system

Abstract

The system according to this embodiment aims to monitor telephone conversations, messages, and browser operations, and to detect and warn about criminal activities such as fraud and illegal part-time jobs. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, and a warning unit. The monitoring unit monitors the content of phone calls, messages, and browser operations. The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work. The warning unit issues a warning to the user based on the results evaluated by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been sufficiently done to monitor telephone call contents, messages, and browser operations to detect criminal acts such as fraud and unreported part-time jobs, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor telephone call contents, messages, and browser operations, detect criminal acts such as fraud and unreported part-time jobs, and issue warnings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an analysis unit, and a warning unit. The monitoring unit monitors the content of phone calls, messages, and browser operations. The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work. The warning unit issues a warning to the user based on the results evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the content of phone calls, messages, and browser operations, and can detect and issue warnings about criminal activities such as fraud and illegal part-time jobs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The fraud and illegal job detection tool according to an embodiment of the present invention is a system that resides on a smartphone or personal computer and detects and warns of criminal activities such as fraud and illegal jobs by monitoring the content of phone calls, messages, and browser operations. This system uses a generative AI to evaluate the possibility of crime from the content of phone calls and message exchanges, and issues a warning to the user if the level of crime is high. For example, in the fraud and illegal job detection tool, a monitoring unit that resides on the smartphone or personal computer monitors the content of phone calls, messages, and browser operations in real time. Next, the monitored data is sent to the generative AI, which analyzes the content of phone calls and message exchanges and evaluates the possibility of criminal activities such as fraud and illegal jobs. If the generative AI determines that there is a high possibility of criminal activity, the warning unit issues a warning to the user. For example, if the content of a phone call includes keywords such as "high reward" or "earn money in a short period of time," the generative AI analyzes this and determines that there is a high possibility of fraud or illegal jobs. Similarly, if the message exchange includes similar keywords, the generative AI analyzes this and evaluates the possibility of criminal activity. This system can prevent fraud and illegal part-time jobs using smartphones. Users can reduce the risk of becoming involved in criminal activity and use their smartphones with peace of mind. Furthermore, companies providing communication services can fulfill their role in society by contributing to crime prevention. Thus, fraud and illegal part-time job detection tools can prevent fraud and illegal part-time jobs using smartphones and personal computers.

[0029] The fraud and illegal work detection tool according to the embodiment comprises a monitoring unit, an analysis unit, and a warning unit. The monitoring unit monitors the content of phone calls, messages, and browser operations. The monitoring unit resides, for example, on a smartphone or personal computer and monitors the content of phone calls in real time. The monitoring unit can also monitor message exchanges in real time. The monitoring unit can also monitor browser operations in real time. The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal work. The analysis unit can, for example, analyze the content of phone calls and evaluate the possibility of fraud or illegal work. The analysis unit can also, for example, analyze message exchanges and evaluate the possibility of fraud or illegal work. The analysis unit can also, for example, analyze browser operations and evaluate the possibility of fraud or illegal work. The warning unit issues a warning to the user based on the results evaluated by the analysis unit. The warning unit issues a warning to the user, for example, if the content of a phone call contains keywords such as "high reward" or "earn money in a short time." The warning unit can also issue a warning to the user if, for example, similar keywords are found in message exchanges. The warning unit can also issue a warning to the user if, for example, similar keywords are found in browser operations. As a result, the fraud and illegal job detection tool according to the embodiment can monitor phone call content, messages, and browser operations to detect and warn about criminal activities such as fraud and illegal jobs.

[0030] The monitoring unit monitors phone call content, messages, and browser operations. For example, the monitoring unit resides on smartphones and personal computers, monitoring phone call content in real time. Specifically, a dedicated application installed on the smartphone or personal computer runs in the background, automatically starting recording when a call begins. The recorded call content is immediately sent to the monitoring unit's database and stored for access by the analysis unit. The monitoring unit can also monitor message exchanges in real time. It hooks notifications from messaging applications, captures the content of incoming and outgoing messages, and sends it to the analysis unit. This allows for immediate analysis of message content, detecting signs of fraud or illegal work. The monitoring unit can also monitor browser operations in real time. Operating as a browser extension, it captures the URLs and page content of websites accessed by the user and sends them to the analysis unit. This allows for immediate analysis and warnings if a user accesses a fraudulent site or illegal work recruitment page. The monitoring unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and warning units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data monitored by the monitoring unit to assess the possibility of criminal activity such as fraud or illegal work. For example, the analysis unit analyzes the content of phone calls to assess the possibility of fraud or illegal work. Specifically, it uses speech recognition technology to convert the audio data of phone calls into text, and then analyzes the text data. The analysis uses natural language processing (NLP) technology to detect specific keywords and phrases and assess the possibility of fraud or illegal work. For example, if keywords such as "high pay," "earn money in a short time," and "confidentiality guaranteed" are included, it is judged that there is a high possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work by analyzing message exchanges. It analyzes the text content of messages to detect specific keywords and phrases. Furthermore, it analyzes the sender and recipient information of messages and compares it with past databases to assess the possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work by analyzing browser operations. It analyzes the URLs and page content of websites accessed by users to assess whether they are fraudulent sites or illegal work recruitment pages. This includes website domain information, past access history, and text analysis of page content. This allows the analysis unit to quickly and accurately analyze monitored data and assess the risk of fraud and illegal work. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past data on fraud and illegal work, it can predict risk fluctuations at specific times and situations and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The warning unit issues warnings to users based on the results evaluated by the analysis unit. For example, the warning unit will issue a warning to the user if the call content contains keywords such as "high reward" or "earn money in a short time." Specifically, it can issue warnings in real time during a call, displaying a warning message on the smartphone screen or issuing an audio warning. The warning unit can also issue warnings to users if similar keywords are included in message exchanges, for example. It will use the notification function of the messaging application to display a warning message and draw the user's attention. The warning unit can also issue warnings to users if similar keywords are included in browser operations, for example. It will use the browser's pop-up notification function to display a warning message and draw the user's attention. This allows the warning unit to issue warnings to users quickly and effectively through various devices and applications. Furthermore, the warning unit can customize the content and timing of warnings, enabling flexible responses according to the user's needs and circumstances. For example, it can adjust the frequency of warnings at specific times or locations, or optimize the content of warnings based on the user's past behavior history. Furthermore, the warning unit records the history of warnings, allowing for later reference and enabling analysis of user behavior patterns and risk tendencies. This allows the warning unit to provide appropriate warnings to users, minimizing the risk of fraud and illegal part-time jobs.

