System

Abstract

An object of a system according to an embodiment is to analyze behavior data of a user and issue a warning when there is a possibility of a crime.SOLUTION: A system includes a user action monitoring part, an analysis part, and a warning part. The user behavior monitoring unit collects user behavior data. The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. The warning unit issues a warning when the analysis unit determines that there is a possibility of a crime.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately predicted or prevented crimes based on user behavior data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user behavior data and issue a warning when a crime is suspected. [Means for solving the problem]

[0006] The system according to the embodiment includes a user behavior monitoring unit, an analysis unit, and a warning unit. The user behavior monitoring unit collects user behavior data. The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. The warning unit issues a warning when the analysis unit determines that a crime may have occurred. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user behavior data and issue a warning if there is a possibility of a crime. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A crime prevention system according to an embodiment of the present invention automatically monitors user behavior, analyzes it using a generation AI, and issues a warning if a crime is suspected. This allows the crime prevention system to reduce the risk of users becoming involved in crime.

[0029] A crime prevention system according to an embodiment includes a user behavior monitoring unit, an analysis unit, and a warning unit. The user behavior monitoring unit collects user behavior data. For example, the user behavior monitoring unit collects location information. The user behavior monitoring unit can also collect browsing history. The user behavior monitoring unit can also collect app usage history. For example, the user behavior monitoring unit collects location information using GPS. The browsing history is collected by collecting web browser history data. The app usage history is collected by collecting smartphone app usage data. The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using natural language processing technology. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit uses a support vector machine as the machine learning algorithm. The natural language processing technology is used to analyze text data. The data mining technology is used to extract useful information from large amounts of data. The warning unit issues a warning when the analysis unit determines that a crime is likely to have occurred. For example, the warning unit displays a pop-up message. The warning unit can also send an email. The warning unit can also send an app notification. For example, the warning unit displays a pop-up message saying, "This action may be fraudulent." The email is sent to the user's registered email address. The app notification is sent using the smartphone's notification function. This allows the crime prevention system according to the embodiment to reduce the risk of users becoming involved in crime. For example, if users recognize the risk of fraud and take appropriate measures, they can prevent themselves from becoming victims of fraud. Furthermore, if users understand the methods used by phishing scams and avoid clicking on suspicious links, they can prevent the leakage of personal information.

[0030] In addition to user behavior data, the analysis unit can analyze a user's past behavioral history and patterns to enable more accurate crime prediction. For example, the analysis unit stores a user's past search history and postings in a database, and the generation AI analyzes this data. For example, a user who has frequently searched for fraud-related keywords in the past may be determined to be at high risk of fraud. The analysis unit can also analyze a user's past location information to understand behavioral patterns in specific locations. For example, if a user frequently repeats behavior in a specific location, it may determine that the crime risk in that location is high. The analysis unit can also analyze a user's past app usage history to understand the usage patterns of specific apps. For example, if the use of a specific app suddenly increases, it may determine that the crime risk related to that app is high. This allows for more accurate crime prediction by analyzing a user's past behavioral history and patterns.

[0031] The analysis unit can analyze user behavior data in real time and instantly detect abnormal behavior patterns. For example, the analysis unit can analyze a user's search keywords and postings in real time to detect abnormal behavior patterns. For example, the analysis unit can issue a warning if a user who normally searches for technology-related keywords suddenly searches for keywords related to illegal downloading. The analysis unit can also analyze a user's location information in real time to detect abnormal movement patterns. For example, the analysis unit can issue a warning if a user who normally travels within a specific area suddenly travels far away. The analysis unit can also analyze a user's app usage history in real time to detect abnormal app usage patterns. For example, the analysis unit can issue a warning if a user who normally uses a specific app suddenly starts frequently using a new app. This allows for rapid warnings by analyzing user behavior data in real time and instantly detecting abnormal behavior patterns.

[0032] The user behavior monitoring unit analyzes not only user behavior data but also social media posts and comments, enabling crime prediction from a wider range of data. The user behavior monitoring unit, for example, analyzes a user's social media posts and comments and issues a warning if a crime is predicted. For example, a warning is issued if there is an increase in posts related to fraud. The user behavior monitoring unit can also analyze a user's blog comments and forum posts and issue a warning if a crime is predicted. For example, a warning is issued if there is an increase in comments related to illegal downloading. The user behavior monitoring unit can also analyze a user's chat messages and issue a warning if a crime is predicted. For example, a warning is issued if there is an increase in chat messages related to fraud. In this way, by including social media posts and comments in the analysis, crime prediction can be performed from a wider range of data.

