery unit can also provide information while considering geographical relevance. For example, if the message sources are concentrated in a particular region, the information delivery unit can provide information based on risk information for that region. This allows for the priority provision of highly relevant information by considering the geographical distribution of messages. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input geographical distribution data of messages into a generating AI and have the generating AI perform the delivery.

[0054] The delivery unit can improve the accuracy of its delivery by referring to relevant literature for the message at the time of delivery. For example, the delivery unit can improve the accuracy of its delivery by referring to literature related to the content of the message. For example, the delivery unit can also extract and deliver important information based on literature related to the content of the message. For example, the delivery unit can adjust its delivery algorithm by referring to literature related to the content of the message. This allows for improved delivery accuracy by referring to relevant literature for the message. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input relevant literature data for the message into a generating AI and have the generating AI perform improvements to the accuracy of its delivery.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can suggest the most suitable input method by referring to the user's past behavior history when receiving user input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also analyze the user's past input and automatically complete similar inputs. For example, it can predict and suggest the next input based on the user's past input. Furthermore, the reception desk can predict and suggest the input method the user will use during specific time periods based on their past input history. This allows the system to provide the most suitable input method by leveraging the user's past behavior history.

[0057] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between messages. For example, it can analyze the interrelationships between messages and monitor related messages together. It can also prioritize monitoring important messages by considering the interrelationships between messages. Furthermore, it can apply algorithms to improve monitoring accuracy based on the interrelationships between messages. In this way, the accuracy of monitoring can be improved by considering the interrelationships between messages.

[0058] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize receiving information related to that region. Similarly, if a user is traveling, it can prioritize receiving information related to their travel destination. Furthermore, if a user is at home, it can prioritize receiving information about their home area. This allows the reception desk to prioritize highly relevant information by considering the user's geographical location.

[0059] The analysis unit can adjust the level of detail in its analysis based on the importance of the message. For example, for important messages, it can analyze them in detail to provide users with crucial information. For regular messages, it can apply standard analysis methods to facilitate smooth communication. Furthermore, for urgent messages, it can analyze them quickly and immediately proceed to the next step. In this way, by adjusting the level of detail in the analysis based on the importance of the message, important information can be appropriately analyzed.

[0060] The information provision system can provide information while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, strict information provision can be performed. Conversely, if the message sender is a trustworthy user, standard information provision can be performed. Furthermore, algorithms can be applied to improve the accuracy of information provision based on the attribute information of the message sender. This allows for improved accuracy of information provision by considering the attribute information of the message sender.

[0061] The delivery unit can improve the accuracy of its delivery by referring to relevant literature related to the message. For example, it can improve the accuracy of its delivery by referring to literature related to the content of the message. It can also extract and deliver important information based on literature related to the content of the message. Furthermore, it can adjust the delivery algorithm by referring to literature related to the content of the message. In this way, the accuracy of delivery can be improved by referring to relevant literature related to the message.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception unit receives input from the user. User input can include text messages, images, audio, etc. For example, the reception unit can receive images and text messages sent by the user and send them to the analysis unit. Step 2: The analysis unit analyzes the message from the other party based on the information received by the reception unit. For example, it analyzes the security of links included in the message and analyzes the content of the message to determine if it poses any risks. Step 3: The monitoring unit monitors the security of messages analyzed by the analysis unit. For example, it constantly monitors the other party's messages and notifies the user if there is any risk. It also monitors the content of the other party's messages and issues a warning if there is any risk. Step 4: The provisioning unit provides information to the user based on the information monitored by the monitoring unit. For example, it may provide users with security advice, check whether the content entered by the user is emotionally charged, and prompt them to confirm submission if necessary.