[0033] The monitoring unit can monitor telephone conversations, messages, and browser operations in real time. For example, the monitoring unit can monitor telephone conversations in real time. The monitoring unit can also monitor message exchanges in real time. The monitoring unit can also monitor browser operations in real time. This allows for immediate detection of criminal activity by monitoring telephone conversations, messages, and browser operations in real time. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can monitor telephone conversations in real time, input the conversation content into a generative AI, and the generative AI can analyze the conversation content.

[0034] The analysis unit can analyze call content and message exchanges to assess the possibility of criminal activity such as fraud or illegal work. For example, the analysis unit can analyze call content to assess the possibility of fraud or illegal work. The analysis unit can also analyze message exchanges to assess the possibility of fraud or illegal work. The analysis unit can also analyze browser operations to assess the possibility of fraud or illegal work. In this way, the possibility of criminal activity can be assessed by analyzing call content and message exchanges. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input call content into a generating AI, and the generating AI can analyze the call content.

[0035] The warning unit can issue warnings to the user based on the results evaluated by the generative AI. For example, the warning unit will issue a warning to the user if the content of a call contains keywords such as "high reward" or "earn money in a short time." The warning unit can also issue a warning to the user if similar keywords are included in message exchanges, for example. The warning unit can also issue a warning to the user if similar keywords are included in browser operations, for example. In this way, criminal activity can be prevented by issuing warnings to the user based on the results evaluated by the generative AI. Some or all of the above processing in the warning unit may be performed using, for example, the generative AI, or without the generative AI. For example, the warning unit can issue a warning using a generative AI model that takes the results evaluated by the generative AI as input and outputs a warning.

[0036] The analysis unit can determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can also determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can also determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. This allows the analysis unit to determine that if the call content contains certain keywords, there is a high probability that it is a scam or an illegal job. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the call content into a generating AI, which will analyze the call content and determine the possibility of it being a scam or an illegal job.

[0037] The analysis unit can assess the possibility of criminal activity if similar keywords are included in the message exchange. For example, if the message exchange contains keywords such as "high reward" or "earn money in a short time," the analysis unit can assess the possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work if the message exchange contains keywords such as "high reward" or "earn money in a short time." The analysis unit can also assess the possibility of fraud or illegal work if the message exchange contains keywords such as "high reward" or "earn money in a short time." This allows the analysis unit to assess the possibility of criminal activity if specific keywords are included in the message exchange. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the message exchange into a generative AI, which can analyze the message exchange and assess the possibility of fraud or illegal work.

[0038] The monitoring unit can improve the accuracy of monitoring by referring to the user's past call and message history during monitoring. For example, the monitoring unit can analyze the user's past call history and detect specific patterns to improve monitoring accuracy. The monitoring unit can also refer to the user's message history and identify frequently exchanged keywords to improve monitoring accuracy. For example, the monitoring unit can comprehensively analyze the user's past call and message history and detect abnormal patterns to improve monitoring accuracy. In this way, the accuracy of monitoring can be improved by referring to the user's past call and message history. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's past call and message history into a generative AI, which can analyze this data to improve monitoring accuracy.

[0039] The monitoring unit can dynamically change the target of monitoring based on the user's current activity during monitoring. For example, if the user is on a call, the monitoring unit will prioritize monitoring the call content. For example, if the user is sending a message, the monitoring unit can also prioritize monitoring the message content. For example, if the user is using a browser, the monitoring unit can also prioritize monitoring the browser operation. This enables efficient monitoring by dynamically changing the target of monitoring based on the user's current activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's current activity status into a generative AI, which can analyze the activity status and dynamically change the target of monitoring.

[0040] The monitoring unit can prioritize monitoring highly relevant data by considering the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit can prioritize monitoring call content related to that region. For example, if the user is traveling, the monitoring unit can prioritize monitoring message content related to the travel destination. For example, if the user is in a specific location, the monitoring unit can prioritize monitoring browser operations related to that location. This allows for the priority monitoring of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can analyze the location information and prioritize monitoring of highly relevant data.