[0033] When analyzing user behavior data, the user behavior monitoring unit can integrate behavior across different devices to understand overall behavior patterns. The user behavior monitoring unit integrates behavior data across different devices, such as a user's smartphone, tablet, and PC, to analyze overall behavior patterns. For example, it integrates and analyzes search history on a smartphone and content posted on a PC. The user behavior monitoring unit can also integrate data from a user's wearable device to analyze overall behavior patterns. For example, it integrates and analyzes heart rate data from a smartwatch and location information from a smartphone. The user behavior monitoring unit can also integrate data from a user's smart home device to analyze overall behavior patterns. For example, it integrates and analyzes voice data from a smart speaker and app usage history on a smartphone. This allows for an understanding of overall behavior patterns by integrating behavior across different devices, enabling more accurate analysis.

[0034] When warning of a possible crime, the warning unit can issue an individually customized warning by taking into account the user's past behavioral history and patterns. The warning unit issues an individually customized warning, for example, based on the user's past behavioral history. For example, a user who has previously searched for fraud-related keywords is warned of a high risk of fraud. The warning unit can also issue an individually customized warning based on the user's past location information. For example, if a user frequently repeats behavior in a specific location, the warning unit warns that there is a high risk of crime in that location. The warning unit can also issue an individually customized warning based on the user's past app usage history. For example, if there is a sudden increase in the use of a specific app, the warning unit warns that there is a high risk of crime related to that app. In this way, by taking into account the user's past behavioral history and patterns and issuing an individually customized warning, more effective attention can be drawn.

[0035] The warning unit links specific cases and news articles to the warning message, making the user aware of realistic risks. The warning unit, for example, links specific cases and news articles to the warning message, making the user aware of realistic risks. For example, it links cases of fraud victims and news articles. The warning unit can also link past crime cases to the warning message, making the user aware of realistic risks. For example, it links cases of phishing scams. The warning unit can also link news articles related to the warning message, making the user aware of realistic risks. For example, it links news articles about credit card fraud. In this way, by linking specific cases and news articles, the user can be made aware of realistic risks.

[0036] When warning of a possible crime, the warning unit issues a voice warning using a voice assistant, thereby urging attention not only visually but also aurally. For example, when warning of a possible crime, the warning unit issues a voice warning using a voice assistant. For example, the warning unit warns of a risk of fraud through a smart speaker. The warning unit can also issue a voice warning using a smartphone voice assistant. For example, the warning unit warns of a risk of fraud using Google Assistant. The warning unit can also issue a voice warning using an in-car voice assistant. For example, the warning unit warns of a risk of fraud while driving. In this way, by issuing a voice warning using a voice assistant, attention can be urged not only visually but also aurally.

[0037] The warning unit can display warning messages in multiple languages ​​to accommodate users in different language regions. The warning unit can, for example, display warning messages in multiple languages ​​to accommodate users in different language regions. For example, the warning unit issues warnings in multiple languages, such as English, Japanese, and Chinese. The warning unit can also display warning messages according to a user's language setting. For example, if the user's language setting is French, the warning unit issues a warning in French. The warning unit can also display warning messages according to a user's regional setting. For example, if the user's regional setting is Germany, the warning unit issues a warning in German. In this way, by displaying warning messages in multiple languages, it is possible to accommodate users in different language regions.

[0038] The analysis unit can refer to a past crime database and present specific crime cases based on the user's behavior. The analysis unit, for example, refers to a past crime database and presents specific crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of phishing fraud. The analysis unit can also refer to a past crime database and present similar crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of credit card fraud. The analysis unit can also refer to a past crime database and present related crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of fraud. In this way, by referring to a past crime database, specific crime cases can be presented based on the user's behavior.

[0039] In addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take. For example, in addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take. For example, the analysis unit can provide specific measures to avoid falling victim to phishing scams. The analysis unit can also provide step-by-step action steps that the user should take. For example, the analysis unit can provide sequential steps to reduce the risk of fraud. The analysis unit can also visually provide the action steps that the user should take. For example, the analysis unit can provide infographics to show the action steps. In this way, in addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take, allowing the user to take appropriate measures.

[0040] The analysis unit visually shows possible crime cases using videos or animations, allowing the user to understand more intuitively. The analysis unit, for example, visually shows possible crime cases using videos or animations, allowing the user to understand more intuitively. For example, it shows phishing fraud methods using animations. The analysis unit can also show simulations of crime cases using videos. For example, it shows simulations of credit card fraud using videos. The analysis unit can also show reenactments of crime cases using animations. For example, it reenacts fraud methods using animations. In this way, by visually showing possible crime cases using videos or animations, the user can understand more intuitively.

[0041] In addition to presenting crime cases, the analysis unit can provide an interactive function for simulating what would happen if the user encountered a similar case. For example, in addition to presenting crime cases, the analysis unit can provide an interactive function for simulating what would happen if the user encountered a similar case. For example, the analysis unit can simulate how to deal with a phishing scam. The analysis unit can also provide a simulation for the user to deal with crime cases. For example, the analysis unit can provide a simulation for dealing with credit card fraud. The analysis unit can also provide interactive training for the user to deal with crime cases. For example, the analysis unit can provide training for dealing with fraud methods. In this way, by providing an interactive function for simulating what would happen if the user encountered a similar case in addition to presenting crime cases, the user can be prepared to deal with actual situations.

