System

Abstract

An object of a system according to an embodiment is to prevent an incident caused by mail transmission.SOLUTION: A system includes a collection part, an analysis part, a determination part, a block part, and a warning part. The collection unit collects contents of the mail. The analysis unit analyzes the contents collected by the collection unit. The determination unit determines whether or not the content analyzed by the analysis unit corresponds to an incident. The block unit blocks transmission when the determination unit determines that the data is equivalent to an incident. The warning unit displays a warning message when the transmission is blocked by the blocking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not have a system in place to prevent incidents caused by email transmission, so there is room for improvement.

[0005] The system according to the embodiment aims to prevent incidents caused by sending emails. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, a blocking unit, and a warning unit. The collection unit collects email content. The analysis unit analyzes the content collected by the collection unit. The determination unit determines whether the content analyzed by the analysis unit is equivalent to an incident. The blocking unit blocks transmission if the determination unit determines that the email is equivalent to an incident. The warning unit displays a warning message if transmission is blocked by the blocking unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent incidents caused by sending emails before they occur. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An email transmission incident prevention system according to an embodiment of the present invention is a system for preventing various incidents caused by email transmission. When a user composes an email and presses the send button, the system inputs information such as the email body, recipients, attachments, and storage files into a generation AI, which then analyzes the information and determines whether the email qualifies as an incident. For example, the generation AI instantly determines whether the email contains confidential information or if it may have been sent by mistake. If the AI ​​determines that the email qualifies as an incident, it blocks the transmission and displays a warning message to the user. For example, a message such as "This email cannot be sent because it contains confidential information" may be displayed. Furthermore, incident prevention events are aggregated and pattern and trend analysis is performed to develop new incident countermeasures. This prevents incidents and improves the efficiency of email transmission operations. It can also be customized to suit individual companies. This allows the email transmission incident prevention system to prevent incidents, improve the efficiency of email transmission operations, provide customization tailored to individual companies, aggregate incident prevention events, and develop new incident countermeasures based on pattern and trend analysis.

[0029] An email transmission incident prevention system according to an embodiment includes a collection unit, an analysis unit, a determination unit, a blocking unit, and a warning unit. The collection unit collects email content. The collection unit collects information such as the email body, recipients, attachments, and storage files. The collection unit can also use AI to adjust the type of data collected and the frequency of collection. The analysis unit analyzes the content collected by the collection unit. The analysis unit determines, for example, whether the email contains confidential information or whether it may have been sent by mistake. The analysis unit can also use AI to adjust the algorithm used and the accuracy of the analysis. The determination unit determines whether the email is an incident based on the content analyzed by the analysis unit. The determination unit makes a determination based, for example, on an incident definition and a determination threshold. The determination unit can also adjust the determination criteria using AI. The blocking unit blocks transmission when the determination unit determines that the email is an incident. The blocking unit can set, for example, blocking conditions and a method for unblocking the email. The blocking unit can also adjust the blocking method using AI. The warning unit displays a warning message when transmission is blocked by the blocking unit. The warning unit displays a warning message such as, for example, "This email cannot be sent because it contains confidential information." The warning unit can also use AI to adjust the content and display method of the warning message. As a result, the email transmission incident prevention system according to the embodiment can prevent incidents caused by email transmission and improve the efficiency of email transmission work.

[0030] Furthermore, the email transmission incident prevention system includes an aggregation unit that aggregates incident prevention events. The aggregation unit aggregates incident prevention events. For example, the aggregation unit aggregates events based on the specific content and criteria of the incident prevention events. The aggregation unit can also use AI to adjust the event classification method and aggregation accuracy. This enables pattern and trend analysis by aggregating incident prevention events.

[0031] Furthermore, the email transmission incident prevention system includes an analysis unit that performs pattern and trend analysis. The analysis unit performs pattern and trend analysis based on, for example, the data and analysis method used. The analysis unit can also use AI to adjust the analysis method and standards. This allows for pattern and trend analysis to be performed and new incident countermeasures to be updated.

[0032] Furthermore, the email sending incident prevention system is equipped with a customization unit that performs customization according to each individual company. The customization unit performs customization according to each individual company. For example, the customization unit performs customization based on the scope of customization and response procedures. The customization unit can also use AI to adjust the customization method and standards. This makes it possible to perform customization according to each individual company.

[0033] The collection unit can collect information on the email body, destinations, attached files, and storage files. The collection unit collects information based on, for example, the specific content of the email body and the collection method. The collection unit can adjust the number of characters to collect and the timing of collection. The collection unit also collects information based on the specific content of the destination and the collection method. The collection unit can adjust the type of addresses to collect and the timing of collection. The collection unit also collects information based on the specific content of the attached files and the collection method. The collection unit can adjust the type of files to collect and the timing of collection. The collection unit collects information based on the specific content of the storage files and the collection method. The collection unit can adjust the type of files to collect and the timing of collection. This improves the accuracy of incident detection by collecting all email information.

[0034] The analysis unit can analyze the collected information and determine whether it contains confidential information or whether there is a possibility of erroneous transmission. The analysis unit analyzes the information, for example, based on a specific definition and criteria of confidential information. The analysis unit can adjust the type of confidential information and the method of determination. The analysis unit also analyzes the information based on specific criteria and methods for determining the possibility of erroneous transmission. The analysis unit can adjust the definition and method of erroneous transmission. This allows for highly accurate determination of confidential information and the possibility of erroneous transmission.

[0035] The warning unit can display a warning message such as "This email cannot be sent because it contains confidential information." The warning unit displays the message based on, for example, the specific content and display method of the warning message. The warning unit can adjust the wording and display timing of the message. This makes it possible to prevent incidents by displaying an appropriate warning message to the user.

[0036] The collection unit can analyze the user's past email sending history and select an appropriate collection method. For example, the collection unit analyzes the patterns of emails that the user frequently sent in the past and prioritizes collecting emails with similar patterns. The collection unit can also analyze the time periods in which the user sent emails in the past and collect emails according to those time periods. Furthermore, the collection unit can analyze the content of emails that the user sent in the past and prioritize collecting emails containing similar content. In this way, the optimal collection method can be selected by analyzing the past email sending history.

[0037] When collecting emails, the collection unit can filter them based on the user's current project or area of ​​interest. For example, the collection unit preferentially collects emails related to projects currently underway by the user. The collection unit can also filter and collect emails related to areas in which the user is interested. Furthermore, the collection unit can preferentially collect emails related to projects in which the user has shown interest in the past. This allows highly relevant emails to be preferentially collected by filtering emails based on the user's current project or area of ​​interest.

[0038] When collecting emails, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can prioritize collecting image data. This allows for efficient collection of emails by selecting the optimal collection means depending on the user's input method.

[0039] When collecting emails, the collection unit can prioritize collecting highly relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting emails related to that area. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting emails related to the business trip destination. Furthermore, when the user is at home, the collection unit can prioritize collecting emails related to the home. In this way, by taking into account the user's geographical location information, highly relevant emails can be prioritized and collected.

[0040] When collecting emails, the collection unit can analyze the user's social media activity and collect related emails. For example, the collection unit prioritizes collecting emails related to content mentioned by the user on social media. The collection unit can also analyze the time period during which the user is active on social media and collect emails according to that time period. Furthermore, the collection unit can collect related emails by referring to the activities of the user's friends on social media. This allows related emails to be collected efficiently by analyzing the user's social media activity.