[0041] The monitoring unit can analyze a user's social media activity and monitor relevant data during monitoring. For example, if a user uses a specific keyword on social media, the monitoring unit can prioritize monitoring call content related to that keyword. For example, if a user is a member of a specific group on social media, the monitoring unit can prioritize monitoring message content related to that group. For example, if a user is participating in a specific event on social media, the monitoring unit can prioritize monitoring browser activity related to that event. This allows for efficient monitoring of relevant data by analyzing a user's social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input user social media activity data into a generative AI, which can then analyze and monitor relevant data.

[0042] The analysis unit can improve the accuracy of its analysis by considering the context of the call content and messages during the analysis. For example, the analysis unit can analyze the context of the call content and accurately understand the meaning of specific keywords. The analysis unit can also analyze the context of messages and accurately understand the meaning of specific phrases. The analysis unit can also integrate the context of the call content and messages and detect abnormal patterns. This improves the accuracy of the analysis by considering the context of the call content and messages. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input contextual data of the call content and messages into a generative AI, which can then analyze it to improve the accuracy of the analysis.

[0043] The analysis unit can correct the analysis results by referring to the user's past behavior patterns during the analysis. For example, the analysis unit can refer to the user's past call patterns to identify abnormal call content. The analysis unit can also refer to the user's past message patterns to identify abnormal message content. The analysis unit can also refer to the user's past behavior patterns in an integrated manner to identify abnormal behavior. This allows the analysis results to be corrected by referring to the user's past behavior patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI, which can then analyze it and correct the analysis results.

[0044] The analysis unit can improve the accuracy of its analysis by considering call content and message sender information during the analysis. For example, the analysis unit can analyze the sender information of call content and evaluate the reliability of a specific sender. The analysis unit can also analyze the sender information of messages and evaluate the reliability of a specific sender. The analysis unit can also comprehensively analyze call content and message sender information to identify abnormal senders. This improves the accuracy of the analysis by considering call content and message sender information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input call content and message sender information into a generating AI, which will then analyze it to improve the accuracy of the analysis.

[0045] The analysis unit can supplement the analysis results by referring to relevant external databases during the analysis process. For example, the analysis unit can refer to an external database to evaluate the reliability of call content. The analysis unit can also refer to an external database to evaluate the reliability of message content. The analysis unit can also refer to an external database to evaluate the reliability of browser operations. In this way, the analysis results can be supplemented by referring to relevant external databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input an external database into a generating AI, which can then analyze it and supplement the analysis results.

[0046] The warning unit can customize the content of a warning by referring to the user's past warning history when an warning is issued. For example, the warning unit can refer to the user's past warning history and customize similar warning content. The warning unit can also analyze the user's past warning history and adjust the frequency of warnings. For example, the warning unit can comprehensively refer to the user's past warning history and suggest the most suitable warning content. This allows the content of warnings to be customized by referring to the user's past warning history. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the user's past warning history data into a generating AI, which can then analyze it and customize the content of the warning.

[0047] The warning unit can adjust the timing of the warning based on the user's current situation. For example, if the user is on a call, the warning unit will issue a warning after the call ends. For example, if the user is sending a message, the warning unit may issue a warning after the message has been sent. For example, if the user is using a browser, the warning unit may issue a warning after the browser operation is finished. By adjusting the timing of the warning based on the user's current situation, the warning can be issued at a more appropriate time. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the warning unit can input the user's current situation data into a generating AI, which can analyze it and adjust the timing of the warning.

[0048] The warning unit can customize the content of a warning by taking into account the user's geographical location information. For example, if the user is in a specific region, the warning unit can customize the content of the warning to be relevant to that region. For example, if the user is traveling, the warning unit can also customize the content of the warning to be relevant to the travel destination. For example, if the user is in a specific location, the warning unit can also customize the content of the warning to be relevant to that location. In this way, the content of the warning can be customized by taking into account the user's geographical location information. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's geographical location data into a generative AI, which can then analyze it and customize the content of the warning.

