system

Abstract

The system according to the embodiment aims to determine the risk of fraud or crime lurking in messages that a user views and to display a warning. According to an embodiment, the system includes a collection unit, an analysis unit, and a warning unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit and determines the risk of fraud or crime. The warning unit displays a warning for messages that are determined by the analysis unit to pose a risk of fraud or crime.

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 technologies are not sufficient in quickly determining and warning users about the risk of fraud or crime that may be present in messages they view, and there is room for improvement.

[0005] The system according to the embodiment aims to determine the risk of fraud or crime lurking in messages that a user views and to display a warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit and determines the risk of fraud or crime. The warning unit displays a warning for messages that are determined by the analysis unit to be at risk of fraud or crime. [Effects of the Invention]

[0007] The system according to the embodiment can determine the risk of fraud or crime lurking in messages that a user views and display a warning. [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) A smartphone app according to an embodiment of the present invention is a system that uses a generation AI to analyze email messages and social media posts viewed by users to determine whether they are potentially fraudulent or criminal. The smartphone app collects emails received and social media messages viewed by users, and the generation AI analyzes these messages to determine whether they are fraudulent or criminal. For example, a warning is displayed for messages that may be fraudulent, alerting the user. This mechanism contributes to society by preventing fraud and fraudulent use. For example, the generation AI collects the content of emails received by users and posts viewed on social media, and analyzes these messages. The generation AI analyzes the content of these messages to determine whether they are fraudulent or criminal. For example, potentially fraudulent messages often contain specific keywords or patterns. The generation AI detects these keywords and patterns to determine whether they are fraudulent. Furthermore, a warning is displayed for potentially fraudulent messages. For example, if a user receives a potentially fraudulent email, the generation AI displays a warning on the email to alert the user. This reduces the user's risk of being involved in fraud. This smartphone app contributes to society by preventing fraud and fraudulent use. For example, if a LINE (registered trademark) group or Facebook (registered trademark) post contains a possible investment scam, the generation AI will detect the message and display a warning to the user. It can also detect messages that may contain viruses and warn the user. In this way, by using generation AI to analyze the text of emails and social media posts viewed by users and determine the possibility of dangerous frauds or criminal cases, it is possible to prevent fraud and fraudulent use and contribute to society.

[0029] A smartphone app according to an embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects emails received by a user and social media messages viewed by the user. For example, the collection unit can collect the text of received emails and the content of posts viewed on social media. The collection unit can also use a generation AI to collect detailed message content. The analysis unit uses the generation AI to analyze the messages collected by the collection unit. For example, the analysis unit analyzes the content of the messages to determine whether they are fraudulent or criminal. The generation AI can detect specific keywords or patterns based on the content of the messages to determine whether they are fraudulent. For example, the generation AI can detect messages containing keywords such as "fraud" or "crime" and determine whether they are fraudulent. The generation AI can also analyze the context and content of the messages to detect fraudulent patterns. The warning unit displays a warning for messages that the analysis unit determines may be fraudulent or criminal. For example, the warning unit can display a warning for messages that may be fraudulent by using a pop-up notification or highlighting the email. The warning unit can also provide countermeasures for when a user ignores the warning. For example, the warning unit may have a function to notify the user again if the user ignores the warning, or a function to temporarily suspend the account. As a result, the smartphone app according to the embodiment can analyze the text of emails and SNS posts that the user views and determine the possibility of fraud or crime, thereby preventing fraud and unauthorized use from occurring.

[0030] The collection unit can collect the contents of received emails or SNS messages. The collection unit can collect, for example, the body of received emails or the contents of posts viewed on SNS. For example, the collection unit analyzes the body of received emails and collects it as text data. The collection unit can also analyze the content of SNS messages and collect it as text data. For example, the collection unit analyzes the body of SNS messages and collects it as text data. The collection unit can also analyze images and videos in SNS messages and collect them as text data. In this way, by collecting the content of received emails and SNS messages, data can be provided for determining the possibility of fraud or crime. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the body of received emails into a generation AI, which collects it as text data.

[0031] The analysis unit can analyze the content of the message and determine the possibility of fraud or crime. The analysis unit can analyze the content of the message and determine the possibility of fraud or crime, for example, using a generation AI. For example, the analysis unit can detect specific keywords or patterns based on the content of the message and determine the possibility of fraud. For example, the generation AI can detect messages containing keywords such as "fraud" or "crime" and determine the possibility of fraud. The generation AI can also analyze the context and content of the message and detect fraud patterns. For example, the generation AI can analyze the context of the message and determine the possibility of fraud. The generation AI can also analyze the content of the message and detect fraud patterns. In this way, by analyzing the content of the message, the possibility of fraud or crime can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the content of the message into the generation AI, which can then determine the possibility of fraud.

[0032] The warning unit can display a warning for potentially fraudulent messages. The warning unit can display a warning for potentially fraudulent messages, for example, by using a pop-up notification or highlighting the email. For example, the warning unit can display a pop-up notification for potentially fraudulent messages to alert the user. The warning unit can also highlight potentially fraudulent messages in email to alert the user. For example, the warning unit can highlight potentially fraudulent messages in red to alert the user. The warning unit can also highlight potentially fraudulent messages in yellow to alert the user. In this way, by displaying a warning for potentially fraudulent messages, the user can be alerted and fraud can be prevented. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the warning unit can input potentially fraudulent messages into the generation AI, which can then display a warning.

[0033] The warning unit can display the warning by a pop-up notification or by highlighting the email. For example, the warning unit can display a pop-up notification for a potentially fraudulent message to alert the user. For example, the warning unit can display a pop-up notification for a potentially fraudulent message to alert the user. The warning unit can also highlight a potentially fraudulent message in an email to alert the user. For example, the warning unit can highlight a potentially fraudulent message in red to alert the user. The warning unit can also highlight a potentially fraudulent message in yellow to alert the user. In this way, by displaying a visual warning to the user, it is possible to quickly notify the user of the possibility of fraud or crime. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input a potentially fraudulent message into the generation AI, which can then display the warning.