[0042] In addition to presenting the amount of damage, the analysis unit can refer to past cases of damage and show specific details of the damage and subsequent countermeasures. For example, in addition to presenting the amount of damage, the analysis unit can refer to past cases of damage and show specific details of the damage and subsequent countermeasures. For example, it can show a case of credit card fraud and subsequent countermeasures. The analysis unit can also show specific countermeasures in stages if the user becomes a victim. For example, it can show contact information and procedures to follow if the user becomes a victim of fraud. The analysis unit can also visually show cases of damage. For example, it can show cases of damage and countermeasures using infographics. In this way, in addition to presenting the amount of damage, past cases of damage can be referred to and specific details of the damage and subsequent countermeasures can be shown, allowing the user to understand specific risks and countermeasures.

[0043] The analysis unit can perform an individually customized risk assessment when presenting the amount of damage, taking into account the user's economic situation and asset information. For example, the analysis unit performs an individually customized risk assessment when presenting the amount of damage, taking into account the user's economic situation and asset information. For example, a more detailed risk assessment is performed for users with high assets. The analysis unit can also perform a risk assessment based on the user's income information. For example, it assesses that a user with a high income may suffer a large amount of damage. The analysis unit can also perform a risk assessment based on the user's debt information. For example, it assesses that a user with a large amount of debt may suffer a large amount of damage. In this way, by performing an individually customized risk assessment taking into account the user's economic situation and asset information, it is possible to provide a more appropriate risk assessment to the user.

[0044] In addition to presenting the amount of damage, the analysis unit can also suggest insurance products and security services to prevent damage before it occurs. For example, in addition to presenting the amount of damage, the analysis unit can also suggest insurance products and security services to prevent damage before it occurs. For example, the analysis unit can suggest insurance products against credit card fraud. The analysis unit can also suggest cybersecurity services. For example, the analysis unit can suggest security software to prevent phishing scams. The analysis unit can also suggest home security services. For example, the analysis unit can suggest a home security system to prevent burglary. In this way, by presenting the amount of damage and suggesting insurance products and security services to prevent damage before it occurs, the user can take specific measures.

[0045] The analysis unit visually presents the amount of damage using infographics or charts, allowing the user to understand more intuitively. The analysis unit visually presents the amount of damage using infographics or charts, for example, allowing the user to understand more intuitively. For example, the amount of damage caused by credit card fraud is presented in a chart. The analysis unit can also present the amount of damage in a graph. For example, the amount of damage caused by phishing fraud is presented in a bar graph. The analysis unit can also present the amount of damage in an infographic. For example, the amount of damage caused by fraud is presented in an infographic. In this way, by visually presenting the amount of damage using infographics or charts, the user can understand more intuitively.

[0046] In addition to presenting a workaround method, the analysis unit can collect feedback when the user actually takes the action and evaluate the effectiveness of the workaround method. For example, in addition to presenting a workaround method, the analysis unit can collect feedback when the user actually takes the action and evaluate the effectiveness of the workaround method. For example, the analysis unit collects feedback after the user has put a workaround method for phishing scams into practice. The analysis unit can also evaluate the effectiveness of the workaround method based on the results of the user's actions. For example, it can evaluate whether the risk of fraud has been reduced. The analysis unit can also collect user opinions and evaluate the effectiveness of the workaround method. For example, it can ask the user whether the workaround method was effective. In this way, in addition to presenting a workaround method, feedback when the user actually takes the action can be collected and the effectiveness of the workaround method can be evaluated, enabling more effective countermeasures.

[0047] When presenting avoidance methods, the analysis unit can provide individually customized advice by taking into account the user's past behavioral history and patterns. For example, when presenting avoidance methods, the analysis unit can provide individually customized advice by taking into account the user's past behavioral history and patterns. For example, the analysis unit can provide customized ways to avoid fraud to a user who has previously searched for fraud-related keywords. The analysis unit can also provide advice based on the user's past location information. For example, if a user frequently repeats behavior in a specific location, the analysis unit can provide advice to reduce the crime risk in that location. The analysis unit can also provide advice based on the user's past app usage history. For example, if the use of a specific app suddenly increases, the analysis unit can provide advice to reduce the crime risk associated with that app. This allows for more effective countermeasures by providing individually customized advice by taking into account the user's past behavioral history and patterns.

[0048] The analysis unit can provide an interactive function that simulates a user actually taking action in addition to presenting the evasion methods. The analysis unit, for example, provides an interactive function that simulates a user actually taking action in addition to presenting the evasion methods. For example, the analysis unit simulates a method for avoiding phishing scams. The analysis unit can also provide a simulation for a user to deal with a crime case. For example, the analysis unit provides a simulation for dealing with credit card fraud. The analysis unit can also provide interactive training for a user to deal with a crime case. For example, the analysis unit provides training for dealing with fraud methods. In this way, by providing an interactive function that simulates a user actually taking action in addition to presenting the evasion methods, the user can prepare to put specific countermeasures into practice.