[0041] When collecting emails, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, the user's past feedback. The collection unit can also give priority to using collection methods that the user has previously preferred. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. This makes it possible to provide the optimal collection method by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. The analysis unit can also perform a simplified analysis on emails with a low level of importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages depending on the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the email.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the email. For example, the analysis unit applies a strict analysis algorithm to emails containing confidential information. The analysis unit can also apply a simple analysis algorithm to emails containing general information. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the email. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of the email.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also refer to the user's past analysis results to detect similar patterns. Furthermore, the analysis unit can analyze the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the analysis priority based on the time of email transmission. For example, the analysis unit prioritizes analysis of emails that are due to be sent soon. The analysis unit can also postpone analysis of emails that are due to be sent further in the future. Furthermore, the analysis unit can gradually adjust the analysis priority depending on the time of transmission. This allows for efficient analysis by determining the analysis priority based on the time of email transmission.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. The analysis unit can also postpone analysis of less relevant emails. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of emails. This allows for efficient analysis by adjusting the order of analysis based on the relevance of emails.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0048] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships between emails when making a judgment. The judgment unit makes a judgment by taking into account, for example, the relationship between the destination and sender of the email. The judgment unit can also make a judgment by taking into account the relevance of the email content and attached files. Furthermore, the judgment unit can refer to the email sending history and make a judgment based on the interrelationships. In this way, by taking into account the interrelationships between emails, the accuracy of the judgment is improved.

[0049] When making a judgment, the judgment unit can take into account the attribute information of the sender of the email. The judgment unit can make a judgment by taking into account, for example, the job title and authority of the sender. The judgment unit can also make a judgment by taking into account the sender's past sending history. Furthermore, the judgment unit can make a judgment by taking into account the department or team to which the sender belongs. This allows for more appropriate judgment by taking into account the attribute information of the sender of the email.

[0050] The determination unit can weight the determination based on the frequency of email transmission when making a determination. For example, the determination unit applies strict criteria to emails that are sent frequently. The determination unit can also apply flexible criteria to emails that are sent infrequently. Furthermore, the determination unit can adjust the weight of the determination in stages according to the sending frequency. This allows for efficient determination by weighting the determination based on the frequency of email transmission.

[0051] The determination unit can make a determination taking into account the geographic distribution of emails. For example, if the destinations are concentrated in a specific region, the determination unit applies determination criteria related to that region. Also, if the sender is in a specific region, the determination unit can make a determination taking into account the security policy of that region. Furthermore, the determination unit can select the optimal determination criteria based on the geographic distribution of the email destinations and senders. This allows for more appropriate determination by taking into account the geographic distribution of emails.

[0052] The determination unit can improve the accuracy of the determination by referring to literature related to the email during the determination. For example, the determination unit can improve the accuracy of the determination by referring to literature related to the content of the email. The determination unit can also improve the accuracy of the determination by referring to literature related to attachments to the email. Furthermore, the determination unit can improve the accuracy of the determination by referring to literature related to the destination of the email. In this way, by referring to literature related to the email, the accuracy of the determination is improved.

[0053] The judgment unit can make judgments taking into account the market value of the email. For example, if the content of the email has a high market value, the judgment unit applies strict judgment criteria. On the other hand, if the content of the email has a low market value, the judgment unit can apply flexible judgment criteria. Furthermore, the judgment unit can gradually adjust the weighting of the judgment depending on the market value of the email. This allows for more appropriate judgments by taking the market value of the email into consideration.

[0054] When blocking, the blocking unit can analyze the user's past blocking history and select the optimal blocking method. For example, the blocking unit analyzes the patterns of emails that the user has previously blocked and prioritizes blocking emails with similar patterns. The blocking unit can also refer to the user's past blocking history and select the optimal blocking method. Furthermore, the blocking unit can improve the accuracy of blocking based on the user's past blocking history. This allows the optimal blocking method to be selected by analyzing the user's past blocking history.

[0055] The blocking unit can customize the blocking method based on the user's current situation when blocking. For example, the blocking unit can delay the blocking notification when the user is in a meeting. Also, the blocking unit can shorten the blocking notification when the user is on a business trip. Furthermore, the blocking unit can provide a detailed blocking notification when the user is at home. This allows for more appropriate blocking by customizing the blocking method based on the user's current situation.

[0056] The blocking unit can improve the blocking method by reflecting user feedback when blocking. For example, the blocking unit can adjust the blocking method based on feedback provided by the user in the past. The blocking unit can also analyze the user's feedback and propose an optimal blocking method. Furthermore, the blocking unit can improve the accuracy of the blocking by reflecting the user's feedback. In this way, the accuracy of the blocking is improved by reflecting the user's feedback.

[0057] When blocking, the blocking unit can select the optimal blocking method by taking into account the user's geographical location information. For example, if the user is in a specific area, the blocking unit applies a blocking method related to that area. Also, if the user is on a business trip, the blocking unit can apply a blocking method related to the user's business trip destination. Furthermore, if the user is at home, the blocking unit can apply a blocking method related to the user's home. In this way, the optimal blocking method can be selected by taking into account the user's geographical location information.

[0058] When blocking, the blocking unit can analyze the user's social media activity and suggest a blocking method. For example, the blocking unit can suggest a blocking method related to the content the user mentioned on social media. The blocking unit can also analyze the time period during which the user is active on social media and suggest a blocking method that suits that time period. Furthermore, the blocking unit can suggest the optimal blocking method by taking into account the activity of the user's friends on social media. In this way, the optimal blocking method can be suggested by analyzing the user's social media activity.

[0059] When blocking, the blocking unit can customize the blocking method by reflecting the user's past feedback. The blocking unit adjusts the blocking method based on, for example, feedback provided by the user in the past. The blocking unit can also analyze the user's feedback and propose an optimal blocking method. Furthermore, the blocking unit can improve the accuracy of the blocking by reflecting the user's past feedback. In this way, the optimal blocking method can be provided by reflecting the user's past feedback.

[0060] When displaying a warning message, the warning unit can provide an optimal message by referring to the user's past warning history. The warning unit can provide an optimal message, for example, based on warning messages the user has received in the past. The warning unit can also refer to the user's past warning history and detect similar patterns. Furthermore, the warning unit can analyze the user's past warning history and suggest an optimal message. In this way, the optimal warning message can be provided by referring to the user's past warning history.

[0061] The warning unit can customize the message content according to the user's current task when displaying the warning message. For example, when the user is in a meeting, the warning unit can display a simple warning message. When the user is on a business trip, the warning unit can display a warning message related to the user's business trip destination. When the user is at home, the warning unit can display a detailed warning message. This allows for more appropriate warnings by customizing the message content according to the user's current task.

[0062] The warning unit can improve the content of a warning message by reflecting user feedback when displaying the warning message. For example, the warning unit can adjust the warning message based on feedback previously provided by the user. The warning unit can also analyze the user's feedback and suggest an optimal warning message. Furthermore, the warning unit can improve the accuracy of the warning message by reflecting the user's feedback. In this way, the accuracy of the warning message is improved by reflecting the user's feedback.

[0063] The warning unit can provide an optimal message by taking into consideration the user's device information when displaying a warning message. For example, if the user is using a smartphone, the warning unit can provide a warning message that matches the screen size. Furthermore, if the user is using a tablet, the warning unit can provide a warning message that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the warning unit can provide a concise and highly visible warning message. This allows the optimal warning message to be provided by taking into consideration the user's device information.