[0034] The warning unit may be equipped with specific measures to be taken in the event that a user ignores a warning. The warning unit may, for example, have a function to re-notify the user if the user ignores a warning. For example, the warning unit may re-notify the user if the user ignores a warning, and warn the user again. The warning unit may also have a function to suspend the account if the user ignores a warning. For example, the warning unit may suspend the account if the user ignores a warning, and warn the user again. This makes it possible to provide measures to reduce the risk of fraud or crime even if the user ignores a warning. Some or all of the above-described processing in the warning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the warning unit may cause the generation AI to execute a function to re-notify the user if the user ignores a warning.

[0035] The collection unit can analyze the user's past message viewing history and select an appropriate collection method. The collection unit can, for example, analyze the user's past message viewing history and select an appropriate collection method. For example, the collection unit can prioritize collecting messages from social media platforms that the user frequently visits. The collection unit can also analyze the time periods in which the user received fraudulent messages in the past and focus collection on those time periods. For example, the collection unit can analyze the time periods in which the user received fraudulent messages in the past and focus collection on those time periods. The collection unit can also prioritize collecting messages containing a specific keyword if the user frequently views messages containing that keyword. For example, if the user frequently views messages containing a specific keyword, the collection unit prioritizes collecting messages containing that keyword. This allows the analysis of the user's past message viewing history to select an optimal collection method and achieve efficient message collection. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past message viewing history into the generation AI, which can select the optimal collection method.

[0036] The collection unit can filter messages based on the user's current areas of interest when collecting messages. For example, the collection unit can filter messages based on the user's current areas of interest when collecting messages. For example, the collection unit prioritizes collecting messages related to topics in which the user is currently interested. Furthermore, if the user frequently views a specific news article, the collection unit can also collect messages related to that topic. For example, if the user frequently views a specific news article, the collection unit can collect messages related to that topic. Furthermore, if the user uses a specific hashtag, the collection unit can prioritize collecting messages including that hashtag. For example, if the user uses a specific hashtag, the collection unit prioritizes collecting messages including that hashtag. This allows for filtering messages based on the user's current areas of interest, thereby prioritizing the collection of highly relevant messages. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current areas of interest into the generation AI, and the generation AI can perform the filtering.

[0037] The collection unit can select an appropriate collection means according to the user's input method when collecting messages. The collection unit can select an appropriate collection means according to the user's input method when collecting messages. For example, when the user uses voice input, the collection unit prioritizes collecting voice messages. Also, when the user uses a lot of text messages, the collection unit can prioritize collecting text messages. For example, when the user uses a lot of text messages, the collection unit prioritizes collecting text messages. Also, when the user sends a lot of images, the collection unit can prioritize collecting image messages. For example, when the user sends a lot of images, the collection unit prioritizes collecting image messages. This enables efficient message collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's input method to the generation AI, which can select the appropriate collection means.

[0038] When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the user's geographical location information. For example, when collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting messages related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting local news and information. For example, when the user is at home, the collection unit prioritizes collecting local news and information. In this way, by prioritizing the collection of highly relevant messages by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant messages.

[0039] The collection unit can analyze the user's social media activity and collect relevant messages when collecting messages. For example, when collecting messages, the collection unit can analyze the user's social media activity and collect relevant messages. For example, if the user is active on a particular social media platform, the collection unit can prioritize collecting messages from that platform. Furthermore, if the user is a member of a particular group or community, the collection unit can prioritize collecting messages from that group. For example, if the user is a member of a particular group or community, the collection unit can prioritize collecting messages from that group. Furthermore, if the user frequently uses a particular hashtag, the collection unit can prioritize collecting messages including that hashtag. For example, if the user frequently uses a particular hashtag, the collection unit prioritizes collecting messages including that hashtag. This allows for the prioritized collection of highly relevant messages by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity into a generation AI, which can then collect relevant messages.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting messages. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting messages. For example, the collection unit can analyze the characteristics of messages that the user previously determined to be important and prioritize collecting similar messages. The collection unit can also analyze the characteristics of messages that the user previously ignored and avoid collecting similar messages. For example, the collection unit can analyze the characteristics of messages that the user previously ignored and avoid collecting similar messages. The collection unit can also adjust the collection method by referring to the content of messages for which the user previously provided feedback. For example, the collection unit adjusts the collection method by referring to the content of messages for which the user previously provided feedback. In this way, the collection method can be customized by reflecting the user's past feedback, thereby achieving efficient message collection. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit can perform a detailed analysis on messages of high importance to clarify risks. The analysis unit can also perform a concise analysis on messages of low importance to provide the minimum necessary information. For example, the analysis unit can perform a concise analysis on messages of low importance to provide the minimum necessary information. The analysis unit can also perform an analysis with a moderate level of detail on messages of medium importance. For example, the analysis unit can perform an analysis with a moderate level of detail on messages of medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the message, it is possible to appropriately provide the necessary information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the message to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the message category during analysis. The analysis unit can apply different analysis algorithms depending on the message category during analysis, for example. For example, the analysis unit can apply a specific keyword detection algorithm to fraudulent messages. The analysis unit can also apply a pattern recognition algorithm to criminal messages. For example, the analysis unit can apply a pattern recognition algorithm to criminal messages. The analysis unit can also apply a malware detection algorithm to messages that may contain a virus. For example, the analysis unit can apply a malware detection algorithm to messages that may contain a virus. In this way, by applying different analysis algorithms depending on the message category, it is possible to determine the possibility of fraud or crime with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the message category into a generation AI, which then applies different analysis algorithms.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can learn the characteristics of messages that the user has previously determined to be fraudulent and detect similar messages with high accuracy. The analysis unit can also learn the characteristics of messages that the user has previously ignored and exclude similar messages. For example, the analysis unit can learn the characteristics of messages that the user has previously ignored and exclude similar messages. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time when the message was sent during analysis. The analysis unit can, for example, determine the analysis priority based on the time when the message was sent during analysis. For example, the analysis unit prioritizes analyzing recently sent messages. The analysis unit can also prioritize analyzing messages sent during a specific time period. For example, the analysis unit prioritizes analyzing messages sent during a specific time period. The analysis unit can also prioritize analyzing messages viewed by a user during a specific time period. For example, the analysis unit prioritizes analyzing messages viewed by a user during a specific time period. In this way, by determining the analysis priority based on the time when the message was sent, important messages can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the time when the message was sent into the generation AI, and the generation AI can determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize analyzing messages from people with whom the user frequently communicates. The analysis unit can also prioritize analyzing messages that the user relates to a specific topic. For example, the analysis unit can prioritize analyzing messages that the user relates to a specific topic. The analysis unit can also prioritize analyzing related messages based on characteristics of messages that the user has previously determined to be important. For example, the analysis unit prioritizes analyzing related messages based on characteristics of messages that the user has previously determined to be important. In this way, by adjusting the order of analysis based on the relevance of messages, highly relevant messages can be prioritized for analysis. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of messages into the generation AI, and the generation AI can adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand language. For example, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand language. For example, the analysis unit can adjust the use of appropriate technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0047] The warning unit can adjust the level of detail of the warning based on the risk level of the message when displaying the warning. For example, the warning unit can adjust the level of detail of the warning based on the risk level of the message when displaying the warning. For example, the warning unit can display a detailed warning for a high-risk message to clarify the risk. The warning unit can also display a moderately detailed warning for a medium-risk message. For example, the warning unit can display a moderately detailed warning for a medium-risk message. The warning unit can also display a concise warning for a low-risk message. For example, the warning unit can display a concise warning for a low-risk message. In this way, by adjusting the level of detail of the warning based on the risk level of the message, it is possible to provide an appropriate warning to the user. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the risk level of the message to the generation AI, and the generation AI can adjust the level of detail of the warning.