[0049] The analysis unit visually shows the evasion methods using videos or animations, allowing the user to understand them more intuitively. The analysis unit, for example, visually shows the evasion methods using videos or animations, allowing the user to understand them more intuitively. For example, it shows how to avoid phishing scams using animations. The analysis unit can also show a simulation of the evasion methods using videos. For example, it shows how to avoid credit card fraud using videos. The analysis unit can also show a reproduction of the evasion methods using animations. For example, it reproduces evasion methods for dealing with fraudulent techniques using animations. In this way, by visually showing the evasion methods using videos or animations, the user can understand them more intuitively.

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

[0051] In addition to user behavior data, the user behavior monitoring unit can also collect user health data, which can be used to predict crimes. For example, it can collect a user's sleep patterns and determine whether abnormal sleep patterns may increase the risk of crime. It can also collect a user's dietary records and issue a warning if their nutritional status is deteriorating. It can also collect a user's exercise data and determine whether a lack of exercise can cause stress and anxiety, increasing the risk of crime. This allows for more accurate crime prediction by analyzing the user's health data.

[0052] In addition to user behavioral data, the analysis unit can also analyze a user's purchasing history to predict crime risks. For example, if a user frequently purchases expensive items, it can determine that there is a high risk of fraud. The analysis unit can also analyze a user's purchasing patterns and issue a warning if abnormal purchasing behavior is observed. For example, a warning can be issued if a user who normally purchases everyday items suddenly purchases an expensive electronic device. The analysis unit can also determine, based on the user's purchasing history, that the purchase of a specific product may increase the risk of crime. This makes it possible to predict crime with greater accuracy by analyzing a user's purchasing history.

[0053] In addition to user behavioral data, the analysis unit can also analyze the user's hobbies and interests to predict crime risks. For example, if a user frequently visits websites related to a particular hobby, the analysis unit can determine that there is a high risk of fraud related to that hobby. The analysis unit can also predict crime risks associated with specific events or locations based on the user's interests. For example, if a user plans to attend a particular music festival, the analysis unit can warn the user of the crime risks associated with that event. The analysis unit can also predict the risk of purchasing a specific product based on the user's hobbies and interests. This allows for more accurate crime prediction by analyzing a user's hobbies and interests.

[0054] In addition to user behavior data, the user behavior monitoring unit can analyze the user's communication history to predict criminal risks. For example, if a user frequently contacts a specific phone number, it can determine that there is a high risk of fraud related to that phone number. The user behavior monitoring unit can also analyze the user's email history to predict criminal risks related to a specific email address. For example, if a user frequently receives emails from a specific email address, it can determine that there is a high risk of fraud related to that email address. The user behavior monitoring unit can also analyze the user's messaging app history to predict criminal risks related to specific contacts. This allows for more accurate criminal prediction by analyzing the user's communication history.

[0055] In addition to user behavioral data, the analysis unit can also analyze a user's learning history to predict criminal risk. For example, if a user is taking a specific online course, it can determine that there is a high risk of fraud related to that course. The analysis unit can also analyze a user's learning patterns and issue a warning if abnormal learning behavior is observed. For example, a warning can be issued if a user who normally takes technology-related courses suddenly starts taking finance-related courses. The analysis unit can also determine, based on the user's learning history, that certain learning content may increase the risk of criminal activity. This allows for more accurate crime prediction by analyzing a user's learning history.

[0056] In addition to user behavior data, the analysis unit can also analyze the user's travel history to predict crime risks. For example, if a user frequently travels to a particular country or region, it can determine that there is a high crime risk associated with that region. The analysis unit can also analyze the user's travel patterns and issue a warning if abnormal travel behavior is observed. For example, a warning can be issued if a user who normally travels domestically suddenly plans to travel abroad. The analysis unit can also determine, based on the user's travel history, that certain travel destinations may increase the risk of crime. This allows for more accurate crime prediction by analyzing the user's travel history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user behavior monitoring unit collects user behavior data, such as location information, browsing history, and app usage history. Location information is collected using GPS, browsing history is collected by collecting web browser history data, and app usage history is collected by collecting smartphone app usage data. Step 2: The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. For example, the data is analyzed using machine learning algorithms, natural language processing technology, and data mining technology. A support vector machine is used as the machine learning algorithm, natural language processing technology is used to analyze text data, and data mining technology is used to extract useful information from large amounts of data. Step 3: The warning unit issues a warning if the analysis unit determines that a crime may have occurred. For example, it may display a pop-up message, send an email, or send an app notification. A pop-up message saying "This behavior may be fraudulent" is displayed, an email is sent to the user's registered email address, and an app notification is sent using the smartphone's notification function.