[0064] (Example of form 2) The safety communication support system according to an embodiment of the present invention is a system for reducing the risk of minors and elderly people with low internet literacy becoming involved in crime when using social networking services (SNS) or job recruitment sites. When a user starts communicating on an SNS or job recruitment site, the system has an AI agent that performs initial communication such as greetings and safety checks to confirm the safety of the other party and then hands over the subsequent communication to the user. For example, if a user types "Hello, I'm Yamada!", the AI ​​agent can return a message such as "Mr. / Ms. Yamada, there is a high possibility that the person you are communicating with is a malicious user." Next, the AI ​​agent constantly monitors whether the other party's message is dangerous. For example, if the other party sends a message such as "Let's talk on this site! http: / / ○○", the AI ​​agent can warn, "The site you have provided is a paid site, so please do not access it under any circumstances." The system also includes a function to check whether the content entered by the user is emotional and to confirm sending if necessary. For example, if a user is about to send an emotional message, the AI ​​agent will provide advice such as "This message is too emotional, please reconsider before sending." This system allows minors and elderly people with low internet literacy to safely use social media and job recruitment sites. For example, when a user applies for a job on a job recruitment site, the AI ​​agent can provide advice such as, "This job is clearly suspicious. Searching for cats late at night and paying this much is odd. Also, this company has no track record of recruiting in the past, so it may not be safe," thereby reducing the risk of the user becoming involved in a crime. Furthermore, the AI ​​agent can verify the safety of the linked site and the company recruiting the job. For example, if a user enters, "I want to apply for a job at this company," the AI ​​agent can provide information such as, "This company has no track record of recruiting in the past, so it may not be safe." This allows users to use social media and job recruitment sites with peace of mind.This allows the safe communication support system to enable minors and elderly people with low internet literacy to use social media and job recruitment sites safely.

[0065] The security communication support system according to the embodiment comprises a reception unit, an analysis unit, a monitoring unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text messages, images, and audio. The reception unit can, for example, receive text messages. The reception unit can also receive images and audio. For example, the reception unit can receive an image sent by a user and send it to the analysis unit. The analysis unit analyzes the other party's message based on the information received by the reception unit. The analysis unit can, for example, analyze the security of links included in the other party's message. The analysis unit can also analyze the content of the other party's message and determine whether it poses any danger. For example, the analysis unit analyzes whether a link included in the other party's message is malicious. The monitoring unit monitors the security of the messages analyzed by the analysis unit. The monitoring unit can, for example, constantly monitor the other party's message and notify the user if there is any danger. The monitoring unit can also monitor the content of the other party's message and issue a warning if there is any danger. For example, the monitoring unit sends a warning message to the user if a link included in the other party's message is dangerous. The provision unit provides information to the user based on the information monitored by the monitoring unit. The provision unit can, for example, provide the user with safety advice. The provision unit can also check whether the content entered by the user is emotional and, if necessary, confirm the transmission. For example, if the provision unit sees a user attempting to send an emotional message, it will prompt the user to reconfirm before sending. In this way, the safety communication support system according to the embodiment allows users to use social networking services and job recruitment sites safely.

[0066] The reception unit receives input from the user. User input includes, but is not limited to, text messages, images, and audio. The reception unit can, for example, receive text messages. It can also receive images and audio. For example, the reception unit can receive images sent by the user and send them to the analysis unit. Specifically, the reception unit has the function of receiving text messages entered by the user in real time and immediately forwarding them to the analysis unit. In the case of images, the reception unit checks the image format and resolution, converts it to an appropriate format, and then sends it to the analysis unit. For audio input, it can convert it to text using speech recognition technology and send the content to the analysis unit. Furthermore, the reception unit can temporarily store the user's input and resend or correct it as needed. For example, if a user wants to correct a message that was sent by mistake, the reception unit will accept the request and send the corrected message to the analysis unit. The reception unit can also manage the user's input history and refer to past messages, images, and audio data. This allows the user to enter new messages while reviewing past interactions. The reception unit is designed for intuitive operation through its user interface, allowing users to easily send messages, images, and audio. For example, users can easily upload images using drag-and-drop functionality, or record and send voice messages by simply pressing a voice input button. This enables the reception unit to efficiently receive diverse inputs from users and quickly forward them to the analysis unit.

[0067] The analysis unit analyzes the sender's message based on the information received by the reception unit. For example, the analysis unit can analyze the security of links included in the sender's message. The analysis unit can also analyze the content of the sender's message and determine if it poses any danger. For example, the analysis unit can analyze whether a link included in the sender's message is malicious. Specifically, the analysis unit uses AI to analyze the content of text messages and detect the possibility of spam or phishing. The AI ​​uses natural language processing technology to understand the context of the message and identify dangerous keywords and phrases. It also uses image analysis technology to detect inappropriate content or dangerous elements in received images. For example, by extracting text from an image using OCR technology and analyzing its content, dangerous links or messages can be identified. For voice messages, speech recognition technology is used to convert them into text and analyze the content. Based on these analysis results, the analysis unit evaluates the security of the message and issues warnings as necessary. Furthermore, the analysis unit can also predict the danger level of a message by utilizing past data and statistical information. For example, if similar messages have been reported as spam or phishing in the past, that information can be used to evaluate the danger level of the current message. Furthermore, the analysis unit can analyze the user's behavior history and message sending patterns to detect abnormal behavior. This allows the analysis unit to enhance the security of messages received by users, enabling them to communicate with peace of mind.