[0049] The warning unit can analyze the user's social media activity and supplement the content of the warning when a warning is issued. For example, if the user uses a specific keyword on social media, the warning unit can supplement the warning content related to that keyword. For example, if the user is a member of a specific group on social media, the warning unit can also supplement the warning content related to that group. For example, if the user is participating in a specific event on social media, the warning unit can also supplement the warning content related to that event. In this way, the content of the warning can be supplemented by analyzing the user's social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's social media activity data into a generative AI, which can then analyze it and supplement the content of the warning.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The monitoring unit can improve the accuracy of monitoring by referring to the user's past behavior patterns. For example, it can analyze the user's past call history and detect specific patterns to improve monitoring accuracy. It can also refer to the user's message history and identify frequently exchanged keywords to improve monitoring accuracy. Furthermore, it can comprehensively analyze the user's past call history and message history to detect abnormal patterns and improve monitoring accuracy. In this way, monitoring accuracy can be improved by referring to the user's past behavior patterns. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0052] The analysis unit can dynamically change the target of analysis based on the user's current activity. For example, if the user is on a call, the analysis can prioritize the call content. If the user is sending a message, the analysis can prioritize the message content. If the user is using a browser, the analysis can prioritize the browser operation. This allows for efficient analysis by dynamically changing the target of analysis based on the user's current activity. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0053] The analysis unit can correct the analysis results by referring to the user's past behavior patterns. For example, it can identify abnormal call content by referring to the user's past call patterns. It can also identify abnormal message content by referring to the user's past message patterns. It can also identify abnormal behavior by comprehensively referring to the user's past behavior patterns. In this way, the analysis results can be corrected by referring to the user's past behavior patterns. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0054] The monitoring unit can dynamically change the target of monitoring based on the user's current activity. For example, if the user is on a call, the content of the call can be prioritized for monitoring. If the user is sending a message, the content of the message can be prioritized for monitoring. If the user is using a browser, the content of the browser can be prioritized for monitoring. This enables efficient monitoring by dynamically changing the target of monitoring based on the user's current activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0055] The warning unit can customize the content of warnings by referring to the user's past warning history. For example, it can refer to the user's past warning history and customize similar warning content. It can also analyze the user's past warning history and adjust the frequency of warnings. It can also comprehensively refer to the user's past warning history and suggest the most suitable warning content. This allows the content of warnings to be customized by referring to the user's past warning history. Some or all of the above processing in the warning unit may be performed using generative AI, or it may be performed without using generative AI.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The monitoring unit monitors phone call content, messages, and browser operations. The monitoring unit resides on, for example, a smartphone or personal computer and monitors phone call content in real time. The monitoring unit can also monitor message exchanges and browser operations in real time. Step 2: The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work. The analysis unit can analyze call content, message exchanges, and browser operations to evaluate the possibility of fraud or illegal part-time work. Step 3: The warning unit issues a warning to the user based on the results evaluated by the analysis unit. The warning unit issues a warning to the user if keywords such as "high rewards" or "earn money in a short time" are found in the call content, message exchanges, or browser operations.

[0058] (Example of form 2) The fraud and illegal job detection tool according to an embodiment of the present invention is a system that resides on a smartphone or personal computer and detects and warns of criminal activities such as fraud and illegal jobs by monitoring the content of phone calls, messages, and browser operations. This system uses a generative AI to evaluate the possibility of crime from the content of phone calls and message exchanges, and issues a warning to the user if the level of crime is high. For example, in the fraud and illegal job detection tool, a monitoring unit that resides on the smartphone or personal computer monitors the content of phone calls, messages, and browser operations in real time. Next, the monitored data is sent to the generative AI, which analyzes the content of phone calls and message exchanges and evaluates the possibility of criminal activities such as fraud and illegal jobs. If the generative AI determines that there is a high possibility of criminal activity, the warning unit issues a warning to the user. For example, if the content of a phone call includes keywords such as "high reward" or "earn money in a short period of time," the generative AI analyzes this and determines that there is a high possibility of fraud or illegal jobs. Similarly, if the message exchange includes similar keywords, the generative AI analyzes this and evaluates the possibility of criminal activity. This system can prevent fraud and illegal part-time jobs using smartphones. Users can reduce the risk of becoming involved in criminal activity and use their smartphones with peace of mind. Furthermore, companies providing communication services can fulfill their role in society by contributing to crime prevention. Thus, fraud and illegal part-time job detection tools can prevent fraud and illegal part-time jobs using smartphones and personal computers.

[0059] The fraud and illegal work detection tool according to the embodiment comprises a monitoring unit, an analysis unit, and a warning unit. The monitoring unit monitors the content of phone calls, messages, and browser operations. The monitoring unit resides, for example, on a smartphone or personal computer and monitors the content of phone calls in real time. The monitoring unit can also monitor message exchanges in real time. The monitoring unit can also monitor browser operations in real time. The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal work. The analysis unit can, for example, analyze the content of phone calls and evaluate the possibility of fraud or illegal work. The analysis unit can also, for example, analyze message exchanges and evaluate the possibility of fraud or illegal work. The analysis unit can also, for example, analyze browser operations and evaluate the possibility of fraud or illegal work. The warning unit issues a warning to the user based on the results evaluated by the analysis unit. The warning unit issues a warning to the user, for example, if the content of a phone call contains keywords such as "high reward" or "earn money in a short time." The warning unit can also issue a warning to the user if, for example, similar keywords are found in message exchanges. The warning unit can also issue a warning to the user if, for example, similar keywords are found in browser operations. As a result, the fraud and illegal job detection tool according to the embodiment can monitor phone call content, messages, and browser operations to detect and warn about criminal activities such as fraud and illegal jobs.