[0059] (Example 2) A crime prevention system according to an embodiment of the present invention automatically monitors user behavior, analyzes it using a generation AI, and issues a warning if a crime is suspected. This allows the crime prevention system to reduce the risk of users becoming involved in crime.

[0060] A crime prevention system according to an embodiment includes a user behavior monitoring unit, an analysis unit, and a warning unit. The user behavior monitoring unit collects user behavior data. For example, the user behavior monitoring unit collects location information. The user behavior monitoring unit can also collect browsing history. The user behavior monitoring unit can also collect app usage history. For example, the user behavior monitoring unit collects location information using GPS. The browsing history is collected by collecting web browser history data. The app usage history is collected by collecting smartphone app usage data. The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using natural language processing technology. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit uses a support vector machine as the machine learning algorithm. The natural language processing technology is used to analyze text data. The data mining technology is used to extract useful information from large amounts of data. The warning unit issues a warning when the analysis unit determines that a crime is likely to have occurred. For example, the warning unit displays a pop-up message. The warning unit can also send an email. The warning unit can also send an app notification. For example, the warning unit displays a pop-up message saying, "This action may be fraudulent." The email is sent to the user's registered email address. The app notification is sent using the smartphone's notification function. This allows the crime prevention system according to the embodiment to reduce the risk of users becoming involved in crime. For example, if users recognize the risk of fraud and take appropriate measures, they can prevent themselves from becoming victims of fraud. Furthermore, if users understand the methods used by phishing scams and avoid clicking on suspicious links, they can prevent the leakage of personal information.

[0061] In addition to user behavior data, the analysis unit can analyze a user's past behavioral history and patterns to enable more accurate crime prediction. For example, the analysis unit stores a user's past search history and postings in a database, and the generation AI analyzes this data. For example, a user who has frequently searched for fraud-related keywords in the past may be determined to be at high risk of fraud. The analysis unit can also analyze a user's past location information to understand behavioral patterns in specific locations. For example, if a user frequently repeats behavior in a specific location, it may determine that the crime risk in that location is high. The analysis unit can also analyze a user's past app usage history to understand the usage patterns of specific apps. For example, if the use of a specific app suddenly increases, it may determine that the crime risk related to that app is high. This allows for more accurate crime prediction by analyzing a user's past behavioral history and patterns.

[0062] The analysis unit can analyze user behavior data in real time and instantly detect abnormal behavior patterns. For example, the analysis unit can analyze a user's search keywords and postings in real time to detect abnormal behavior patterns. For example, the analysis unit can issue a warning if a user who normally searches for technology-related keywords suddenly searches for keywords related to illegal downloading. The analysis unit can also analyze a user's location information in real time to detect abnormal movement patterns. For example, the analysis unit can issue a warning if a user who normally travels within a specific area suddenly travels far away. The analysis unit can also analyze a user's app usage history in real time to detect abnormal app usage patterns. For example, the analysis unit can issue a warning if a user who normally uses a specific app suddenly starts frequently using a new app. This allows for rapid warnings by analyzing user behavior data in real time and instantly detecting abnormal behavior patterns.

[0063] The analysis unit can use the emotion estimation function to analyze the user's emotional state and urge them to be especially careful when stress or anxiety is increasing. The analysis unit, for example, estimates the user's emotion from the content of the user's posts and search keywords, and issues a warning when stress or anxiety is increasing. For example, it issues a warning when there is an increase in posts with strong negative emotions. The analysis unit can also analyze the user's voice data to estimate the user's emotional state. For example, it can analyze the tone and speed of the voice and issue a warning when stress or anxiety is increasing. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) to estimate the emotional state. For example, it can analyze heart rate fluctuations and issue a warning when stress or anxiety is increasing. In this way, by analyzing the user's emotional state and urging them to be especially careful when stress or anxiety is increasing, it is possible to provide appropriate warnings according to the user's psychological state.

[0064] The user behavior monitoring unit analyzes not only user behavior data but also social media posts and comments, enabling crime prediction from a wider range of data. The user behavior monitoring unit, for example, analyzes a user's social media posts and comments and issues a warning if a crime is predicted. For example, a warning is issued if there is an increase in posts related to fraud. The user behavior monitoring unit can also analyze a user's blog comments and forum posts and issue a warning if a crime is predicted. For example, a warning is issued if there is an increase in comments related to illegal downloading. The user behavior monitoring unit can also analyze a user's chat messages and issue a warning if a crime is predicted. For example, a warning is issued if there is an increase in chat messages related to fraud. In this way, by including social media posts and comments in the analysis, crime prediction can be performed from a wider range of data.