[0068] The monitoring unit monitors the security of messages analyzed by the analysis unit. For example, the monitoring unit can constantly monitor the sender's messages and notify the user if there is any danger. The monitoring unit can also monitor the content of the sender's messages and issue warnings if there is any danger. For example, if the monitoring unit finds a dangerous link in the sender's message, it will send a warning message to the user. Specifically, the monitoring unit monitors the security of messages in real time based on the analysis results provided by the analysis unit. The monitoring unit uses AI to continuously evaluate the content of messages and immediately notifies the user if any dangerous elements are detected. For example, if a message containing a phishing link or malware is detected, the monitoring unit will issue a warning to the user and urge them not to click the link. The monitoring unit can also monitor the user's message sending history and detect unusual patterns or suspicious behavior. For example, if a large number of messages are sent at an unusual time, the monitoring unit will detect the anomaly and ask the user for confirmation. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy of its monitoring algorithms. For example, if a user reports a dangerous message, the monitoring algorithm is adjusted based on that information to detect similar messages more quickly in the future. This allows the monitoring unit to enhance the security of messages users receive, enabling them to communicate with peace of mind.

[0069] The service provider provides information to users based on data monitored by the monitoring unit. For example, the service provider can provide users with security advice. It can also check whether user input is emotionally charged and, if necessary, confirm its submission. For instance, if a user attempts to send an emotionally charged message, the service provider may prompt them to reconsider before sending. Specifically, the service provider uses AI to analyze user input and detect emotional expressions or aggressive language. The AI ​​uses natural language processing to understand the context of the message and identify emotional tones and aggressive phrases. For example, if a user uses language expressing anger or frustration, the service provider may temporarily hold the message and prompt the user to reconsider. Furthermore, the service provider can advise users on safe communication methods and precautions. For example, it may provide information on how to identify phishing emails and how to set secure passwords. In addition, the service provider can collect user feedback to continuously improve the accuracy and usefulness of the advice it provides. For example, users can evaluate the advice provided, and the advice can be improved based on that evaluation. Furthermore, the service provider can analyze users' behavioral history and message sending patterns to provide individually customized advice. This allows the service provider to support users in communicating safely and enable them to use social media and job recruitment sites with peace of mind.

[0070] The analysis unit can analyze whether the recipient's message poses any risks. For example, the analysis unit can analyze whether a link included in the recipient's message is malicious. For example, the analysis unit can analyze whether a link included in the recipient's message leads to a phishing site. The analysis unit can also analyze whether a link included in the recipient's message contains malware. For example, the analysis unit can analyze whether a link included in the recipient's message contains a virus. By analyzing the risks of the recipient's message, the user's safety can be ensured. 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 a link included in the recipient's message into a generating AI and have the generating AI perform an analysis of the link's safety.

[0071] The monitoring unit can constantly monitor the other party's messages and notify the user if there is any danger. For example, the monitoring unit can monitor the other party's messages in real time and immediately notify the user if there is any danger. The monitoring unit can also periodically scan the other party's messages and warn the user if there is any danger. For example, the monitoring unit can scan the other party's messages daily and notify the user if there is any danger. The monitoring unit can also constantly monitor the other party's messages and display a pop-up notification if there is any danger. For example, the monitoring unit can display a pop-up notification on the user's screen if there is any danger in the other party's messages. This ensures the user's safety by constantly monitoring the other party's messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the other party's messages into a generating AI and have the generating AI perform message monitoring.