[0048] The warning unit can apply different warning display methods depending on the message category when displaying a warning. The warning unit can apply different warning display methods depending on the message category when displaying a warning. For example, the warning unit can display a red warning for fraudulent messages to warn the user. The warning unit can also display a yellow warning for criminal messages to warn the user. For example, the warning unit can display a yellow warning for criminal messages to warn the user. The warning unit can also display an orange warning for messages that may contain a virus to warn the user. For example, the warning unit can display an orange warning for messages that may contain a virus to warn the user. In this way, by applying different warning display methods depending on the message category, it is possible to provide an appropriate warning to the user. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the message category into the generation AI, and the generation AI can apply different warning display methods.

[0049] The warning unit can improve the accuracy of the warning by referring to the user's past warning responses when displaying the warning. The warning unit can improve the accuracy of the warning by referring to the user's past warning responses when displaying the warning. For example, the warning unit can learn the characteristics of warnings that the user has ignored in the past and prevent similar warnings from being displayed. The warning unit can also learn the characteristics of warnings to which the user has responded in the past and prioritize displaying similar warnings. For example, the warning unit can learn the characteristics of warnings to which the user has responded in the past and prioritize displaying similar warnings. The warning unit can also adjust the warning display method based on the user's past feedback. For example, the warning unit adjusts the warning display method based on the user's past feedback. In this way, the accuracy of the warning can be improved by referring to the user's past warning responses. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the user's past warning responses into the generation AI, which can improve the accuracy of the warning.

[0050] The warning unit, when displaying a warning, can determine the priority of the warning based on the time the message was sent. For example, when displaying a warning, the warning unit can determine the priority of the warning based on the time the message was sent. For example, the warning unit can prioritize displaying a warning for a recently sent message. The warning unit can also prioritize displaying a warning for a message sent during a specific time period. For example, the warning unit can prioritize displaying a warning for a message sent during a specific time period. The warning unit can also prioritize displaying a warning for a message that the user will view during a specific time period. For example, the warning unit can prioritize displaying a warning for a message that the user will view during a specific time period. In this way, by determining the priority of the warning based on the time the message was sent, important warnings can be displayed quickly. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the time the message was sent into the generation AI, and the generation AI can determine the priority of the warning.

[0051] The warning unit can adjust the order of warnings based on the relevance of messages when displaying a warning. The warning unit can adjust the order of warnings based on the relevance of messages when displaying a warning, for example. For example, the warning unit can prioritize displaying warnings for messages from people with whom the user frequently communicates. The warning unit can also prioritize displaying warnings for messages related to a specific topic that the user is interested in. For example, the warning unit can prioritize displaying warnings for messages related to a specific topic that the user is interested in. The warning unit can also prioritize displaying warnings for related messages based on characteristics of messages that the user has previously determined to be important. For example, the warning unit can prioritize displaying warnings for related messages based on characteristics of messages that the user has previously determined to be important. In this way, by adjusting the order of warnings based on the relevance of messages, it is possible to prioritize displaying highly relevant warnings. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the relevance of messages into the generation AI, and the generation AI can adjust the order of warnings.

[0052] The warning unit may adjust the use of technical terminology in the warning according to the user's level of expertise when displaying the warning. For example, when displaying the warning, the warning unit may adjust the use of technical terminology in the warning according to the user's level of expertise. For example, if the user has technical expertise, the warning unit may display the warning using detailed technical terminology. Furthermore, if the user does not have technical expertise, the warning unit may display the warning using concise and easy-to-understand language. For example, if the user does not have technical expertise, the warning unit may display the warning using concise and easy-to-understand language. Furthermore, the warning unit may adjust the use of appropriate technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a warning that is easy for the user to understand. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the warning unit may input the user's level of expertise into the generation AI, which may adjust the use of technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit may also collect data from other applications on the user's device. For example, the collection unit may collect data from messaging apps and calendar apps used by the user to provide information for determining the possibility of fraud or crime. The collection unit may also collect the user's browser history and search history and use this data for analysis. Furthermore, the collection unit may collect the user's location information and device sensor information to provide data for more accurately determining the possibility of fraud or crime. This allows the collection unit to collect information from various data sources on the user's device and make a more comprehensive determination of the possibility of fraud or crime.