[0065] When analyzing user behavior data, the user behavior monitoring unit can integrate behavior across different devices to understand overall behavior patterns. The user behavior monitoring unit integrates behavior data across different devices, such as a user's smartphone, tablet, and PC, to analyze overall behavior patterns. For example, it integrates and analyzes search history on a smartphone and content posted on a PC. The user behavior monitoring unit can also integrate data from a user's wearable device to analyze overall behavior patterns. For example, it integrates and analyzes heart rate data from a smartwatch and location information from a smartphone. The user behavior monitoring unit can also integrate data from a user's smart home device to analyze overall behavior patterns. For example, it integrates and analyzes voice data from a smart speaker and app usage history on a smartphone. This allows for an understanding of overall behavior patterns by integrating behavior across different devices, enabling more accurate analysis.

[0066] The analysis unit can use the emotion estimation function to analyze the emotion a user has when performing a specific action in real time, and intensify the warning if negative emotions are increasing. The analysis unit, for example, analyzes the user's behavioral data and infers the emotion a user has when performing a specific action in real time. For example, the warning is intensified if negative emotions are increasing. The analysis unit can also analyze the user's facial expression data and infer the emotion a user has when performing a specific action in real time. For example, the analysis unit can analyze changes in facial expressions and intensify the warning if negative emotions are increasing. The analysis unit can also analyze the user's voice data and infer the emotion a user has when performing a specific action in real time. For example, the analysis unit can analyze the tone and speed of the voice and intensify the warning if negative emotions are increasing. This makes it possible to more effectively alert users by analyzing the emotion a user has when performing a specific action in real time, and intensifying the warning if negative emotions are increasing.

[0067] When warning of a possible crime, the warning unit can issue an individually customized warning by taking into account the user's past behavioral history and patterns. The warning unit issues an individually customized warning, for example, based on the user's past behavioral history. For example, a user who has previously searched for fraud-related keywords is warned of a high risk of fraud. The warning unit can also issue an individually customized warning based on the user's past location information. For example, if a user frequently repeats behavior in a specific location, the warning unit warns that there is a high risk of crime in that location. The warning unit can also issue an individually customized warning based on the user's past app usage history. For example, if there is a sudden increase in the use of a specific app, the warning unit warns that there is a high risk of crime related to that app. In this way, by taking into account the user's past behavioral history and patterns and issuing an individually customized warning, more effective attention can be drawn.

[0068] The warning unit links specific cases and news articles to the warning message, making the user aware of realistic risks. The warning unit, for example, links specific cases and news articles to the warning message, making the user aware of realistic risks. For example, it links cases of fraud victims and news articles. The warning unit can also link past crime cases to the warning message, making the user aware of realistic risks. For example, it links cases of phishing scams. The warning unit can also link news articles related to the warning message, making the user aware of realistic risks. For example, it links news articles about credit card fraud. In this way, by linking specific cases and news articles, the user can be made aware of realistic risks.

[0069] The warning unit uses the emotion estimation function to adjust the warning message in a tone that corresponds to the user's emotional state, thereby more effectively attracting attention. The warning unit, for example, uses the emotion estimation function to adjust the warning message in a tone that corresponds to the user's emotional state. For example, if the user is feeling stressed, the warning unit issues a warning in a gentle tone. Furthermore, if the user is feeling anxious, the warning unit can also issue a warning in a tone that gives a sense of security. Furthermore, if the user is feeling angry, the warning unit can also issue a warning in a calm tone. In this way, by adjusting the warning message in a tone that corresponds to the user's emotional state, it is possible to more effectively attract attention.

[0070] When warning of a possible crime, the warning unit issues a voice warning using a voice assistant, thereby urging attention not only visually but also aurally. For example, when warning of a possible crime, the warning unit issues a voice warning using a voice assistant. For example, the warning unit warns of a risk of fraud through a smart speaker. The warning unit can also issue a voice warning using a smartphone voice assistant. For example, the warning unit warns of a risk of fraud using Google Assistant. The warning unit can also issue a voice warning using an in-car voice assistant. For example, the warning unit warns of a risk of fraud while driving. In this way, by issuing a voice warning using a voice assistant, attention can be urged not only visually but also aurally.

[0071] The warning unit can display warning messages in multiple languages ​​to accommodate users in different language regions. The warning unit can, for example, display warning messages in multiple languages ​​to accommodate users in different language regions. For example, the warning unit issues warnings in multiple languages, such as English, Japanese, and Chinese. The warning unit can also display warning messages according to a user's language setting. For example, if the user's language setting is French, the warning unit issues a warning in French. The warning unit can also display warning messages according to a user's regional setting. For example, if the user's regional setting is Germany, the warning unit issues a warning in German. In this way, by displaying warning messages in multiple languages, it is possible to accommodate users in different language regions.