[0060] The monitoring unit monitors phone call content, messages, and browser operations. For example, the monitoring unit resides on smartphones and personal computers, monitoring phone call content in real time. Specifically, a dedicated application installed on the smartphone or personal computer runs in the background, automatically starting recording when a call begins. The recorded call content is immediately sent to the monitoring unit's database and stored for access by the analysis unit. The monitoring unit can also monitor message exchanges in real time. It hooks notifications from messaging applications, captures the content of incoming and outgoing messages, and sends it to the analysis unit. This allows for immediate analysis of message content, detecting signs of fraud or illegal work. The monitoring unit can also monitor browser operations in real time. Operating as a browser extension, it captures the URLs and page content of websites accessed by the user and sends them to the analysis unit. This allows for immediate analysis and warnings if a user accesses a fraudulent site or illegal work recruitment page. The monitoring unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and warning units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes data monitored by the monitoring unit to assess the possibility of criminal activity such as fraud or illegal work. For example, the analysis unit analyzes the content of phone calls to assess the possibility of fraud or illegal work. Specifically, it uses speech recognition technology to convert the audio data of phone calls into text, and then analyzes the text data. The analysis uses natural language processing (NLP) technology to detect specific keywords and phrases and assess the possibility of fraud or illegal work. For example, if keywords such as "high pay," "earn money in a short time," and "confidentiality guaranteed" are included, it is judged that there is a high possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work by analyzing message exchanges. It analyzes the text content of messages to detect specific keywords and phrases. Furthermore, it analyzes the sender and recipient information of messages and compares it with past databases to assess the possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work by analyzing browser operations. It analyzes the URLs and page content of websites accessed by users to assess whether they are fraudulent sites or illegal work recruitment pages. This includes website domain information, past access history, and text analysis of page content. This allows the analysis unit to quickly and accurately analyze monitored data and assess the risk of fraud and illegal work. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past data on fraud and illegal work, it can predict risk fluctuations at specific times and situations and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0062] The warning unit issues warnings to users based on the results evaluated by the analysis unit. For example, the warning unit will issue a warning to the user if the call content contains keywords such as "high reward" or "earn money in a short time." Specifically, it can issue warnings in real time during a call, displaying a warning message on the smartphone screen or issuing an audio warning. The warning unit can also issue warnings to users if similar keywords are included in message exchanges, for example. It will use the notification function of the messaging application to display a warning message and draw the user's attention. The warning unit can also issue warnings to users if similar keywords are included in browser operations, for example. It will use the browser's pop-up notification function to display a warning message and draw the user's attention. This allows the warning unit to issue warnings to users quickly and effectively through various devices and applications. Furthermore, the warning unit can customize the content and timing of warnings, enabling flexible responses according to the user's needs and circumstances. For example, it can adjust the frequency of warnings at specific times or locations, or optimize the content of warnings based on the user's past behavior history. Furthermore, the warning unit records the history of warnings, allowing for later reference and enabling analysis of user behavior patterns and risk tendencies. This allows the warning unit to provide appropriate warnings to users, minimizing the risk of fraud and illegal part-time jobs.

[0063] The monitoring unit can monitor telephone conversations, messages, and browser operations in real time. For example, the monitoring unit can monitor telephone conversations in real time. The monitoring unit can also monitor message exchanges in real time. The monitoring unit can also monitor browser operations in real time. This allows for immediate detection of criminal activity by monitoring telephone conversations, messages, and browser operations in real time. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can monitor telephone conversations in real time, input the conversation content into a generative AI, and the generative AI can analyze the conversation content.

[0064] The analysis unit can analyze call content and message exchanges to assess the possibility of criminal activity such as fraud or illegal work. For example, the analysis unit can analyze call content to assess the possibility of fraud or illegal work. The analysis unit can also analyze message exchanges to assess the possibility of fraud or illegal work. The analysis unit can also analyze browser operations to assess the possibility of fraud or illegal work. In this way, the possibility of criminal activity can be assessed by analyzing call content and message exchanges. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input call content into a generating AI, and the generating AI can analyze the call content.

[0065] The warning unit can issue warnings to the user based on the results evaluated by the generative AI. For example, the warning unit will issue a warning to the user if the content of a call contains keywords such as "high reward" or "earn money in a short time." The warning unit can also issue a warning to the user if similar keywords are included in message exchanges, for example. The warning unit can also issue a warning to the user if similar keywords are included in browser operations, for example. In this way, criminal activity can be prevented by issuing warnings to the user based on the results evaluated by the generative AI. Some or all of the above processing in the warning unit may be performed using, for example, the generative AI, or without the generative AI. For example, the warning unit can issue a warning using a generative AI model that takes the results evaluated by the generative AI as input and outputs a warning.

[0066] The analysis unit can determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can also determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. The analysis unit can also determine that if the call content contains keywords such as "high pay" or "earn money quickly," there is a high probability that it is a scam or an illegal job. This allows the analysis unit to determine that if the call content contains certain keywords, there is a high probability that it is a scam or an illegal job. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the call content into a generating AI, which will analyze the call content and determine the possibility of it being a scam or an illegal job.

[0067] The analysis unit can assess the possibility of criminal activity if similar keywords are included in the message exchange. For example, if the message exchange contains keywords such as "high reward" or "earn money in a short time," the analysis unit can assess the possibility of fraud or illegal work. The analysis unit can also assess the possibility of fraud or illegal work if the message exchange contains keywords such as "high reward" or "earn money in a short time." The analysis unit can also assess the possibility of fraud or illegal work if the message exchange contains keywords such as "high reward" or "earn money in a short time." This allows the analysis unit to assess the possibility of criminal activity if specific keywords are included in the message exchange. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the message exchange into a generative AI, which can analyze the message exchange and assess the possibility of fraud or illegal work.