[0055] The analysis unit can learn a user's past behavioral patterns and predict the possibility of fraud or crime. For example, the analysis unit can learn what messages a user has received in the past and how the user responded, and display a warning if a similar pattern is observed. The analysis unit can also analyze trends in messages a user receives during a specific time period and prioritize the detection of messages during that time period that are likely to be fraudulent. Furthermore, the analysis unit can analyze the behavioral patterns of other users in the user's social network and detect potentially fraudulent messages early on. This allows the analysis unit to utilize a user's past behavioral patterns to more accurately predict the possibility of fraud or crime.

[0056] The warning unit can learn the user's past reactions to warnings and optimize the warning display method. For example, the warning unit can learn the characteristics of warnings that the user has ignored in the past and prevent similar warnings from being displayed. The warning unit can also learn the characteristics of warnings to which the user has responded in the past and display similar warnings with priority. Furthermore, the warning unit can adjust the warning display method based on the user's past feedback. This can improve the accuracy of warnings by referring to the user's past reactions to warnings.

[0057] The analysis unit can evaluate not only the content of the message but also the trustworthiness of the sender. For example, the analysis unit analyzes the sender's past message history and prioritizes detecting messages from senders with low trustworthiness. The analysis unit can also evaluate the sender's reputation within a social network and warn of messages from senders with low trustworthiness. Furthermore, the analysis unit can evaluate the trustworthiness of the sender's email address or IP address and detect messages from senders with a high likelihood of fraud. This allows the analysis unit to more accurately determine the possibility of fraud or crime by evaluating the sender's trustworthiness.

[0058] The collection unit can prioritize collecting highly relevant messages in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting messages related to that area. Also, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting local news and information. In this way, by prioritized collection of highly relevant messages in consideration of the user's geographical location information, it is possible to provide useful information to the user.

[0059] The warning unit can adjust the level of detail of the warning based on the risk level of the message. For example, the warning unit can display a detailed warning for a high-risk message to clarify the risk. The warning unit can also display a moderate level of detail for a medium-risk message. Furthermore, the warning unit can display a concise warning for a low-risk message. In this way, by adjusting the level of detail of the warning based on the risk level of the message, it is possible to provide an appropriate warning to the user.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects emails received by the user and SNS messages viewed by the user. For example, the collection unit can collect the text of received emails and the content of posts viewed on SNS. The collection unit can also use generation AI to collect detailed information about the content of messages. Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. For example, the analysis unit analyzes the content of the messages and determines the possibility of fraud or crime. The generation AI can detect specific keywords and patterns based on the content of the messages and determine the possibility of fraud. For example, it can detect messages containing keywords such as "fraud" or "crime" and determine the possibility of fraud. The generation AI can also analyze the context and content of messages to detect fraud patterns. Step 3: The warning unit displays a warning for messages that are determined by the analysis unit to be potentially fraudulent or criminal. For example, the warning unit can display a warning for potentially fraudulent messages by using a pop-up notification or highlighting the email. The warning unit can also have measures in place to deal with cases where a user ignores the warning. For example, the warning unit can have a function to notify the user again if the user ignores the warning, or a function to suspend the account.

[0062] (Example 2) A smartphone app according to an embodiment of the present invention is a system that uses a generation AI to analyze email messages and social media posts viewed by users to determine whether they are potentially fraudulent or criminal. The smartphone app collects emails received and social media messages viewed by users, and the generation AI analyzes these messages to determine whether they are fraudulent or criminal. For example, a warning is displayed for messages that may be fraudulent, alerting the user. This mechanism contributes to society by preventing fraud and fraudulent use. For example, the generation AI collects the content of emails received by users and posts viewed on social media, and analyzes these messages. The generation AI analyzes the content of these messages to determine whether they are fraudulent or criminal. For example, potentially fraudulent messages often contain specific keywords or patterns. The generation AI detects these keywords and patterns to determine whether they are fraudulent. Furthermore, a warning is displayed for potentially fraudulent messages. For example, if a user receives a potentially fraudulent email, the generation AI displays a warning on the email to alert the user. This reduces the user's risk of being involved in fraud. This smartphone app contributes to society by preventing fraud and fraudulent use. For example, if a message in a LINE group or Facebook post appears to be an investment scam, the generation AI will detect the message and display a warning to the user. It can also detect messages that may contain viruses and warn the user. In this way, by using generation AI to analyze the text of emails and social media posts viewed by users and determine the possibility of dangerous frauds or criminal cases, it is possible to prevent fraud and fraudulent use and contribute to society.

[0063] A smartphone app according to an embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects emails received by a user and social media messages viewed by the user. For example, the collection unit can collect the text of received emails and the content of posts viewed on social media. The collection unit can also use a generation AI to collect detailed message content. The analysis unit uses the generation AI to analyze the messages collected by the collection unit. For example, the analysis unit analyzes the content of the messages to determine whether they are fraudulent or criminal. The generation AI can detect specific keywords or patterns based on the content of the messages to determine whether they are fraudulent. For example, the generation AI can detect messages containing keywords such as "fraud" or "crime" and determine whether they are fraudulent. The generation AI can also analyze the context and content of the messages to detect fraudulent patterns. The warning unit displays a warning for messages that the analysis unit determines may be fraudulent or criminal. For example, the warning unit can display a warning for messages that may be fraudulent by using a pop-up notification or highlighting the email. The warning unit can also provide countermeasures for when a user ignores the warning. For example, the warning unit may have a function to notify the user again if the user ignores the warning, or a function to temporarily suspend the account. As a result, the smartphone app according to the embodiment can analyze the text of emails and SNS posts that the user views and determine the possibility of fraud or crime, thereby preventing fraud and unauthorized use from occurring.