[0064] When displaying a warning message, the warning unit can make the message content multilingual according to the user's language setting. The warning unit can automatically set the language of the warning message based on, for example, the language setting of the user's device. The warning unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the warning unit can provide the warning message in that language. This makes it possible to provide more appropriate warnings by making the message content multilingual according to the user's language setting.

[0065] The warning unit can customize the warning message displayed based on the user's occupation and lifestyle. For example, if the user is a medical professional, the warning unit can provide a medical-related warning message. If the user is a businessman, the warning unit can provide a business-related warning message. If the user is a student, the warning unit can provide an academic-related warning message. This allows for more appropriate warnings by customizing the message based on the user's occupation and lifestyle.

[0066] The aggregator can optimize the aggregation algorithm by referring to past aggregated data during aggregation. For example, the aggregator selects an optimal aggregation algorithm based on past aggregated data. The aggregator can also analyze past aggregated data to improve the accuracy of the aggregation algorithm. Furthermore, the aggregator can detect similar patterns by referring to past aggregated data. This improves the accuracy of the aggregation algorithm by referring to past aggregated data.

[0067] The aggregating unit can update the aggregated data by reflecting user feedback during aggregation. The aggregating unit updates the aggregated data, for example, based on feedback previously provided by the user. The aggregating unit can also analyze the user feedback and propose optimal aggregated data. Furthermore, the aggregating unit can improve the accuracy of the aggregated data by reflecting user feedback. As a result, the accuracy of the aggregated data is improved by reflecting user feedback.

[0068] During aggregation, the aggregation unit can weight the aggregated data based on the time of email transmission. For example, the aggregation unit prioritizes aggregation of email data that was sent recently. The aggregation unit can also postpone aggregation of email data that was sent further back. Furthermore, the aggregation unit can gradually adjust the weighting of the aggregated data depending on the time of transmission. This allows for efficient data aggregation by weighting the aggregated data based on the time of email transmission.

[0069] During aggregation, the aggregator can integrate information from different data sources to enrich the aggregated data. For example, the aggregator can integrate and aggregate information on email content and attachments. The aggregator can also integrate and aggregate information on email destinations and senders. Furthermore, the aggregator can integrate and aggregate information on email sending history and related literature. In this way, the accuracy of the aggregated data is improved by integrating information from different data sources.

[0070] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can detect similar patterns by referring to past analysis data. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data.

[0071] During analysis, the analysis unit can update the analysis data by reflecting user feedback. For example, the analysis unit updates the analysis data based on feedback previously provided by the user. The analysis unit can also analyze the user's feedback and propose optimal analysis data. Furthermore, the analysis unit can improve the accuracy of the analysis data by reflecting user feedback. As a result, the accuracy of the analysis data is improved by reflecting user feedback.

[0072] During analysis, the analysis unit can weight the analysis data based on the time of email transmission. For example, the analysis unit prioritizes analysis of data from emails sent sooner. The analysis unit can also postpone analysis of data from emails sent further back. Furthermore, the analysis unit can gradually adjust the weighting of the analysis data depending on the time of transmission. This allows for efficient data analysis by weighting the analysis data based on the time of email transmission.

[0073] During analysis, the analysis unit can integrate information from different data sources to enrich the analysis data. For example, the analysis unit integrates and analyzes the content of emails and information on attachments. The analysis unit can also integrate and analyze information on the destination and sender of emails. Furthermore, the analysis unit can integrate and analyze information on email sending history and related literature. In this way, the accuracy of the analysis data is improved by integrating information from different data sources.

[0074] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit selects the optimal customization method based on, for example, the user's past customization history. The customization unit can also detect similar patterns by referring to the user's past customization history. Furthermore, the customization unit can analyze the user's past customization history and suggest the optimal customization method. In this way, the optimal customization method can be selected by referring to the user's past customization history.

[0075] During customization, the customization unit can customize the customization means based on the user's current situation. For example, when the user is in a meeting, the customization unit can provide a simple customization method. Furthermore, when the user is on a business trip, the customization unit can provide a customization method related to the destination of the business trip. Furthermore, when the user is at home, the customization unit can provide a detailed customization method. This enables more appropriate customization by customizing the customization means based on the user's current situation.

[0076] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit adjusts the customization method based on feedback previously provided by the user. The customization unit can also analyze the user feedback and propose an optimal customization method. Furthermore, the customization unit can improve the accuracy of customization by reflecting user feedback. As a result, the accuracy of customization is improved by reflecting user feedback.

[0077] During customization, the customization unit can select the optimal customization method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the customization unit applies a customization method related to that area. Furthermore, when the user is on a business trip, the customization unit can apply a customization method related to the business trip destination. Furthermore, when the user is at home, the customization unit can apply a customization method related to the home. In this way, the optimal customization method can be selected by taking into consideration the user's geographical location information.

[0078] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest customization methods related to the content the user mentioned on social media. The customization unit can also analyze the time periods during which the user is active on social media and suggest customization methods that match those time periods. Furthermore, the customization unit can suggest the optimal customization method by taking into account the activity of the user's friends on social media. In this way, the optimal customization method can be suggested by analyzing the user's social media activity.

[0079] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit adjusts the customization method based on, for example, feedback provided by the user in the past. The customization unit can also analyze the user's feedback and propose an optimal customization method. Furthermore, the customization unit can improve the accuracy of customization by reflecting the user's feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The email sending incident prevention system can further include a learning unit that learns user behavioral patterns. The learning unit can learn, for example, when a user has sent emails in the past and what content the emails have contained, and based on this, improve the accuracy of incident predictions. The learning unit can also analyze the tendency of emails sent by a user during a specific time period and predict the risk of incidents during that time period. Furthermore, the learning unit can learn a user's behavioral patterns when sending emails related to a specific project and predict the risk of incidents related to that project. This can improve the accuracy of incident predictions based on a user's behavioral patterns.

[0082] The email sending incident prevention system may further include a prediction unit that analyzes the user's email sending history and builds a prediction model based on past incident patterns. The prediction unit can, for example, analyze the characteristics of emails that have been sent by mistake in the past and display a warning for emails with similar characteristics. The prediction unit can also learn the patterns of emails that have leaked confidential information in the past and block the sending of emails with similar patterns. Furthermore, the prediction unit can calculate the probability of an incident occurring under specific conditions based on past incident data and take appropriate measures based on that probability. This makes it possible to build a prediction model based on past incident patterns and prevent incidents from occurring.

[0083] The email sending incident prevention system may further include a monitoring unit that monitors users' behavior when sending emails in real time and detects abnormal behavior. For example, if a user attempts to send an email at an unusual time, the monitoring unit may determine this as an abnormality and prompt the user to confirm before sending. Furthermore, if a user attempts to send an email to an unusual destination, the monitoring unit may determine this as an abnormality and display a warning message before sending. Furthermore, if a user attempts to send an email with unusual content, the monitoring unit may determine this as an abnormality and block the sending. This allows users' behavior when sending emails to be monitored in real time, detect abnormal behavior, and prevent incidents from occurring.