[0072] The service provider can check whether the content entered by the user is emotional and, if necessary, confirm its transmission. For example, if the service provider attempts to send an emotional message, it may prompt the user to reconfirm before sending. For example, if the service provider attempts to send a message containing emotions such as anger or sadness, it may prompt the user to reconfirm before sending. The service provider can also analyze the emotions using an emotion analysis algorithm when the user attempts to send an emotional message and prompt the user to reconfirm before sending. For example, if the service provider attempts to send an emotional message, it may analyze the emotions using an emotion analysis algorithm and provide advice such as, "This message is too emotional, please reconfirm before sending." This allows for appropriate communication by checking whether the user's input is emotional. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's input into a generating AI and have the generating AI perform the emotion analysis.

[0073] The service provider can verify the safety of linked sites and job-recruiting companies. For example, the service provider can evaluate the reliability of linked sites to verify their safety. The service provider can also perform security scans on linked sites to verify their safety. For example, the service provider can analyze the URL of a linked site to check whether it is a phishing site or a site containing malware. The service provider can also evaluate the past recruitment record of job-recruiting companies to verify their safety. For example, the service provider can investigate the past recruitment record of job-recruiting companies to verify their safety. In this way, user safety can be ensured by verifying the safety of linked sites and job-recruiting companies. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or without AI. For example, the service provider can input the URL of a linked site into a generating AI and have the generating AI perform an analysis of the safety of the linked site.

[0074] The service provider can provide users with safety advice. For example, the service provider can provide users with security guidelines. The service provider can also send users with warning messages. For example, the service provider can send users with warning messages such as, "This link is dangerous. Do not access it." The service provider can also provide users with detailed reports. For example, the service provider can provide users with detailed reports such as, "This job recruitment company has no prior recruitment history, so it may not be safe." By providing users with safety advice, the service provider can ensure their safety. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of the advice to be provided to the user into a generating AI and have the generating AI generate the advice.

[0075] The reception unit can estimate the user's emotions and adjust the timing of input processing based on the estimated emotions. For example, if the user is stressed, the reception unit can temporarily hold the input and wait until the user calms down. For example, if the user is relaxed, the reception unit can immediately accept the input to facilitate smooth communication. For example, if the user is in a hurry, the reception unit can quickly accept the input and immediately proceed to the next step. This allows for timely input by adjusting the timing of input processing based on 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 processing described above in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete similar input content by referring to content that the user has entered in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal reception method.

[0077] The reception unit can filter input content based on the user's current situation and areas of interest when receiving it. For example, the reception unit can suggest appropriate input content based on the user's current situation (e.g., working, on break, etc.). The reception unit can also prioritize receiving relevant input content based on the user's areas of interest (e.g., hobbies, work, etc.). The reception unit can also filter out unnecessary input content based on the user's current situation and areas of interest. This allows the reception unit to receive appropriate input content by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reception unit can estimate the user's emotions and determine the priority of input content to be received based on the estimated emotions. For example, if the user is feeling anxious, the reception unit can prioritize important messages to provide reassurance. For example, if the user is relaxed, the reception unit can prioritize normal messages to facilitate smooth communication. For example, if the user is in a hurry, the reception unit can prioritize urgent messages to provide a quick response. In this way, by prioritizing input content based on the user's emotions, important content can be received first. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception unit can prioritize receiving highly relevant content by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, if the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, by considering the user's geographical location, highly relevant content can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant content.

[0080] The reception unit can analyze the user's social media activity when receiving input and accept relevant content. For example, the reception unit can prioritize accepting content related to topics that the user frequently mentions on social media. For example, the reception unit can analyze the user's social media activity history and suggest relevant content. For example, the reception unit can prioritize accepting content related to accounts that the user follows on social media. In this way, relevant content can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant content.

[0081] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can analyze the message content in detail and provide reassurance. For example, if the user is relaxed, the analysis unit can apply the normal analysis method to facilitate smooth interaction. For example, if the user is in a hurry, the analysis unit can quickly analyze the message and immediately proceed to the next step. This allows for appropriate analysis by adjusting the message analysis method based on 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, in the case of an important message, the analysis unit can analyze it in detail and provide important information to the user. For example, in the case of a normal message, the analysis unit can apply a normal analysis method to facilitate smooth communication. For example, in the case of an urgent message, the analysis unit can analyze it quickly and proceed to the next step immediately. This allows for the appropriate analysis of important information by adjusting the level of detail of the analysis based on the importance of the message. 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 message importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, in the case of a business message, the analysis unit can apply a business-specific analysis algorithm to provide appropriate information. For example, in the case of a private message, the analysis unit can apply a private-specific analysis algorithm to provide appropriate information. For example, in the case of an urgent message, the analysis unit can apply an urgent-specific analysis algorithm to respond quickly. This allows for appropriate analysis by applying different analysis algorithms depending on the message category. 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 message category data into a generating AI and have the generating AI select an analysis algorithm.