[0064] The collection unit can collect the contents of received emails or SNS messages. The collection unit can collect, for example, the body of received emails or the contents of posts viewed on SNS. For example, the collection unit analyzes the body of received emails and collects it as text data. The collection unit can also analyze the content of SNS messages and collect it as text data. For example, the collection unit analyzes the body of SNS messages and collects it as text data. The collection unit can also analyze images and videos in SNS messages and collect them as text data. In this way, by collecting the content of received emails and SNS messages, data can be provided for determining the possibility of fraud or crime. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the body of received emails into a generation AI, which collects it as text data.

[0065] The analysis unit can analyze the content of the message and determine the possibility of fraud or crime. The analysis unit can analyze the content of the message and determine the possibility of fraud or crime, for example, using a generation AI. For example, the analysis unit can detect specific keywords or patterns based on the content of the message and determine the possibility of fraud. For example, the generation AI can detect messages containing keywords such as "fraud" or "crime" and determine the possibility of fraud. The generation AI can also analyze the context and content of the message and detect fraud patterns. For example, the generation AI can analyze the context of the message and determine the possibility of fraud. The generation AI can also analyze the content of the message and detect fraud patterns. In this way, by analyzing the content of the message, the possibility of fraud or crime can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the content of the message into the generation AI, which can then determine the possibility of fraud.

[0066] The warning unit can display a warning for potentially fraudulent messages. The warning unit can display a warning for potentially fraudulent messages, for example, by using a pop-up notification or highlighting the email. For example, the warning unit can display a pop-up notification for potentially fraudulent messages to alert the user. The warning unit can also highlight potentially fraudulent messages in email to alert the user. For example, the warning unit can highlight potentially fraudulent messages in red to alert the user. The warning unit can also highlight potentially fraudulent messages in yellow to alert the user. In this way, by displaying a warning for potentially fraudulent messages, the user can be alerted and fraud can be prevented. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the warning unit can input potentially fraudulent messages into the generation AI, which can then display a warning.

[0067] The warning unit can display the warning by a pop-up notification or by highlighting the email. For example, the warning unit can display a pop-up notification for a potentially fraudulent message to alert the user. For example, the warning unit can display a pop-up notification for a potentially fraudulent message to alert the user. The warning unit can also highlight a potentially fraudulent message in an email to alert the user. For example, the warning unit can highlight a potentially fraudulent message in red to alert the user. The warning unit can also highlight a potentially fraudulent message in yellow to alert the user. In this way, by displaying a visual warning to the user, it is possible to quickly notify the user of the possibility of fraud or crime. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input a potentially fraudulent message into the generation AI, which can then display the warning.

[0068] The warning unit may be equipped with specific measures to be taken in the event that a user ignores a warning. The warning unit may, for example, have a function to re-notify the user if the user ignores a warning. For example, the warning unit may re-notify the user if the user ignores a warning, and warn the user again. The warning unit may also have a function to suspend the account if the user ignores a warning. For example, the warning unit may suspend the account if the user ignores a warning, and warn the user again. This makes it possible to provide measures to reduce the risk of fraud or crime even if the user ignores a warning. Some or all of the above-described processing in the warning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the warning unit may cause the generation AI to execute a function to re-notify the user if the user ignores a warning.

[0069] The collection unit can estimate the user's emotions and adjust the timing of message collection based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of message collection based on the estimated user emotions. For example, if the user is stressed, the collection unit delays message collection and collects messages when the user is relaxed. The collection unit can also immediately collect messages and quickly analyze them when the user is relaxed. For example, if the user is relaxed, the collection unit can immediately collect messages and quickly analyze them. The collection unit can also temporarily stop message collection when the user is in a hurry and collect them all at once later. For example, if the user is in a hurry, the collection unit can temporarily stop message collection and collect them all at once later. This allows the timing of message collection to be adjusted according to the user's emotions, thereby reducing user stress and achieving efficient message collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which may infer the emotion and adjust the collection timing.

[0070] The collection unit can analyze the user's past message viewing history and select an appropriate collection method. The collection unit can, for example, analyze the user's past message viewing history and select an appropriate collection method. For example, the collection unit can prioritize collecting messages from social media platforms that the user frequently visits. The collection unit can also analyze the time periods in which the user received fraudulent messages in the past and focus collection on those time periods. For example, the collection unit can analyze the time periods in which the user received fraudulent messages in the past and focus collection on those time periods. The collection unit can also prioritize collecting messages containing a specific keyword if the user frequently views messages containing that keyword. For example, if the user frequently views messages containing a specific keyword, the collection unit prioritizes collecting messages containing that keyword. This allows the analysis of the user's past message viewing history to select an optimal collection method and achieve efficient message collection. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past message viewing history into the generation AI, which can select the optimal collection method.

[0071] The collection unit can filter messages based on the user's current areas of interest when collecting messages. For example, the collection unit can filter messages based on the user's current areas of interest when collecting messages. For example, the collection unit prioritizes collecting messages related to topics in which the user is currently interested. Furthermore, if the user frequently views a specific news article, the collection unit can also collect messages related to that topic. For example, if the user frequently views a specific news article, the collection unit can collect messages related to that topic. Furthermore, if the user uses a specific hashtag, the collection unit can prioritize collecting messages including that hashtag. For example, if the user uses a specific hashtag, the collection unit prioritizes collecting messages including that hashtag. This allows for filtering messages based on the user's current areas of interest, thereby prioritizing the collection of highly relevant messages. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current areas of interest into the generation AI, and the generation AI can perform the filtering.

[0072] The collection unit can select an appropriate collection means according to the user's input method when collecting messages. The collection unit can select an appropriate collection means according to the user's input method when collecting messages. For example, when the user uses voice input, the collection unit prioritizes collecting voice messages. Also, when the user uses a lot of text messages, the collection unit can prioritize collecting text messages. For example, when the user uses a lot of text messages, the collection unit prioritizes collecting text messages. Also, when the user sends a lot of images, the collection unit can prioritize collecting image messages. For example, when the user sends a lot of images, the collection unit prioritizes collecting image messages. This enables efficient message collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's input method to the generation AI, which can select the appropriate collection means.