[0072] The warning unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving the warning message and evaluate the effectiveness of the warning in real time. The warning unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when receiving the warning message. For example, it analyzes facial expressions and voice when receiving the warning message. The warning unit can also collect user feedback and evaluate the effectiveness of the warning in real time. For example, it analyzes behavioral changes after receiving the warning message. The warning unit can also analyze the user's biometric data and evaluate the effectiveness of the warning in real time. For example, it analyzes heart rate fluctuations. In this way, it is possible to analyze the emotional reaction of the user when receiving the warning message and evaluate the effectiveness of the warning in real time, thereby optimizing the effectiveness of the warning.

[0073] The analysis unit can refer to a past crime database and present specific crime cases based on the user's behavior. The analysis unit, for example, refers to a past crime database and presents specific crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of phishing fraud. The analysis unit can also refer to a past crime database and present similar crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of credit card fraud. The analysis unit can also refer to a past crime database and present related crime cases based on the user's behavior. For example, specific risks are presented to the user based on past cases of fraud. In this way, by referring to a past crime database, specific crime cases can be presented based on the user's behavior.

[0074] In addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take. For example, in addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take. For example, the analysis unit can provide specific measures to avoid falling victim to phishing scams. The analysis unit can also provide step-by-step action steps that the user should take. For example, the analysis unit can provide sequential steps to reduce the risk of fraud. The analysis unit can also visually provide the action steps that the user should take. For example, the analysis unit can provide infographics to show the action steps. In this way, in addition to presenting a crime case, the analysis unit can provide detailed specific action steps that the user should take, allowing the user to take appropriate measures.

[0075] The analysis unit can use the emotion estimation function to analyze what emotions the user feels about the presented crime case and adjust the way the case is presented. For example, the analysis unit uses the emotion estimation function to analyze what emotions the user feels about the presented crime case. For example, if the user feels anxious, the case is presented in a tone that gives a sense of security. The analysis unit can also analyze the user's facial expression data to estimate emotions. For example, by analyzing changes in facial expression and if the user feels anxious, the case is presented in a tone that gives a sense of security. The analysis unit can also analyze the user's voice data to estimate emotions. For example, by analyzing the tone and speed of the voice and if the user feels anxious, the case is presented in a tone that gives a sense of security. In this way, by analyzing what emotions the user feels about the presented crime case and adjusting the way the case is presented, more effective attention can be drawn.

[0076] The analysis unit visually shows possible crime cases using videos or animations, allowing the user to understand more intuitively. The analysis unit, for example, visually shows possible crime cases using videos or animations, allowing the user to understand more intuitively. For example, it shows phishing fraud methods using animations. The analysis unit can also show simulations of crime cases using videos. For example, it shows simulations of credit card fraud using videos. The analysis unit can also show reenactments of crime cases using animations. For example, it reenacts fraud methods using animations. In this way, by visually showing possible crime cases using videos or animations, the user can understand more intuitively.

[0077] In addition to presenting crime cases, the analysis unit can provide an interactive function for simulating what would happen if the user encountered a similar case. For example, in addition to presenting crime cases, the analysis unit can provide an interactive function for simulating what would happen if the user encountered a similar case. For example, the analysis unit can simulate how to deal with a phishing scam. The analysis unit can also provide a simulation for the user to deal with crime cases. For example, the analysis unit can provide a simulation for dealing with credit card fraud. The analysis unit can also provide interactive training for the user to deal with crime cases. For example, the analysis unit can provide training for dealing with fraud methods. In this way, by providing an interactive function for simulating what would happen if the user encountered a similar case in addition to presenting crime cases, the user can be prepared to deal with actual situations.

[0078] The analysis unit can use the emotion estimation function to monitor in real time how the user feels about the presented crime cases and optimize the presentation method. For example, the analysis unit uses the emotion estimation function to monitor in real time how the user feels about the presented crime cases. For example, if the user feels anxious, the case is presented in a tone that gives a sense of security. The analysis unit can also monitor the user's facial expression data in real time and estimate their emotions. For example, by analyzing changes in facial expression in real time, if the user feels anxious, the case is presented in a tone that gives a sense of security. The analysis unit can also monitor the user's voice data in real time and estimate their emotions. For example, by analyzing the tone and speed of the voice in real time, if the user feels anxious, the case is presented in a tone that gives a sense of security. In this way, by monitoring in real time how the user feels about the presented crime cases and optimizing the presentation method, more effective warnings can be issued.

[0079] In addition to presenting the amount of damage, the analysis unit can refer to past cases of damage and show specific details of the damage and subsequent countermeasures. For example, in addition to presenting the amount of damage, the analysis unit can refer to past cases of damage and show specific details of the damage and subsequent countermeasures. For example, it can show a case of credit card fraud and subsequent countermeasures. The analysis unit can also show specific countermeasures in stages if the user becomes a victim. For example, it can show contact information and procedures to follow if the user becomes a victim of fraud. The analysis unit can also visually show cases of damage. For example, it can show cases of damage and countermeasures using infographics. In this way, in addition to presenting the amount of damage, past cases of damage can be referred to and specific details of the damage and subsequent countermeasures can be shown, allowing the user to understand specific risks and countermeasures.