[0084] 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 feeling anxious, the analysis unit can perform a detailed analysis to provide reassurance. For example, if the user is relaxed, the analysis unit can perform a normal analysis to facilitate smooth interaction. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis to immediately move to the next step. This allows for appropriate analysis by adjusting the length of the analysis based on 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-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can determine the priority of analysis based on the message transmission timing during analysis. For example, the analysis unit can prioritize the analysis of messages sent at critical times to provide important information to the user. For example, the analysis unit can also analyze messages sent at normal times with normal priority to facilitate smooth communication. For example, the analysis unit can prioritize the analysis of messages sent in emergencies to enable a rapid response. In this way, by determining the priority of analysis based on the message transmission timing, important information can be appropriately analyzed. 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 message transmission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0086] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize the analysis of messages with high relevance to provide important information to the user. For example, the analysis unit can also analyze messages with normal relevance in the normal order to facilitate smooth communication. For example, the analysis unit can prioritize the analysis of messages with urgent relevance to enable a quick response. In this way, important information can be appropriately analyzed by adjusting the order of analysis based on the relevance of messages. 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 message relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0087] The monitoring unit can estimate the user's emotions and adjust monitoring standards based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can apply strict monitoring standards to provide reassurance. For example, if the user is relaxed, the monitoring unit can apply normal monitoring standards to facilitate smooth interaction. For example, if the user is in a hurry, the monitoring unit can monitor quickly and immediately move to the next step. This ensures appropriate monitoring by adjusting monitoring standards based on 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of messages during monitoring. For example, the monitoring unit can analyze the interrelationships of messages and monitor related messages collectively. For example, the monitoring unit can also prioritize monitoring important messages by considering the interrelationships of messages. For example, the monitoring unit can apply algorithms to improve the accuracy of monitoring based on the interrelationships of messages. This allows for improved monitoring accuracy by considering the interrelationships of messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input message interrelationship data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0089] The monitoring unit can perform monitoring while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, the monitoring unit can perform strict monitoring. For example, if the message sender is a trustworthy user, the monitoring unit can perform normal monitoring. The monitoring unit can also apply algorithms to improve the accuracy of monitoring based on the attribute information of the message sender. This improves the accuracy of monitoring by considering the attribute information of the message sender. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information data of the message sender into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0090] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit can prioritize displaying important monitoring results to provide reassurance. For example, if the user is relaxed, the monitoring unit can display monitoring results in the normal order to facilitate smooth interaction. For example, if the user is in a hurry, the monitoring unit can prioritize displaying urgent monitoring results to enable a quick response. This allows for the priority provision of important information by adjusting the order in which monitoring results are displayed based on 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The monitoring unit can perform monitoring while considering the geographical distribution of messages. For example, if the message sources are concentrated in a particular region, the monitoring unit can prioritize monitoring information related to that region. For example, if the message sources are widely distributed, the monitoring unit can also perform monitoring while considering geographical relevance. For example, if the message sources are concentrated in a particular region, the monitoring unit can perform monitoring based on risk information for that region. This allows for prioritizing the monitoring of highly relevant information by considering the geographical distribution of messages. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of messages into a generating AI and have the generating AI perform the monitoring.

[0092] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature related to the message during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature related to the content of the message. For example, the monitoring unit can also extract important information based on literature related to the content of the message and perform monitoring. For example, the monitoring unit can adjust the monitoring algorithm by referring to literature related to the content of the message. In this way, the accuracy of monitoring can be improved by referring to relevant literature related to the message. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data for the message into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0093] The service provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize providing important information to reassure them. For example, if the user is relaxed, the service provider can prioritize providing normal information to facilitate smooth communication. For example, if the user is in a hurry, the service provider can prioritize providing urgent information to respond quickly. In this way, by prioritizing the information to be provided based on the user's emotions, important information 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The delivery unit can improve the accuracy of its delivery by considering the interrelationships between messages during delivery. For example, the delivery unit can analyze the interrelationships between messages and provide related information in a single batch. For example, the delivery unit can also prioritize the provision of important information by considering the interrelationships between messages. For example, the delivery unit can apply algorithms to improve the accuracy of its delivery based on the interrelationships between messages. This allows for improved delivery accuracy by considering the interrelationships between messages. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input message interrelationship data into a generating AI and have the generating AI perform the improvement of delivery accuracy.