[0073] The collection unit can estimate the user's emotions and determine the priority of messages to be collected based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and determine the priority of messages to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones messages of low importance. The collection unit can also collect all messages equally when the user is relaxed. For example, when the user is relaxed, the collection unit collects all messages equally. The collection unit can also prioritize collecting messages of high importance when the user is in a hurry. For example, when the user is in a hurry, the collection unit prioritizes collecting messages of high importance. In this way, by determining the priority of messages to be collected according to the user's emotions, important messages can be prioritized and collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then estimate the emotions and determine the priority of the messages to be collected.

[0074] When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the user's geographical location information. For example, when collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting messages related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting local news and information. For example, when the user is at home, the collection unit prioritizes collecting local news and information. In this way, by prioritizing the collection of highly relevant messages by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant messages.

[0075] The collection unit can analyze the user's social media activity and collect relevant messages when collecting messages. For example, when collecting messages, the collection unit can analyze the user's social media activity and collect relevant messages. For example, if the user is active on a particular social media platform, the collection unit can prioritize collecting messages from that platform. Furthermore, if the user is a member of a particular group or community, the collection unit can prioritize collecting messages from that group. For example, if the user is a member of a particular group or community, the collection unit can prioritize collecting messages from that group. Furthermore, if the user frequently uses a particular hashtag, the collection unit can prioritize collecting messages including that hashtag. For example, if the user frequently uses a particular hashtag, the collection unit prioritizes collecting messages including that hashtag. This allows for the prioritized collection of highly relevant messages by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity into a generation AI, which can then collect relevant messages.

[0076] The collection unit can customize the collection method by reflecting the user's past feedback when collecting messages. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting messages. For example, the collection unit can analyze the characteristics of messages that the user previously determined to be important and prioritize collecting similar messages. The collection unit can also analyze the characteristics of messages that the user previously ignored and avoid collecting similar messages. For example, the collection unit can analyze the characteristics of messages that the user previously ignored and avoid collecting similar messages. The collection unit can also adjust the collection method by referring to the content of messages for which the user previously provided feedback. For example, the collection unit adjusts the collection method by referring to the content of messages for which the user previously provided feedback. In this way, the collection method can be customized by reflecting the user's past feedback, thereby achieving efficient message collection. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can, for example, estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit provides a detailed analysis result if the user is relaxed. The analysis unit can also provide a concise analysis result that focuses on the main points if the user is in a hurry. For example, the analysis unit provides a concise analysis result that focuses on the main points if the user is in a hurry. This allows the analysis result to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the way the analysis is presented.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit can perform a detailed analysis on messages of high importance to clarify risks. The analysis unit can also perform a concise analysis on messages of low importance to provide the minimum necessary information. For example, the analysis unit can perform a concise analysis on messages of low importance to provide the minimum necessary information. The analysis unit can also perform an analysis with a moderate level of detail on messages of medium importance. For example, the analysis unit can perform an analysis with a moderate level of detail on messages of medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the message, it is possible to appropriately provide the necessary information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the message to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the message category during analysis. The analysis unit can apply different analysis algorithms depending on the message category during analysis, for example. For example, the analysis unit can apply a specific keyword detection algorithm to fraudulent messages. The analysis unit can also apply a pattern recognition algorithm to criminal messages. For example, the analysis unit can apply a pattern recognition algorithm to criminal messages. The analysis unit can also apply a malware detection algorithm to messages that may contain a virus. For example, the analysis unit can apply a malware detection algorithm to messages that may contain a virus. In this way, by applying different analysis algorithms depending on the message category, it is possible to determine the possibility of fraud or crime with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the message category into a generation AI, which then applies different analysis algorithms.

[0080] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can learn the characteristics of messages that the user has previously determined to be fraudulent and detect similar messages with high accuracy. The analysis unit can also learn the characteristics of messages that the user has previously ignored and exclude similar messages. For example, the analysis unit can learn the characteristics of messages that the user has previously ignored and exclude similar messages. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can, for example, 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, to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. For example, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an appropriate amount of information for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may infer the emotion and adjust the length of the analysis.

[0082] The analysis unit can determine the analysis priority based on the time when the message was sent during analysis. The analysis unit can, for example, determine the analysis priority based on the time when the message was sent during analysis. For example, the analysis unit prioritizes analyzing recently sent messages. The analysis unit can also prioritize analyzing messages sent during a specific time period. For example, the analysis unit prioritizes analyzing messages sent during a specific time period. The analysis unit can also prioritize analyzing messages viewed by a user during a specific time period. For example, the analysis unit prioritizes analyzing messages viewed by a user during a specific time period. In this way, by determining the analysis priority based on the time when the message was sent, important messages can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the time when the message was sent into the generation AI, and the generation AI can determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize analyzing messages from people with whom the user frequently communicates. The analysis unit can also prioritize analyzing messages that the user relates to a specific topic. For example, the analysis unit can prioritize analyzing messages that the user relates to a specific topic. The analysis unit can also prioritize analyzing related messages based on characteristics of messages that the user has previously determined to be important. For example, the analysis unit prioritizes analyzing related messages based on characteristics of messages that the user has previously determined to be important. In this way, by adjusting the order of analysis based on the relevance of messages, highly relevant messages can be prioritized for analysis. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of messages into the generation AI, and the generation AI can adjust the order of analysis.

[0084] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand language. For example, if the user does not have technical expertise, the analysis unit can provide analysis results using concise and easy-to-understand language. For example, the analysis unit can adjust the use of appropriate technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0085] The warning unit can estimate the user's emotion and adjust the warning display method based on the estimated user's emotion. The warning unit can, for example, estimate the user's emotion and adjust the warning display method based on the estimated user's emotion. For example, if the user is nervous, the warning unit displays a simple, highly visible warning. Furthermore, if the user is relaxed, the warning unit can also display a warning including detailed information. For example, if the user is relaxed, the warning unit displays a warning including detailed information. Furthermore, if the user is in a hurry, the warning unit can also display a concise warning that hits the essentials. For example, if the user is in a hurry, the warning unit displays a concise warning that hits the essentials. In this way, by adjusting the warning display method according to the user's emotion, it is possible to provide an appropriate warning to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The 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. Some or all of the above-described processing in the warning unit can be performed using, for example, the generation AI, or without the generation AI. For example, the warning unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust how the warning is displayed.