[0084] The email sending incident prevention system may further include a prediction unit that analyzes the user's email sending history and builds a prediction model based on past incident patterns. The prediction unit can, for example, analyze the characteristics of emails that have been sent by mistake in the past and display a warning for emails with similar characteristics. The prediction unit can also learn the patterns of emails that have leaked confidential information in the past and block the sending of emails with similar patterns. Furthermore, the prediction unit can calculate the probability of an incident occurring under specific conditions based on past incident data and take appropriate measures based on that probability. This makes it possible to build a prediction model based on past incident patterns and prevent incidents from occurring.

[0085] The email sending incident prevention system may further include a monitoring unit that monitors users' behavior when sending emails in real time and detects abnormal behavior. For example, if a user attempts to send an email at an unusual time, the monitoring unit may determine this as an abnormality and prompt the user to confirm before sending. Furthermore, if a user attempts to send an email to an unusual destination, the monitoring unit may determine this as an abnormality and display a warning message before sending. Furthermore, if a user attempts to send an email with unusual content, the monitoring unit may determine this as an abnormality and block the sending. This allows users' behavior when sending emails to be monitored in real time, detect abnormal behavior, and prevent incidents from occurring.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The collection unit collects email content. The collection unit collects information such as the email body, recipients, attachments, and storage files. The collection unit can also use AI to adjust the type of data collected and the frequency of collection. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit determines whether the content contains confidential information or whether it may have been sent by mistake. The analysis unit can also use AI to adjust the algorithms used and the accuracy of the analysis. Step 3: The determination unit determines whether the content analyzed by the analysis unit is an incident. The determination unit makes the determination based on, for example, the definition of an incident and a determination threshold. The determination unit can also use AI to adjust the determination criteria. Step 4: The blocking unit blocks transmission if the judgment unit determines that the transmission is equivalent to an incident. The blocking unit can, for example, set the conditions for blocking and the method for unblocking the transmission. The blocking unit can also use AI to adjust the blocking method. Step 5: The warning unit displays a warning message if the blocking unit blocks the sending. For example, the warning unit displays a warning message such as "This email cannot be sent because it contains confidential information." The warning unit can also use AI to adjust the content and display method of the warning message.

[0088] (Example 2) An email transmission incident prevention system according to an embodiment of the present invention is a system for preventing various incidents caused by email transmission. When a user composes an email and presses the send button, the system inputs information such as the email body, recipients, attachments, and storage files into a generation AI, which then analyzes the information and determines whether the email qualifies as an incident. For example, the generation AI instantly determines whether the email contains confidential information or if it may have been sent by mistake. If the AI ​​determines that the email qualifies as an incident, it blocks the transmission and displays a warning message to the user. For example, a message such as "This email cannot be sent because it contains confidential information" may be displayed. Furthermore, incident prevention events are aggregated and pattern and trend analysis is performed to develop new incident countermeasures. This prevents incidents and improves the efficiency of email transmission operations. It can also be customized to suit individual companies. This allows the email transmission incident prevention system to prevent incidents, improve the efficiency of email transmission operations, provide customization tailored to individual companies, aggregate incident prevention events, and develop new incident countermeasures based on pattern and trend analysis.

[0089] An email transmission incident prevention system according to an embodiment includes a collection unit, an analysis unit, a determination unit, a blocking unit, and a warning unit. The collection unit collects email content. The collection unit collects information such as the email body, recipients, attachments, and storage files. The collection unit can also use AI to adjust the type of data collected and the frequency of collection. The analysis unit analyzes the content collected by the collection unit. The analysis unit determines, for example, whether the email contains confidential information or whether it may have been sent by mistake. The analysis unit can also use AI to adjust the algorithm used and the accuracy of the analysis. The determination unit determines whether the email is an incident based on the content analyzed by the analysis unit. The determination unit makes a determination based, for example, on an incident definition and a determination threshold. The determination unit can also adjust the determination criteria using AI. The blocking unit blocks transmission when the determination unit determines that the email is an incident. The blocking unit can set, for example, blocking conditions and a method for unblocking the email. The blocking unit can also adjust the blocking method using AI. The warning unit displays a warning message when transmission is blocked by the blocking unit. The warning unit displays a warning message such as, for example, "This email cannot be sent because it contains confidential information." The warning unit can also use AI to adjust the content and display method of the warning message. As a result, the email transmission incident prevention system according to the embodiment can prevent incidents caused by email transmission and improve the efficiency of email transmission work.

[0090] Furthermore, the email transmission incident prevention system includes an aggregation unit that aggregates incident prevention events. The aggregation unit aggregates incident prevention events. For example, the aggregation unit aggregates events based on the specific content and criteria of the incident prevention events. The aggregation unit can also use AI to adjust the event classification method and aggregation accuracy. This enables pattern and trend analysis by aggregating incident prevention events.

[0091] Furthermore, the email transmission incident prevention system includes an analysis unit that performs pattern and trend analysis. The analysis unit performs pattern and trend analysis based on, for example, the data and analysis method used. The analysis unit can also use AI to adjust the analysis method and standards. This allows for pattern and trend analysis to be performed and new incident countermeasures to be updated.

[0092] Furthermore, the email sending incident prevention system is equipped with a customization unit that performs customization according to each individual company. The customization unit performs customization according to each individual company. For example, the customization unit performs customization based on the scope of customization and response procedures. The customization unit can also use AI to adjust the customization method and standards. This makes it possible to perform customization according to each individual company.

[0093] The collection unit can collect information on the email body, destinations, attached files, and storage files. The collection unit collects information based on, for example, the specific content of the email body and the collection method. The collection unit can adjust the number of characters to collect and the timing of collection. The collection unit also collects information based on the specific content of the destination and the collection method. The collection unit can adjust the type of addresses to collect and the timing of collection. The collection unit also collects information based on the specific content of the attached files and the collection method. The collection unit can adjust the type of files to collect and the timing of collection. The collection unit collects information based on the specific content of the storage files and the collection method. The collection unit can adjust the type of files to collect and the timing of collection. This improves the accuracy of incident detection by collecting all email information.

[0094] The analysis unit can analyze the collected information and determine whether it contains confidential information or whether there is a possibility of erroneous transmission. The analysis unit analyzes the information, for example, based on a specific definition and criteria of confidential information. The analysis unit can adjust the type of confidential information and the method of determination. The analysis unit also analyzes the information based on specific criteria and methods for determining the possibility of erroneous transmission. The analysis unit can adjust the definition and method of erroneous transmission. This allows for highly accurate determination of confidential information and the possibility of erroneous transmission.

[0095] The warning unit can display a warning message such as "This email cannot be sent because it contains confidential information." The warning unit displays the message based on, for example, the specific content and display method of the warning message. The warning unit can adjust the wording and display timing of the message. This makes it possible to prevent incidents by displaying an appropriate warning message to the user.

[0096] The collection unit can estimate the user's emotions and adjust the timing of email collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing so that the user can check emails in a relaxed state. Furthermore, if the user is relaxed, the collection unit can collect emails immediately to encourage a quick response. Furthermore, if the user is in a hurry, the collection unit can advance the collection timing so that the user can check emails quickly. This allows emails to be collected at a more appropriate time by adjusting the email collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The collection unit can analyze the user's past email sending history and select an appropriate collection method. For example, the collection unit analyzes the patterns of emails that the user frequently sent in the past and prioritizes collecting emails with similar patterns. The collection unit can also analyze the time periods in which the user sent emails in the past and collect emails according to those time periods. Furthermore, the collection unit can analyze the content of emails that the user sent in the past and prioritize collecting emails containing similar content. In this way, the optimal collection method can be selected by analyzing the past email sending history.