[0068] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring intensity and collect more detailed data. For example, if the user is relaxed, the monitoring unit can also lower the monitoring intensity and collect only the minimum necessary data. For example, if the user is in a hurry, the monitoring unit can adjust the monitoring intensity to a moderate level and efficiently collect data. This allows for more appropriate monitoring by adjusting the monitoring intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the monitoring intensity.

[0069] The monitoring unit can improve the accuracy of monitoring by referring to the user's past call and message history during monitoring. For example, the monitoring unit can analyze the user's past call history and detect specific patterns to improve monitoring accuracy. The monitoring unit can also refer to the user's message history and identify frequently exchanged keywords to improve monitoring accuracy. For example, the monitoring unit can comprehensively analyze the user's past call and message history and detect abnormal patterns to improve monitoring accuracy. In this way, the accuracy of monitoring can be improved by referring to the user's past call and message history. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's past call and message history into a generative AI, which can analyze this data to improve monitoring accuracy.

[0070] The monitoring unit can dynamically change the target of monitoring based on the user's current activity during monitoring. For example, if the user is on a call, the monitoring unit will prioritize monitoring the call content. For example, if the user is sending a message, the monitoring unit can also prioritize monitoring the message content. For example, if the user is using a browser, the monitoring unit can also prioritize monitoring the browser operation. This enables efficient monitoring by dynamically changing the target of monitoring based on the user's current activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's current activity status into a generative AI, which can analyze the activity status and dynamically change the target of monitoring.

[0071] The monitoring unit can estimate the user's emotions and determine the priority of data to monitor based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit may prioritize monitoring call content. For example, if the user is relaxed, the monitoring unit may prioritize monitoring message content. For example, if the user is excited, the monitoring unit may prioritize monitoring browser operations. This allows for prioritizing the monitoring of more important data by determining the priority of data to monitor according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of data to monitor.

[0072] The monitoring unit can prioritize monitoring highly relevant data by considering the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit can prioritize monitoring call content related to that region. For example, if the user is traveling, the monitoring unit can prioritize monitoring message content related to the travel destination. For example, if the user is in a specific location, the monitoring unit can prioritize monitoring browser operations related to that location. This allows for the priority monitoring of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can analyze the location information and prioritize monitoring of highly relevant data.

[0073] The monitoring unit can analyze a user's social media activity and monitor relevant data during monitoring. For example, if a user uses a specific keyword on social media, the monitoring unit can prioritize monitoring call content related to that keyword. For example, if a user is a member of a specific group on social media, the monitoring unit can prioritize monitoring message content related to that group. For example, if a user is participating in a specific event on social media, the monitoring unit can prioritize monitoring browser activity related to that event. This allows for efficient monitoring of relevant data by analyzing a user's social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input user social media activity data into a generative AI, which can then analyze and monitor relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can enhance the analysis algorithm and perform a more detailed analysis. For example, if the user is relaxed, the analysis unit can simplify the analysis algorithm and perform only the minimum necessary analysis. For example, if the user is in a hurry, the analysis unit can adjust the analysis algorithm to a moderate level to perform an efficient analysis. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the analysis algorithm.

[0075] The analysis unit can improve the accuracy of its analysis by considering the context of the call content and messages during the analysis. For example, the analysis unit can analyze the context of the call content and accurately understand the meaning of specific keywords. The analysis unit can also analyze the context of messages and accurately understand the meaning of specific phrases. The analysis unit can also integrate the context of the call content and messages and detect abnormal patterns. This improves the accuracy of the analysis by considering the context of the call content and messages. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input contextual data of the call content and messages into a generative AI, which can then analyze it to improve the accuracy of the analysis.

[0076] The analysis unit can correct the analysis results by referring to the user's past behavior patterns during the analysis. For example, the analysis unit can refer to the user's past call patterns to identify abnormal call content. The analysis unit can also refer to the user's past message patterns to identify abnormal message content. The analysis unit can also refer to the user's past behavior patterns in an integrated manner to identify abnormal behavior. This allows the analysis results to be corrected by referring to the user's past behavior patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI, which can then analyze it and correct the analysis results.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit may prioritize analyzing the call content. For example, if the user is relaxed, the analysis unit may prioritize analyzing the message content. For example, if the user is excited, the analysis unit may prioritize analyzing browser operations. This allows for prioritizing the analysis of more important data by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of analysis.

[0078] The analysis unit can improve the accuracy of its analysis by considering call content and message sender information during the analysis. For example, the analysis unit can analyze the sender information of call content and evaluate the reliability of a specific sender. The analysis unit can also analyze the sender information of messages and evaluate the reliability of a specific sender. The analysis unit can also comprehensively analyze call content and message sender information to identify abnormal senders. This improves the accuracy of the analysis by considering call content and message sender information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input call content and message sender information into a generating AI, which will then analyze it to improve the accuracy of the analysis.

[0079] The analysis unit can supplement the analysis results by referring to relevant external databases during the analysis process. For example, the analysis unit can refer to an external database to evaluate the reliability of call content. The analysis unit can also refer to an external database to evaluate the reliability of message content. The analysis unit can also refer to an external database to evaluate the reliability of browser operations. In this way, the analysis results can be supplemented by referring to relevant external databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input an external database into a generating AI, which can then analyze it and supplement the analysis results.