[0086] The warning unit can adjust the level of detail of the warning based on the risk level of the message when displaying the warning. For example, the warning unit can adjust the level of detail of the warning based on the risk level of the message when displaying the warning. For example, the warning unit can display a detailed warning for a high-risk message to clarify the risk. The warning unit can also display a moderately detailed warning for a medium-risk message. For example, the warning unit can display a moderately detailed warning for a medium-risk message. The warning unit can also display a concise warning for a low-risk message. For example, the warning unit can display a concise warning for a low-risk message. In this way, by adjusting the level of detail of the warning based on the risk level of the message, it is possible to provide an appropriate warning to the user. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the risk level of the message to the generation AI, and the generation AI can adjust the level of detail of the warning.

[0087] The warning unit can apply different warning display methods depending on the message category when displaying a warning. The warning unit can apply different warning display methods depending on the message category when displaying a warning. For example, the warning unit can display a red warning for fraudulent messages to warn the user. The warning unit can also display a yellow warning for criminal messages to warn the user. For example, the warning unit can display a yellow warning for criminal messages to warn the user. The warning unit can also display an orange warning for messages that may contain a virus to warn the user. For example, the warning unit can display an orange warning for messages that may contain a virus to warn the user. In this way, by applying different warning display methods depending on the message category, it is possible to provide an appropriate warning to the user. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the message category into the generation AI, and the generation AI can apply different warning display methods.

[0088] The warning unit can improve the accuracy of the warning by referring to the user's past warning responses when displaying the warning. The warning unit can improve the accuracy of the warning by referring to the user's past warning responses when displaying the warning. For example, the warning unit can learn the characteristics of warnings that the user has ignored in the past and prevent similar warnings from being displayed. The warning unit can also learn the characteristics of warnings to which the user has responded in the past and prioritize displaying similar warnings. For example, the warning unit can learn the characteristics of warnings to which the user has responded in the past and prioritize displaying similar warnings. The warning unit can also adjust the warning display method based on the user's past feedback. For example, the warning unit adjusts the warning display method based on the user's past feedback. In this way, the accuracy of the warning can be improved by referring to the user's past warning responses. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the user's past warning responses into the generation AI, which can improve the accuracy of the warning.

[0089] The warning unit can estimate the user's emotion and adjust the length of the warning based on the estimated user's emotion. The warning unit can, for example, estimate the user's emotion and adjust the length of the warning based on the estimated user's emotion. For example, if the user is nervous, the warning unit can display a short and to-the-point warning. Furthermore, if the user is relaxed, the warning unit can display a longer warning with detailed explanations. For example, if the user is relaxed, the warning unit can display a longer warning with detailed explanations. Furthermore, if the user is in a hurry, the warning unit can display a concise and highly visible warning. For example, if the user is in a hurry, the warning unit can display a concise and highly visible warning. This allows the user to receive an appropriate amount of information by adjusting the length of the warning based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the warning unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the length of the warning.

[0090] The warning unit, when displaying a warning, can determine the priority of the warning based on the time the message was sent. For example, when displaying a warning, the warning unit can determine the priority of the warning based on the time the message was sent. For example, the warning unit can prioritize displaying a warning for a recently sent message. The warning unit can also prioritize displaying a warning for a message sent during a specific time period. For example, the warning unit can prioritize displaying a warning for a message sent during a specific time period. The warning unit can also prioritize displaying a warning for a message that the user will view during a specific time period. For example, the warning unit can prioritize displaying a warning for a message that the user will view during a specific time period. In this way, by determining the priority of the warning based on the time the message was sent, important warnings can be displayed quickly. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the time the message was sent into the generation AI, and the generation AI can determine the priority of the warning.

[0091] The warning unit can adjust the order of warnings based on the relevance of messages when displaying a warning. The warning unit can adjust the order of warnings based on the relevance of messages when displaying a warning, for example. For example, the warning unit can prioritize displaying warnings for messages from people with whom the user frequently communicates. The warning unit can also prioritize displaying warnings for messages related to a specific topic that the user is interested in. For example, the warning unit can prioritize displaying warnings for messages related to a specific topic that the user is interested in. The warning unit can also prioritize displaying warnings for related messages based on characteristics of messages that the user has previously determined to be important. For example, the warning unit can prioritize displaying warnings for related messages based on characteristics of messages that the user has previously determined to be important. In this way, by adjusting the order of warnings based on the relevance of messages, it is possible to prioritize displaying highly relevant warnings. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can input the relevance of messages into the generation AI, and the generation AI can adjust the order of warnings.

[0092] The warning unit may adjust the use of technical terminology in the warning according to the user's level of expertise when displaying the warning. For example, when displaying the warning, the warning unit may adjust the use of technical terminology in the warning according to the user's level of expertise. For example, if the user has technical expertise, the warning unit may display the warning using detailed technical terminology. Furthermore, if the user does not have technical expertise, the warning unit may display the warning using concise and easy-to-understand language. For example, if the user does not have technical expertise, the warning unit may display the warning using concise and easy-to-understand language. Furthermore, the warning unit may adjust the use of appropriate technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a warning that is easy for the user to understand. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the warning unit may input the user's level of expertise into the generation AI, which may adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and warning unit described above 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 the computer 36 of the smart device 14 and collects emails and SNS messages received by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected messages using a generation AI to determine the possibility of fraud or crime. The warning unit is realized, for example, by the control unit 46A of the smart device 14 and displays a warning for messages that may be fraudulent. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and warning unit, described above, 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 the computer 36 of the smart glasses 214 and collects emails and SNS messages received by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected messages using a generation AI to determine the possibility of fraud or crime. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays a warning for messages that may be fraudulent. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and warning unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and collects emails and SNS messages received by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected messages using a generation AI to determine the possibility of fraud or crime. The warning unit is realized, for example, by the control unit 46A of the headset type terminal 314 and displays a warning for messages that may be fraudulent. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and warning unit described above 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 the computer 36 of the robot 414 and collects emails and SNS messages received by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected messages using a generation AI to determine the possibility of fraud or crime. The warning unit is realized, for example, by the control unit 46A of the robot 414 and displays a warning for messages that may be fraudulent.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The collection unit may also collect data from other applications on the user's device. For example, the collection unit may collect data from messaging apps and calendar apps used by the user to provide information for determining the possibility of fraud or crime. The collection unit may also collect the user's browser history and search history and use this data for analysis. Furthermore, the collection unit may collect the user's location information and device sensor information to provide data for more accurately determining the possibility of fraud or crime. This allows the collection unit to collect information from various data sources on the user's device and make a more comprehensive determination of the possibility of fraud or crime.