[0098] When collecting emails, the collection unit can filter them based on the user's current project or area of ​​interest. For example, the collection unit preferentially collects emails related to projects currently underway by the user. The collection unit can also filter and collect emails related to areas in which the user is interested. Furthermore, the collection unit can preferentially collect emails related to projects in which the user has shown interest in the past. This allows highly relevant emails to be preferentially collected by filtering emails based on the user's current project or area of ​​interest.

[0099] When collecting emails, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can prioritize collecting image data. This allows for efficient collection of emails by selecting the optimal collection means depending on the user's input method.

[0100] The collection unit can estimate the user's emotions and determine the priority of emails to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones emails of low importance and prioritizes collecting emails of high importance. Furthermore, when the user is relaxed, the collection unit can collect all emails equally. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting emails of high urgency. In this way, by determining the priority of emails to be collected according to the user's emotions, important emails can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] When collecting emails, the collection unit can prioritize collecting highly relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting emails related to that area. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting emails related to the business trip destination. Furthermore, when the user is at home, the collection unit can prioritize collecting emails related to the home. In this way, by taking into account the user's geographical location information, highly relevant emails can be prioritized and collected.

[0102] When collecting emails, the collection unit can analyze the user's social media activity and collect related emails. For example, the collection unit prioritizes collecting emails related to content mentioned by the user on social media. The collection unit can also analyze the time period during which the user is active on social media and collect emails according to that time period. Furthermore, the collection unit can collect related emails by referring to the activities of the user's friends on social media. This allows related emails to be collected efficiently by analyzing the user's social media activity.

[0103] When collecting emails, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, the user's past feedback. The collection unit can also give priority to using collection methods that the user has previously preferred. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. This makes it possible to provide the optimal collection method by reflecting the user's past feedback.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. The analysis unit can also perform a simplified analysis on emails with a low level of importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages depending on the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the email.

[0106] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the email. For example, the analysis unit applies a strict analysis algorithm to emails containing confidential information. The analysis unit can also apply a simple analysis algorithm to emails containing general information. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the email. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of the email.

[0107] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also refer to the user's past analysis results to detect similar patterns. Furthermore, the analysis unit can analyze the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0108] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] During analysis, the analysis unit can determine the analysis priority based on the time of email transmission. For example, the analysis unit prioritizes analysis of emails that are due to be sent soon. The analysis unit can also postpone analysis of emails that are due to be sent further in the future. Furthermore, the analysis unit can gradually adjust the analysis priority depending on the time of transmission. This allows for efficient analysis by determining the analysis priority based on the time of email transmission.

[0110] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. The analysis unit can also postpone analysis of less relevant emails. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of emails. This allows for efficient analysis by adjusting the order of analysis based on the relevance of emails.

[0111] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0112] The determination unit can estimate the user's emotions and adjust the determination criteria based on the estimated user's emotions. For example, if the user is nervous, the determination unit can apply strict determination criteria. Furthermore, if the user is relaxed, the determination unit can apply flexible determination criteria. Furthermore, if the user is in a hurry, the determination unit can apply criteria for making a quick determination. This allows for more appropriate determination by adjusting the determination criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships between emails when making a judgment. The judgment unit makes a judgment by taking into account, for example, the relationship between the destination and sender of the email. The judgment unit can also make a judgment by taking into account the relevance of the email content and attached files. Furthermore, the judgment unit can refer to the email sending history and make a judgment based on the interrelationships. In this way, by taking into account the interrelationships between emails, the accuracy of the judgment is improved.

[0114] When making a judgment, the judgment unit can take into account the attribute information of the sender of the email. The judgment unit can make a judgment by taking into account, for example, the job title and authority of the sender. The judgment unit can also make a judgment by taking into account the sender's past sending history. Furthermore, the judgment unit can make a judgment by taking into account the department or team to which the sender belongs. This allows for more appropriate judgment by taking into account the attribute information of the sender of the email.

[0115] The determination unit can weight the determination based on the frequency of email transmission when making a determination. For example, the determination unit applies strict criteria to emails that are sent frequently. The determination unit can also apply flexible criteria to emails that are sent infrequently. Furthermore, the determination unit can adjust the weight of the determination in stages according to the sending frequency. This allows for efficient determination by weighting the determination based on the frequency of email transmission.

[0116] The determination unit can estimate the user's emotions and adjust the display order of the determination results based on the estimated user emotions. For example, if the user is nervous, the determination unit can display the most important determination results first. Also, if the user is relaxed, the determination unit can display all determination results evenly. Furthermore, if the user is in a hurry, the determination unit can display the most urgent determination results first. This allows for more appropriate information provision by adjusting the display order of the determination results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The determination unit can make a determination taking into account the geographic distribution of emails. For example, if the destinations are concentrated in a specific region, the determination unit applies determination criteria related to that region. Also, if the sender is in a specific region, the determination unit can make a determination taking into account the security policy of that region. Furthermore, the determination unit can select the optimal determination criteria based on the geographic distribution of the email destinations and senders. This allows for more appropriate determination by taking into account the geographic distribution of emails.

[0118] The determination unit can improve the accuracy of the determination by referring to literature related to the email during the determination. For example, the determination unit can improve the accuracy of the determination by referring to literature related to the content of the email. The determination unit can also improve the accuracy of the determination by referring to literature related to attachments to the email. Furthermore, the determination unit can improve the accuracy of the determination by referring to literature related to the destination of the email. In this way, by referring to literature related to the email, the accuracy of the determination is improved.

[0119] The judgment unit can make judgments taking into account the market value of the email. For example, if the content of the email has a high market value, the judgment unit applies strict judgment criteria. On the other hand, if the content of the email has a low market value, the judgment unit can apply flexible judgment criteria. Furthermore, the judgment unit can gradually adjust the weighting of the judgment depending on the market value of the email. This allows for more appropriate judgments by taking the market value of the email into consideration.

[0120] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated user emotions. For example, if the user is nervous, the blocking unit can immediately block and provide a detailed explanation. Also, if the user is relaxed, the blocking unit can apply a flexible blocking method and provide the user with options. Furthermore, if the user is in a hurry, the blocking unit can quickly block and provide a concise explanation. This allows for more appropriate blocking by adjusting the blocking method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] When blocking, the blocking unit can analyze the user's past blocking history and select the optimal blocking method. For example, the blocking unit analyzes the patterns of emails that the user has previously blocked and prioritizes blocking emails with similar patterns. The blocking unit can also refer to the user's past blocking history and select the optimal blocking method. Furthermore, the blocking unit can improve the accuracy of blocking based on the user's past blocking history. This allows the optimal blocking method to be selected by analyzing the user's past blocking history.

[0122] The blocking unit can customize the blocking method based on the user's current situation when blocking. For example, the blocking unit can delay the blocking notification when the user is in a meeting. Also, the blocking unit can shorten the blocking notification when the user is on a business trip. Furthermore, the blocking unit can provide a detailed blocking notification when the user is at home. This allows for more appropriate blocking by customizing the blocking method based on the user's current situation.

[0123] The blocking unit can improve the blocking method by reflecting user feedback when blocking. For example, the blocking unit can adjust the blocking method based on feedback provided by the user in the past. The blocking unit can also analyze the user's feedback and propose an optimal blocking method. Furthermore, the blocking unit can improve the accuracy of the blocking by reflecting the user's feedback. In this way, the accuracy of the blocking is improved by reflecting the user's feedback.