[0095] The information provider can provide information while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, the information provider can provide strict information. For example, if the message sender is a trustworthy user, the information provider can provide normal information. The information provider can also apply algorithms to improve the accuracy of the information provision based on the attribute information of the message sender. This improves the accuracy of the information provision by considering the attribute information of the message sender. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the attribute information data of the message sender into a generating AI and have the generating AI perform the task of improving the accuracy of the information provision.

[0096] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting how the information is displayed based on the user's emotions, important information can be displayed appropriately. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The information delivery unit can provide information while considering the geographical distribution of messages. For example, if the message sources are concentrated in a particular region, the information delivery unit can prioritize providing information related to that region. For example, if the message sources are widely distributed, the information delivery unit can also provide information while considering geographical relevance. For example, if the message sources are concentrated in a particular region, the information delivery unit can provide information based on risk information for that region. This allows for the priority provision of highly relevant information by considering the geographical distribution of messages. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input geographical distribution data of messages into a generating AI and have the generating AI perform the delivery.

[0098] The delivery unit can improve the accuracy of its delivery by referring to relevant literature for the message at the time of delivery. For example, the delivery unit can improve the accuracy of its delivery by referring to literature related to the content of the message. For example, the delivery unit can also extract and deliver important information based on literature related to the content of the message. For example, the delivery unit can adjust its delivery algorithm by referring to literature related to the content of the message. This allows for improved delivery accuracy by referring to relevant literature for the message. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input relevant literature data for the message into a generating AI and have the generating AI perform improvements to the accuracy of its delivery.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can suggest the most suitable input method by referring to the user's past behavior history when receiving user input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also analyze the user's past input and automatically complete similar inputs. For example, it can predict and suggest the next input based on the user's past input. Furthermore, the reception desk can predict and suggest the input method the user will use during specific time periods based on their past input history. This allows the system to provide the most suitable input method by leveraging the user's past behavior history.

[0101] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated emotions. For example, if the user is feeling anxious, the message content can be analyzed in detail to provide reassurance. If the user is relaxed, the normal analysis method can be applied to facilitate smooth communication. Furthermore, if the user is in a hurry, the message can be analyzed quickly, allowing for immediate progress to the next step. In this way, appropriate analysis can be performed by adjusting the message analysis method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0102] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between messages. For example, it can analyze the interrelationships between messages and monitor related messages together. It can also prioritize monitoring important messages by considering the interrelationships between messages. Furthermore, it can apply algorithms to improve monitoring accuracy based on the interrelationships between messages. In this way, the accuracy of monitoring can be improved by considering the interrelationships between messages.

[0103] The service provider can estimate the user's emotions and prioritize the information provided based on those emotions. For example, if the user is feeling anxious, important information can be prioritized to provide reassurance. If the user is relaxed, normal information can be prioritized to facilitate smooth communication. Furthermore, if the user is in a hurry, urgent information can be prioritized to enable a quick response. In this way, by prioritizing the information provided based on the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0104] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize receiving information related to that region. Similarly, if a user is traveling, it can prioritize receiving information related to their travel destination. Furthermore, if a user is at home, it can prioritize receiving information about their home area. This allows the reception desk to prioritize highly relevant information by considering the user's geographical location.

[0105] The analysis unit can adjust the level of detail in its analysis based on the importance of the message. For example, for important messages, it can analyze them in detail to provide users with crucial information. For regular messages, it can apply standard analysis methods to facilitate smooth communication. Furthermore, for urgent messages, it can analyze them quickly and immediately proceed to the next step. In this way, by adjusting the level of detail in the analysis based on the importance of the message, important information can be appropriately analyzed.