[0080] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is tense, the warning unit may issue a warning in a calm tone. If the user is relaxed, the warning unit may also issue a warning in a bright tone. If the user is in a hurry, the warning unit may also issue a quick and concise warning. By adjusting the way the warning is expressed according to the user's emotions, more effective warnings can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the warning unit may be performed using a generative AI, or not using a generative AI. For example, the warning unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way the warning is expressed.

[0081] The warning unit can customize the content of a warning by referring to the user's past warning history when an warning is issued. For example, the warning unit can refer to the user's past warning history and customize similar warning content. The warning unit can also analyze the user's past warning history and adjust the frequency of warnings. For example, the warning unit can comprehensively refer to the user's past warning history and suggest the most suitable warning content. This allows the content of warnings to be customized by referring to the user's past warning history. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the user's past warning history data into a generating AI, which can then analyze it and customize the content of the warning.

[0082] The warning unit can adjust the timing of the warning based on the user's current situation. For example, if the user is on a call, the warning unit will issue a warning after the call ends. For example, if the user is sending a message, the warning unit may issue a warning after the message has been sent. For example, if the user is using a browser, the warning unit may issue a warning after the browser operation is finished. By adjusting the timing of the warning based on the user's current situation, the warning can be issued at a more appropriate time. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the warning unit can input the user's current situation data into a generating AI, which can analyze it and adjust the timing of the warning.

[0083] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is feeling anxious, the alert unit will give a higher priority to the alert. For example, if the user is relaxed, the alert unit may give a lower priority to the alert. For example, if the user is excited, the alert unit may adjust the priority of the alert to a medium level. This allows for prioritizing more important alerts by determining the priority of alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the alert unit may be performed using a generative AI, or not using a generative AI. For example, the alert unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of alerts.

[0084] The warning unit can customize the content of a warning by taking into account the user's geographical location information. For example, if the user is in a specific region, the warning unit can customize the content of the warning to be relevant to that region. For example, if the user is traveling, the warning unit can also customize the content of the warning to be relevant to the travel destination. For example, if the user is in a specific location, the warning unit can also customize the content of the warning to be relevant to that location. In this way, the content of the warning can be customized by taking into account the user's geographical location information. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's geographical location data into a generative AI, which can then analyze it and customize the content of the warning.

[0085] The warning unit can analyze the user's social media activity and supplement the content of the warning when a warning is issued. For example, if the user uses a specific keyword on social media, the warning unit can supplement the warning content related to that keyword. For example, if the user is a member of a specific group on social media, the warning unit can also supplement the warning content related to that group. For example, if the user is participating in a specific event on social media, the warning unit can also supplement the warning content related to that event. In this way, the content of the warning can be supplemented by analyzing the user's social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's social media activity data into a generative AI, which can then analyze it and supplement the content of the warning.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis and perform a more detailed analysis. If the user is relaxed, the analysis unit can lower the accuracy of the analysis and perform only the minimum necessary analysis. If the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to a moderate level and perform the analysis efficiently. In this way, by adjusting the accuracy of the analysis according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using generative AI or not using generative AI.

[0088] The monitoring unit can improve the accuracy of monitoring by referring to the user's past behavior patterns. For example, it can analyze the user's past call history and detect specific patterns to improve monitoring accuracy. It can also refer to the user's message history and identify frequently exchanged keywords to improve monitoring accuracy. Furthermore, it can comprehensively analyze the user's past call history and message history to detect abnormal patterns and improve monitoring accuracy. In this way, monitoring accuracy can be improved by referring to the user's past behavior patterns. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0089] The warning unit can estimate the user's emotions and customize the content of the warning based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can issue a warning in a calm tone. If the user is relaxed, the warning unit can issue a warning in a cheerful tone. If the user is in a hurry, the warning unit can issue a quick and concise warning. This allows for more effective warnings by customizing the content of the warning according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the warning unit may be performed using generative AI or not.

[0090] The analysis unit can dynamically change the target of analysis based on the user's current activity. For example, if the user is on a call, the analysis can prioritize the call content. If the user is sending a message, the analysis can prioritize the message content. If the user is using a browser, the analysis can prioritize the browser operation. This allows for efficient analysis by dynamically changing the target of analysis based on the user's current activity. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0091] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is stressed, the monitoring intensity can be increased to collect more detailed data. If the user is relaxed, the monitoring intensity can be lowered to collect only the minimum necessary data. If the user is in a hurry, the monitoring intensity can be adjusted to a moderate level to efficiently collect data. This allows for more appropriate monitoring by adjusting the monitoring intensity according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using generative AI or not.

[0092] The analysis unit can correct the analysis results by referring to the user's past behavior patterns. For example, it can identify abnormal call content by referring to the user's past call patterns. It can also identify abnormal message content by referring to the user's past message patterns. It can also identify abnormal behavior by comprehensively referring to the user's past behavior patterns. In this way, the analysis results can be corrected by referring to the user's past behavior patterns. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0093] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated emotions. For example, if the user is feeling anxious, the warning priority can be increased. If the user is relaxed, the warning priority can be decreased. If the user is excited, the warning priority can be adjusted to a medium level. This allows for prioritizing more important warnings by determining the priority of warnings according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the warning unit may be performed using generative AI or not.