[0124] The blocking unit can estimate the user's emotions and determine the priority of blocks based on the estimated user emotions. For example, if the user is nervous, the blocking unit can prioritize blocks with high importance. Furthermore, if the user is relaxed, the blocking unit can perform all blocks equally. Furthermore, if the user is in a hurry, the blocking unit can prioritize blocks with high urgency. This allows for more appropriate blocking by determining the priority of blocks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0125] When blocking, the blocking unit can select the optimal blocking method by taking into account the user's geographical location information. For example, if the user is in a specific area, the blocking unit applies a blocking method related to that area. Also, if the user is on a business trip, the blocking unit can apply a blocking method related to the user's business trip destination. Furthermore, if the user is at home, the blocking unit can apply a blocking method related to the user's home. In this way, the optimal blocking method can be selected by taking into account the user's geographical location information.

[0126] When blocking, the blocking unit can analyze the user's social media activity and suggest a blocking method. For example, the blocking unit can suggest a blocking method related to the content the user mentioned on social media. The blocking unit can also analyze the time period during which the user is active on social media and suggest a blocking method that suits that time period. Furthermore, the blocking unit can suggest the optimal blocking method by taking into account the activity of the user's friends on social media. In this way, the optimal blocking method can be suggested by analyzing the user's social media activity.

[0127] When blocking, the blocking unit can customize the blocking method by reflecting the user's past feedback. The blocking unit adjusts the blocking method based on, for example, feedback provided by the user in the past. The blocking unit can also analyze the user's feedback and propose an optimal blocking method. Furthermore, the blocking unit can improve the accuracy of the blocking by reflecting the user's past feedback. In this way, the optimal blocking method can be provided by reflecting the user's past feedback.

[0128] The warning unit can estimate the user's emotions and adjust the way the warning message is expressed based on the estimated user's emotions. For example, if the user is nervous, the warning unit can display a calm warning message. If the user is relaxed, the warning unit can display a detailed warning message. If the user is in a hurry, the warning unit can display a concise warning message. This allows for more appropriate warnings by adjusting the way the warning message is expressed depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0129] When displaying a warning message, the warning unit can provide an optimal message by referring to the user's past warning history. The warning unit can provide an optimal message, for example, based on warning messages the user has received in the past. The warning unit can also refer to the user's past warning history and detect similar patterns. Furthermore, the warning unit can analyze the user's past warning history and suggest an optimal message. In this way, the optimal warning message can be provided by referring to the user's past warning history.

[0130] The warning unit can customize the message content according to the user's current task when displaying the warning message. For example, when the user is in a meeting, the warning unit can display a simple warning message. When the user is on a business trip, the warning unit can display a warning message related to the user's business trip destination. When the user is at home, the warning unit can display a detailed warning message. This allows for more appropriate warnings by customizing the message content according to the user's current task.

[0131] The warning unit can improve the content of a warning message by reflecting user feedback when displaying the warning message. For example, the warning unit can adjust the warning message based on feedback previously provided by the user. The warning unit can also analyze the user's feedback and suggest an optimal warning message. Furthermore, the warning unit can improve the accuracy of the warning message by reflecting the user's feedback. In this way, the accuracy of the warning message is improved by reflecting the user's feedback.

[0132] The warning unit can estimate the user's emotions and adjust the display order of warning messages based on the estimated user emotions. For example, if the user is nervous, the warning unit can display a warning message with a high level of importance first. Furthermore, if the user is relaxed, the warning unit can display all warning messages evenly. Furthermore, if the user is in a hurry, the warning unit can display a warning message with a high level of urgency first. This allows for more appropriate information provision by adjusting the display order of warning messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0133] The warning unit can provide an optimal message by taking into consideration the user's device information when displaying a warning message. For example, if the user is using a smartphone, the warning unit can provide a warning message that matches the screen size. Furthermore, if the user is using a tablet, the warning unit can provide a warning message that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the warning unit can provide a concise and highly visible warning message. This allows the optimal warning message to be provided by taking into consideration the user's device information.

[0134] When displaying a warning message, the warning unit can make the message content multilingual according to the user's language setting. The warning unit can automatically set the language of the warning message based on, for example, the language setting of the user's device. The warning unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the warning unit can provide the warning message in that language. This makes it possible to provide more appropriate warnings by making the message content multilingual according to the user's language setting.

[0135] The warning unit can customize the warning message displayed based on the user's occupation and lifestyle. For example, if the user is a medical professional, the warning unit can provide a medical-related warning message. If the user is a businessman, the warning unit can provide a business-related warning message. If the user is a student, the warning unit can provide an academic-related warning message. This allows for more appropriate warnings by customizing the message based on the user's occupation and lifestyle.

[0136] The aggregation unit can estimate the user's emotions and select aggregated data based on the estimated user emotions. For example, if the user is nervous, the aggregation unit prioritizes aggregating data of high importance. Furthermore, if the user is relaxed, the aggregation unit can aggregate all data evenly. Furthermore, if the user is in a hurry, the aggregation unit can prioritize aggregating data of high urgency. This allows for more appropriate data aggregation by selecting aggregated data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0137] The aggregator can optimize the aggregation algorithm by referring to past aggregated data during aggregation. For example, the aggregator selects an optimal aggregation algorithm based on past aggregated data. The aggregator can also analyze past aggregated data to improve the accuracy of the aggregation algorithm. Furthermore, the aggregator can detect similar patterns by referring to past aggregated data. This improves the accuracy of the aggregation algorithm by referring to past aggregated data.

[0138] The aggregating unit can update the aggregated data by reflecting user feedback during aggregation. The aggregating unit updates the aggregated data, for example, based on feedback previously provided by the user. The aggregating unit can also analyze the user feedback and propose optimal aggregated data. Furthermore, the aggregating unit can improve the accuracy of the aggregated data by reflecting user feedback. As a result, the accuracy of the aggregated data is improved by reflecting user feedback.

[0139] The aggregation unit can estimate the user's emotions and adjust the aggregation frequency based on the estimated user emotions. For example, if the user is nervous, the aggregation unit can increase the aggregation frequency to encourage a quick response. Furthermore, if the user is relaxed, the aggregation unit can reduce the aggregation frequency to aggregate all data. Furthermore, if the user is in a hurry, the aggregation unit can increase the aggregation frequency to encourage a quick response. This allows for more appropriate data aggregation by adjusting the aggregation frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0140] During aggregation, the aggregation unit can weight the aggregated data based on the time of email transmission. For example, the aggregation unit prioritizes aggregation of email data that was sent recently. The aggregation unit can also postpone aggregation of email data that was sent further back. Furthermore, the aggregation unit can gradually adjust the weighting of the aggregated data depending on the time of transmission. This allows for efficient data aggregation by weighting the aggregated data based on the time of email transmission.

[0141] During aggregation, the aggregator can integrate information from different data sources to enrich the aggregated data. For example, the aggregator can integrate and aggregate information on email content and attachments. The aggregator can also integrate and aggregate information on email destinations and senders. Furthermore, the aggregator can integrate and aggregate information on email sending history and related literature. In this way, the accuracy of the aggregated data is improved by integrating information from different data sources.