[0106] The monitoring unit can estimate the user's emotions and adjust monitoring standards based on those estimates. For example, if the user is feeling anxious, strict monitoring standards can be applied to provide reassurance. Conversely, if the user is relaxed, normal monitoring standards can be applied to facilitate smoother interaction. Furthermore, if the user is in a hurry, monitoring can be performed quickly to immediately move to the next step. In this way, appropriate monitoring can be performed by adjusting monitoring standards based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0107] The information provision system can provide information while considering the attribute information of the message sender. For example, if the message sender is a user who has caused problems in the past, strict information provision can be performed. Conversely, if the message sender is a trustworthy user, standard information provision can be performed. Furthermore, algorithms can be applied to improve the accuracy of information provision based on the attribute information of the message sender. This allows for improved accuracy of information provision by considering the attribute information of the message sender.

[0108] The reception system can estimate the user's emotions and prioritize the input content to be received based on those emotions. For example, if the user is feeling anxious, important messages can be prioritized to provide reassurance. If the user is relaxed, normal messages can be prioritized to facilitate smooth communication. Furthermore, if the user is in a hurry, urgent messages can be prioritized for a quick response. In this way, by prioritizing input content based on the user's emotions, important information can be received first. Emotion estimation is achieved using an emotion engine or generative AI.

[0109] The delivery unit can improve the accuracy of its delivery by referring to relevant literature related to the message. For example, it can improve the accuracy of its delivery by referring to literature related to the content of the message. It can also extract and deliver important information based on literature related to the content of the message. Furthermore, it can adjust the delivery algorithm by referring to literature related to the content of the message. In this way, the accuracy of delivery can be improved by referring to relevant literature related to the message.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception unit receives input from the user. User input can include text messages, images, audio, etc. For example, the reception unit can receive images and text messages sent by the user and send them to the analysis unit. Step 2: The analysis unit analyzes the message from the other party based on the information received by the reception unit. For example, it analyzes the security of links included in the message and analyzes the content of the message to determine if it poses any risks. Step 3: The monitoring unit monitors the security of messages analyzed by the analysis unit. For example, it constantly monitors the other party's messages and notifies the user if there is any risk. It also monitors the content of the other party's messages and issues a warning if there is any risk. Step 4: The provisioning unit provides information to the user based on the information monitored by the monitoring unit. For example, it may provide users with security advice, check whether the content entered by the user is emotionally charged, and prompt them to confirm submission if necessary.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, monitoring unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the other party's message based on the received information. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the security of the analyzed message. The provision unit is implemented by the control unit 46A of the smart device 14 and provides information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, monitoring unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the other party's message based on the received information. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors the security of the analyzed message. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, monitoring unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the other party's message based on the received information. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the security of the analyzed message. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, monitoring unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the other party's message based on the received information. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors the security of the analyzed message. The provision unit is implemented by the control unit 46A of the robot 414 and provides information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception area that receives input from users, An analysis unit analyzes the other party's message based on the information received by the reception unit, A monitoring unit monitors the security of the messages analyzed by the aforementioned analysis unit, The system includes a providing unit that provides information to the user based on the information monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze whether the other party's message poses any danger. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, The system constantly monitors the other party's messages and notifies the user if there is any danger. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The system checks whether the content entered by the user is emotionally charged and confirms submission as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Check the safety of the linked website and the company recruiting the part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with safety advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input processing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system filters it based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the input content to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting highly relevant content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the message analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the messages were sent. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, When monitoring, consider the interrelationships between messages to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, the system takes into account the attribute information of the message sender. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, It estimates the user's sentiment and adjusts the order in which monitoring results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, During monitoring, the geographical distribution of messages should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, During monitoring, refer to relevant literature related to the message to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we improve the accuracy of the service by considering the interrelationships between messages. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the information, the sender's attributes will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When delivering messages, the geographical distribution of the messages will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the information, we refer to related literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 reception area that receives input from users, An analysis unit analyzes the other party's message based on the information received by the reception unit, A monitoring unit monitors the security of the messages analyzed by the aforementioned analysis unit, The system includes a providing unit that provides information to the user based on the information monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze whether the other party's message poses any danger. The system according to feature 1.

3. The aforementioned monitoring unit, The system constantly monitors the other party's messages and notifies the user if there is any danger. The system according to feature 1.

4. The aforementioned supply unit is, The system checks whether the content entered by the user is emotionally charged and confirms submission as needed. The system according to feature 1.

5. The aforementioned supply unit is, Check the safety of the linked website and the company recruiting the part-time job. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with safety advice. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input processing based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, the system filters it based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the input content to be received based on those estimated emotions. The system according to feature 1.

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