[0095] The analysis unit can learn a user's past behavioral patterns and predict the possibility of fraud or crime. For example, the analysis unit can learn what messages a user has received in the past and how the user responded, and display a warning if a similar pattern is observed. The analysis unit can also analyze trends in messages a user receives during a specific time period and prioritize the detection of messages during that time period that are likely to be fraudulent. Furthermore, the analysis unit can analyze the behavioral patterns of other users in the user's social network and detect potentially fraudulent messages early on. This allows the analysis unit to utilize a user's past behavioral patterns to more accurately predict the possibility of fraud or crime.

[0096] The warning unit can estimate the user's emotions and adjust the warning display method based on the estimated user's emotions. For example, if the user is nervous, the warning unit can display a simple, highly visible warning. If the user is relaxed, the warning unit can also display a warning including detailed information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning that focuses on the main points. In this way, by adjusting the warning display method according to the user's emotions, it is possible to provide a warning that is appropriate for the user.

[0097] The warning unit can learn the user's past reactions to warnings and optimize the warning display method. For example, the warning unit can learn the characteristics of warnings that the user has ignored in the past and prevent similar warnings from being displayed. The warning unit can also learn the characteristics of warnings to which the user has responded in the past and display similar warnings with priority. Furthermore, the warning unit can adjust the warning display method based on the user's past feedback. This can improve the accuracy of warnings by referring to the user's past reactions to warnings.

[0098] The collection unit can estimate the user's emotions and determine the priority of messages to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit postpones messages of low importance. Also, if the user is relaxed, the collection unit can collect all messages equally. Furthermore, if the user is in a hurry, the collection unit can preferentially collect messages of high importance. In this way, by determining the priority of messages to be collected according to the user's emotions, important messages can be preferentially collected.

[0099] The analysis unit can evaluate not only the content of the message but also the trustworthiness of the sender. For example, the analysis unit analyzes the sender's past message history and prioritizes detecting messages from senders with low trustworthiness. The analysis unit can also evaluate the sender's reputation within a social network and warn of messages from senders with low trustworthiness. Furthermore, the analysis unit can evaluate the trustworthiness of the sender's email address or IP address and detect messages from senders with a high likelihood of fraud. This allows the analysis unit to more accurately determine the possibility of fraud or crime by evaluating the sender's trustworthiness.

[0100] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated user's emotions. For example, if the user is nervous, the warning unit can display a short, to-the-point warning. If the user is relaxed, the warning unit can display a longer warning with detailed explanations. Furthermore, if the user is in a hurry, the warning unit can display a concise, highly visible warning. In this way, by adjusting the length of the warning according to the user's emotions, it is possible to provide the user with an appropriate amount of information.

[0101] The collection unit can prioritize collecting highly relevant messages in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting messages related to that area. Also, when the user is traveling, the collection unit can prioritize collecting messages related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting local news and information. In this way, by prioritized collection of highly relevant messages in consideration of the user's geographical location information, it is possible to provide useful information to the user.

[0102] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0103] The warning unit can adjust the level of detail of the warning based on the risk level of the message. For example, the warning unit can display a detailed warning for a high-risk message to clarify the risk. The warning unit can also display a moderate level of detail for a medium-risk message. Furthermore, the warning unit can display a concise warning for a low-risk message. In this way, by adjusting the level of detail of the warning based on the risk level of the message, it is possible to provide an appropriate warning to the user.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects emails received by the user and SNS messages viewed by the user. For example, the collection unit can collect the text of received emails and the content of posts viewed on SNS. The collection unit can also use generation AI to collect detailed information about the content of messages. Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. For example, the analysis unit analyzes the content of the messages and determines the possibility of fraud or crime. The generation AI can detect specific keywords and patterns based on the content of the messages and determine the possibility of fraud. For example, it can detect messages containing keywords such as "fraud" or "crime" and determine the possibility of fraud. The generation AI can also analyze the context and content of messages to detect fraud patterns. Step 3: The warning unit displays a warning for messages that are determined by the analysis unit to be potentially fraudulent or criminal. For example, the warning unit can display a warning for potentially fraudulent messages by using a pop-up notification or highlighting the email. The warning unit can also have measures in place to deal with cases where a user ignores the warning. For example, the warning unit can have a function to notify the user again if the user ignores the warning, or a function to suspend the account.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 messages; an analysis unit that analyzes the messages collected by the collection unit and determines the risk of fraud or crime; a warning unit that displays a warning for messages that are determined by the analysis unit to be at risk of fraud or crime. A system characterized by:

2. The collecting unit Collecting the content of incoming emails or social media messages 2. The system of claim 1.

3. The analysis unit Analyze the content of messages to determine whether they are fraudulent or criminal.

2. The system of claim 1.

4. The warning unit Display warnings for potentially fraudulent messages 2. The system of claim 1.

5. The warning unit Display alerts as pop-up notifications or email highlighting 2. The system of claim 1.

6. The warning unit Provide specific measures in case users ignore the warning 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust message collection timing based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past message browsing history and select the appropriate collection method 2. The system of claim 1.

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