[0142] The analysis unit can estimate the user's emotions and select analysis data based on the estimated user emotions. For example, if the user is nervous, the analysis unit prioritizes analysis of data with high importance. Furthermore, if the user is relaxed, the analysis unit can analyze all data equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of data with high urgency. This allows for more appropriate data analysis by selecting analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0143] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can detect similar patterns by referring to past analysis data. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data.

[0144] During analysis, the analysis unit can update the analysis data by reflecting user feedback. For example, the analysis unit updates the analysis data based on feedback previously provided by the user. The analysis unit can also analyze the user's feedback and propose optimal analysis data. Furthermore, the analysis unit can improve the accuracy of the analysis data by reflecting user feedback. As a result, the accuracy of the analysis data is improved by reflecting user feedback.

[0145] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can increase the frequency of analysis to encourage a quick response. Furthermore, if the user is relaxed, the analysis unit can reduce the frequency of analysis and analyze the entire data. Furthermore, if the user is in a hurry, the analysis unit can increase the frequency of analysis to encourage a quick response. This allows for more appropriate data analysis by adjusting the frequency of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0146] During analysis, the analysis unit can weight the analysis data based on the time of email transmission. For example, the analysis unit prioritizes analysis of data from emails sent sooner. The analysis unit can also postpone analysis of data from emails sent further back. Furthermore, the analysis unit can gradually adjust the weighting of the analysis data depending on the time of transmission. This allows for efficient data analysis by weighting the analysis data based on the time of email transmission.

[0147] During analysis, the analysis unit can integrate information from different data sources to enrich the analysis data. For example, the analysis unit integrates and analyzes the content of emails and information on attachments. The analysis unit can also integrate and analyze information on the destination and sender of emails. Furthermore, the analysis unit can integrate and analyze information on email sending history and related literature. In this way, the accuracy of the analysis data is improved by integrating information from different data sources.

[0148] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. For example, the customization unit can provide a simple customization method when the user is nervous. Furthermore, the customization unit can provide a detailed customization method when the user is relaxed. Furthermore, the customization unit can provide a quick customization method when the user is in a hurry. This allows for more appropriate customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0149] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit selects the optimal customization method based on, for example, the user's past customization history. The customization unit can also detect similar patterns by referring to the user's past customization history. Furthermore, the customization unit can analyze the user's past customization history and suggest the optimal customization method. In this way, the optimal customization method can be selected by referring to the user's past customization history.

[0150] During customization, the customization unit can customize the customization means based on the user's current situation. For example, when the user is in a meeting, the customization unit can provide a simple customization method. Furthermore, when the user is on a business trip, the customization unit can provide a customization method related to the destination of the business trip. Furthermore, when the user is at home, the customization unit can provide a detailed customization method. This enables more appropriate customization by customizing the customization means based on the user's current situation.

[0151] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit adjusts the customization method based on feedback previously provided by the user. The customization unit can also analyze the user feedback and propose an optimal customization method. Furthermore, the customization unit can improve the accuracy of customization by reflecting user feedback. As a result, the accuracy of customization is improved by reflecting user feedback.

[0152] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is nervous, the customization unit prioritizes customization with high importance. Furthermore, if the user is relaxed, the customization unit can perform all customizations equally. Furthermore, if the user is in a hurry, the customization unit can prioritize customization with high urgency. This enables more appropriate customization by determining the priority of customization according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0153] During customization, the customization unit can select the optimal customization method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the customization unit applies a customization method related to that area. Furthermore, when the user is on a business trip, the customization unit can apply a customization method related to the business trip destination. Furthermore, when the user is at home, the customization unit can apply a customization method related to the home. In this way, the optimal customization method can be selected by taking into consideration the user's geographical location information.

[0154] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest customization methods related to the content the user mentioned on social media. The customization unit can also analyze the time periods during which the user is active on social media and suggest customization methods that match those time periods. Furthermore, the customization unit can suggest the optimal customization method by taking into account the activity of the user's friends on social media. In this way, the optimal customization method can be suggested by analyzing the user's social media activity.

[0155] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit adjusts the customization method based on, for example, feedback provided by the user in the past. The customization unit can also analyze the user's feedback and propose an optimal customization method. Furthermore, the customization unit can improve the accuracy of customization by reflecting the user's feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, blocking unit, warning unit, aggregation unit, analysis unit, and customization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information such as email text, destinations, attachments, and storage files using the control unit 46A of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can adjust the type of data to be collected and the frequency of collection using AI. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and analyzes the collected content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the analyzed content corresponds to an incident. The blocking unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and blocks transmission if it is determined to correspond to an incident. The warning unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and displays a warning message when transmission is blocked. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates incident prevention events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs pattern and trend analysis. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs customization according to individual companies. The collection unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of email collection based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, blocking unit, warning unit, aggregation unit, analysis unit, and customization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information such as email text, destinations, attachments, and storage files using the control unit 46A of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can adjust the type of data to be collected and the frequency of collection using AI. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and analyzes the collected content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the analyzed content corresponds to an incident. The blocking unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and blocks transmission when it is determined to be equivalent to an incident. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and displays a warning message when transmission is blocked. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates incident prevention events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs pattern and trend analysis. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs customization according to individual companies. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of email collection based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, blocking unit, warning unit, aggregation unit, analysis unit, and customization unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect information such as email text, destinations, attachments, and storage files using the control unit 46A of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can adjust the type of data to be collected and the frequency of collection using AI. The analysis unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes the collected content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines whether the analyzed content corresponds to an incident. The blocking unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and blocks transmission when it is determined to be equivalent to an incident. The warning unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and displays a warning message when transmission is blocked. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates incident prevention events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs pattern and trend analysis. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs customization according to individual companies. The collection unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of email collection based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, blocking unit, warning unit, aggregation unit, analysis unit, and customization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information such as email text, destinations, attachments, and storage files using the control unit 46A of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can adjust the type of data to be collected and the frequency of collection using AI. The analysis unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and analyzes the collected content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the analyzed content corresponds to an incident. The blocking unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and blocks transmission if it is determined to correspond to an incident. The warning unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and displays a warning message when transmission is blocked. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates incident prevention events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs pattern and trend analysis. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs customization according to individual companies. The collection unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of email collection based on the estimated user's emotions.

[0156] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0157] The email sending incident prevention system can further include a learning unit that learns user behavioral patterns. The learning unit can learn, for example, when a user has sent emails in the past and what content the emails have contained, and based on this, improve the accuracy of incident predictions. The learning unit can also analyze the tendency of emails sent by a user during a specific time period and predict the risk of incidents during that time period. Furthermore, the learning unit can learn a user's behavioral patterns when sending emails related to a specific project and predict the risk of incidents related to that project. This can improve the accuracy of incident predictions based on a user's behavioral patterns.

[0158] The email transmission incident prevention system may further include a risk assessment unit that estimates the user's emotions and assesses the risk of an incident based on the estimated emotions. For example, if the user is feeling stressed, the risk assessment unit may assess that the risk of an incident is high and prompt the user for additional confirmation before sending. If the user is relaxed, the risk assessment unit may assess that the risk of an incident is low and allow the user to perform normal sending procedures. If the user is in a hurry, the risk assessment unit may assess that the risk of an incident is high and display a warning message before sending. This allows the risk of an incident to be assessed based on the user's emotions and appropriate measures to be taken.