[0094] The monitoring unit can dynamically change the target of monitoring based on the user's current activity. For example, if the user is on a call, the content of the call can be prioritized for monitoring. If the user is sending a message, the content of the message can be prioritized for monitoring. If the user is using a browser, the content of the browser can be prioritized for monitoring. This enables efficient monitoring by dynamically changing the target of monitoring based on the user's current activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis of call content can be prioritized. If the user is relaxed, the analysis of message content can be prioritized. If the user is excited, the analysis of browser operations can be prioritized. In this way, by determining the priority of analysis according to the user's emotions, more important data can be analyzed preferentially. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using generative AI or not using generative AI.

[0096] The warning unit can customize the content of warnings by referring to the user's past warning history. For example, it can refer to the user's past warning history and customize similar warning content. It can also analyze the user's past warning history and adjust the frequency of warnings. It can also comprehensively refer to the user's past warning history and suggest the most suitable warning content. This allows the content of warnings to be customized by referring to the user's past warning history. Some or all of the above processing in the warning unit may be performed using generative AI, or it may be performed without using generative AI.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The monitoring unit monitors phone call content, messages, and browser operations. The monitoring unit resides on, for example, a smartphone or personal computer and monitors phone call content in real time. The monitoring unit can also monitor message exchanges and browser operations in real time. Step 2: The analysis unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work. The analysis unit can analyze call content, message exchanges, and browser operations to evaluate the possibility of fraud or illegal part-time work. Step 3: The warning unit issues a warning to the user based on the results evaluated by the analysis unit. The warning unit issues a warning to the user if keywords such as "high rewards" or "earn money in a short time" are found in the call content, message exchanges, or browser operations.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the monitoring unit, analysis unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors telephone conversations, messages, and browser operations in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data monitored by the monitoring unit to evaluate the possibility of criminal activity such as fraud or illegal part-time work. The warning unit is implemented by the control unit 46A of the smart device 14 and issues a warning to the user based on the results evaluated by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the monitoring unit, analysis unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors telephone conversations, messages, and browser operations in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data monitored by the monitoring unit to evaluate the possibility of criminal activity such as fraud or illegal part-time work. The warning unit is implemented by the control unit 46A of the smart glasses 214 and issues a warning to the user based on the results evaluated by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the monitoring unit, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors telephone conversation content, messages, and browser operations in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data monitored by the monitoring unit to evaluate the possibility of criminal activity such as fraud or illegal part-time work. The warning unit is implemented by the control unit 46A of the headset terminal 314 and issues a warning to the user based on the results evaluated by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the monitoring unit, analysis unit, and warning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors telephone conversations, messages, and browser operations in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data monitored by the monitoring unit to evaluate the possibility of criminal activity such as fraud or illegal part-time work. The warning unit is implemented by the control unit 46A of the robot 414 and issues a warning to the user based on the results evaluated by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A monitoring unit that monitors phone call content, messages, and browser operations, The aforementioned monitoring unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work, The system includes a warning unit that issues a warning to the user based on the results evaluated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Monitor phone conversations, messages, and browser activity in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes call content and message exchanges to assess the possibility of criminal activity such as fraud or illegal part-time work. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is The system issues warnings to users based on evaluations conducted by a generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, If the call content includes keywords such as "high pay" or "earn money quickly," it is highly likely to be a scam or an illegal part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, In message exchanges, if similar keywords are included, the possibility of criminal activity is assessed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, During monitoring, the system improves monitoring accuracy by referencing the user's past call and message history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, the monitoring target is dynamically changed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates user sentiment and prioritizes data to monitor based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and monitors relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the context of the call content and messages. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis results are corrected by referring to the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the content of the call and the sender information of the message. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the results are supplemented by referencing relevant external databases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, the content of the warning is customized by referring to the user's past warning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When a warning is issued, the timing of the warning will be adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, the content of the warning will be customized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When issuing a warning, the system analyzes the user's social media activity to supplement the warning content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors phone call content, messages, and browser operations, The aforementioned monitoring unit analyzes the data monitored by the monitoring unit and evaluates the possibility of criminal activity such as fraud or illegal part-time work, The system includes a warning unit that issues a warning to the user based on the results evaluated by the analysis unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Monitor phone conversations, messages, and browser activity in real time. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes call content and message exchanges to assess the possibility of criminal activity such as fraud or illegal part-time work. The system according to feature 1.

4. The aforementioned warning unit is The system issues warnings to users based on evaluations generated by AI. The system according to feature 1.

5. The aforementioned analysis unit, If the call content contains certain keywords, it is highly likely to be a scam or an illegal job. The system according to feature 1.

6. The aforementioned analysis unit, In message exchanges, if similar keywords are included, the possibility of criminal activity is assessed. The system according to feature 1.

7. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system according to feature 1.

8. The aforementioned monitoring unit, During monitoring, the system improves monitoring accuracy by referencing the user's past call and message history. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, the monitoring target is dynamically changed based on the user's current activity status. The system according to feature 1.

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