[0159] The email sending incident prevention system may further include a prediction unit that analyzes the user's email sending history and builds a prediction model based on past incident patterns. The prediction unit can, for example, analyze the characteristics of emails that have been sent by mistake in the past and display a warning for emails with similar characteristics. The prediction unit can also learn the patterns of emails that have leaked confidential information in the past and block the sending of emails with similar patterns. Furthermore, the prediction unit can calculate the probability of an incident occurring under specific conditions based on past incident data and take appropriate measures based on that probability. This makes it possible to build a prediction model based on past incident patterns and prevent incidents from occurring.

[0160] The email transmission incident prevention system may further include a warning customization unit that estimates the user's emotions and customizes the content of the warning message based on the estimated emotions. For example, if the user is nervous, the warning customization unit may display a warning message in a calm tone, allowing the user to respond calmly. If the user is relaxed, the warning customization unit may display a detailed warning message, allowing the user to fully understand the message and respond accordingly. Furthermore, if the user is in a hurry, the warning customization unit may display a concise and to-the-point warning message, encouraging a quick response. This allows the content of the warning message to be customized according to the user's emotions, making it possible to provide more effective warnings.

[0161] The email sending incident prevention system may further include a monitoring unit that monitors users' behavior when sending emails in real time and detects abnormal behavior. For example, if a user attempts to send an email at an unusual time, the monitoring unit may determine this as an abnormality and prompt the user to confirm before sending. Furthermore, if a user attempts to send an email to an unusual destination, the monitoring unit may determine this as an abnormality and display a warning message before sending. Furthermore, if a user attempts to send an email with unusual content, the monitoring unit may determine this as an abnormality and block the sending. This allows users' behavior when sending emails to be monitored in real time, detect abnormal behavior, and prevent incidents from occurring.

[0162] The email transmission incident prevention system may further include a risk assessment unit that estimates the user's emotions and assesses the risk of an incident based on the estimated emotions. For example, if the user is feeling stressed, the risk assessment unit may assess that the risk of an incident is high and prompt the user for additional confirmation before sending. If the user is relaxed, the risk assessment unit may assess that the risk of an incident is low and allow the user to perform normal sending procedures. If the user is in a hurry, the risk assessment unit may assess that the risk of an incident is high and display a warning message before sending. This allows the risk of an incident to be assessed based on the user's emotions and appropriate measures to be taken.

[0163] The email sending incident prevention system may further include a prediction unit that analyzes the user's email sending history and builds a prediction model based on past incident patterns. The prediction unit can, for example, analyze the characteristics of emails that have been sent by mistake in the past and display a warning for emails with similar characteristics. The prediction unit can also learn the patterns of emails that have leaked confidential information in the past and block the sending of emails with similar patterns. Furthermore, the prediction unit can calculate the probability of an incident occurring under specific conditions based on past incident data and take appropriate measures based on that probability. This makes it possible to build a prediction model based on past incident patterns and prevent incidents from occurring.

[0164] The email transmission incident prevention system may further include a warning customization unit that estimates the user's emotions and customizes the content of the warning message based on the estimated emotions. For example, if the user is nervous, the warning customization unit may display a warning message in a calm tone, allowing the user to respond calmly. If the user is relaxed, the warning customization unit may display a detailed warning message, allowing the user to fully understand the message and respond accordingly. Furthermore, if the user is in a hurry, the warning customization unit may display a concise and to-the-point warning message, encouraging a quick response. This allows the content of the warning message to be customized according to the user's emotions, making it possible to provide more effective warnings.

[0165] The email sending incident prevention system may further include a monitoring unit that monitors users' behavior when sending emails in real time and detects abnormal behavior. For example, if a user attempts to send an email at an unusual time, the monitoring unit may determine this as an abnormality and prompt the user to confirm before sending. Furthermore, if a user attempts to send an email to an unusual destination, the monitoring unit may determine this as an abnormality and display a warning message before sending. Furthermore, if a user attempts to send an email with unusual content, the monitoring unit may determine this as an abnormality and block the sending. This allows users' behavior when sending emails to be monitored in real time, detect abnormal behavior, and prevent incidents from occurring.

[0166] The email transmission incident prevention system may further include a risk assessment unit that estimates the user's emotions and assesses the risk of an incident based on the estimated emotions. For example, if the user is feeling stressed, the risk assessment unit may assess that the risk of an incident is high and prompt the user for additional confirmation before sending. If the user is relaxed, the risk assessment unit may assess that the risk of an incident is low and allow the user to perform normal sending procedures. If the user is in a hurry, the risk assessment unit may assess that the risk of an incident is high and display a warning message before sending. This allows the risk of an incident to be assessed based on the user's emotions and appropriate measures to be taken.

[0167] The processing flow of the second embodiment will be briefly explained below.

[0168] Step 1: The collection unit collects email content. The collection unit collects information such as the email body, recipients, attachments, and storage files. The collection unit can also use AI to adjust the type of data collected and the frequency of collection. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit determines whether the content contains confidential information or whether it may have been sent by mistake. The analysis unit can also use AI to adjust the algorithms used and the accuracy of the analysis. Step 3: The determination unit determines whether the content analyzed by the analysis unit is an incident. The determination unit makes the determination based on, for example, the definition of an incident and a determination threshold. The determination unit can also use AI to adjust the determination criteria. Step 4: The blocking unit blocks transmission if the judgment unit determines that the transmission is equivalent to an incident. The blocking unit can, for example, set the conditions for blocking and the method for unblocking the transmission. The blocking unit can also use AI to adjust the blocking method. Step 5: The warning unit displays a warning message if the blocking unit blocks the sending. For example, the warning unit displays a warning message such as "This email cannot be sent because it contains confidential information." The warning unit can also use AI to adjust the content and display method of the warning message.

[0169] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0170] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0174] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0175] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0181] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0182] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0185] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0190] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0191] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0192] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0193] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0195] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0196] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0197] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0198] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0199] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0200] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0201] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0203] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0204] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0205] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0206] 7, a 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.

[0207] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0208] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0209] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0211] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0212] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0213] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0214] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0216] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0217] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0218] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0219] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0220] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0221] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0222] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0223] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0224] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0225] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0226] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0227] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0228] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0229] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0230] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0231] 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.

[0232] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0233] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0234] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0235] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0236] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0237] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0238] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0239] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0240] [Explanation of symbols]

[0241] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects email content; an analysis unit that analyzes the content collected by the collection unit; a determination unit that determines whether the content analyzed by the analysis unit corresponds to an incident; a blocking unit that blocks transmission when the determining unit determines that the transmission is equivalent to an incident; a warning unit that displays a warning message when transmission is blocked by the blocking unit; Equipped with A system characterized by:

2. Equipped with a consolidation unit that consolidates incident prevention events 2. The system of claim 1.

3. Equipped with an analysis section that performs pattern and trend analysis 2. The system of claim 1.

4. We have a customization department that handles customization for individual companies.

2. The system of claim 1.

5. The collecting unit Collects information about email body, recipients, attachments, and storage files 2. The system of claim 1.

6. The analysis unit Analyze the collected information to determine whether it contains confidential information or whether it may have been sent by mistake 2. The system of claim 1.

7. The warning unit Show a warning message that this email contains confidential information and cannot be sent 2. The system of claim 1.

8. The collecting unit Infer user emotions and adjust email collection timing based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Analyze users' past email sending history and select the appropriate collection method 2. The system of claim 1.

Images