[0080] The analysis unit can perform an individually customized risk assessment when presenting the amount of damage, taking into account the user's economic situation and asset information. For example, the analysis unit performs an individually customized risk assessment when presenting the amount of damage, taking into account the user's economic situation and asset information. For example, a more detailed risk assessment is performed for users with high assets. The analysis unit can also perform a risk assessment based on the user's income information. For example, it assesses that a user with a high income may suffer a large amount of damage. The analysis unit can also perform a risk assessment based on the user's debt information. For example, it assesses that a user with a large amount of debt may suffer a large amount of damage. In this way, by performing an individually customized risk assessment taking into account the user's economic situation and asset information, it is possible to provide a more appropriate risk assessment to the user.

[0081] The analysis unit can use the emotion estimation function to analyze what emotions the user feels about the presented damage amount and adjust the presentation method. The analysis unit, for example, uses the emotion estimation function to analyze what emotions the user feels about the presented damage amount. For example, if the user feels anxious, the damage amount is presented in a tone that gives a sense of relief. The analysis unit can also analyze the user's facial expression data to estimate emotions. For example, by analyzing changes in facial expression and, if the user feels anxious, the damage amount is presented in a tone that gives a sense of relief. The analysis unit can also analyze the user's voice data to estimate emotions. For example, by analyzing the tone and speed of the voice and, if the user feels anxious, the damage amount is presented in a tone that gives a sense of relief. In this way, by analyzing what emotions the user feels about the presented damage amount and adjusting the presentation method, more effective risk recognition is possible.

[0082] In addition to presenting the amount of damage, the analysis unit can also suggest insurance products and security services to prevent damage before it occurs. For example, in addition to presenting the amount of damage, the analysis unit can also suggest insurance products and security services to prevent damage before it occurs. For example, the analysis unit can suggest insurance products against credit card fraud. The analysis unit can also suggest cybersecurity services. For example, the analysis unit can suggest security software to prevent phishing scams. The analysis unit can also suggest home security services. For example, the analysis unit can suggest a home security system to prevent burglary. In this way, by presenting the amount of damage and suggesting insurance products and security services to prevent damage before it occurs, the user can take specific measures.

[0083] The analysis unit visually presents the amount of damage using infographics or charts, allowing the user to understand more intuitively. The analysis unit visually presents the amount of damage using infographics or charts, for example, allowing the user to understand more intuitively. For example, the amount of damage caused by credit card fraud is presented in a chart. The analysis unit can also present the amount of damage in a graph. For example, the amount of damage caused by phishing fraud is presented in a bar graph. The analysis unit can also present the amount of damage in an infographic. For example, the amount of damage caused by fraud is presented in an infographic. In this way, by visually presenting the amount of damage using infographics or charts, the user can understand more intuitively.

[0084] The analysis unit can use the emotion estimation function to monitor in real time how the user feels about the presented damage amount and optimize the presentation method. The analysis unit, for example, uses the emotion estimation function to monitor in real time how the user feels about the presented damage amount. For example, if the user feels anxious, the damage amount is presented in a tone that conveys a sense of relief. The analysis unit can also monitor the user's facial expression data in real time and estimate their emotions. For example, by analyzing changes in facial expression in real time, if the user feels anxious, the damage amount is presented in a tone that conveys a sense of relief. The analysis unit can also monitor the user's voice data in real time and estimate their emotions. For example, by analyzing the tone and speed of the voice in real time, if the user feels anxious, the damage amount is presented in a tone that conveys a sense of relief. In this way, by monitoring in real time how the user feels about the presented damage amount and optimizing the presentation method, more effective risk recognition is possible.

[0085] In addition to presenting a workaround method, the analysis unit can collect feedback when the user actually takes the action and evaluate the effectiveness of the workaround method. For example, in addition to presenting a workaround method, the analysis unit can collect feedback when the user actually takes the action and evaluate the effectiveness of the workaround method. For example, the analysis unit collects feedback after the user has put a workaround method for phishing scams into practice. The analysis unit can also evaluate the effectiveness of the workaround method based on the results of the user's actions. For example, it can evaluate whether the risk of fraud has been reduced. The analysis unit can also collect user opinions and evaluate the effectiveness of the workaround method. For example, it can ask the user whether the workaround method was effective. In this way, in addition to presenting a workaround method, feedback when the user actually takes the action can be collected and the effectiveness of the workaround method can be evaluated, enabling more effective countermeasures.

[0086] When presenting avoidance methods, the analysis unit can provide individually customized advice by taking into account the user's past behavioral history and patterns. For example, when presenting avoidance methods, the analysis unit can provide individually customized advice by taking into account the user's past behavioral history and patterns. For example, the analysis unit can provide customized ways to avoid fraud to a user who has previously searched for fraud-related keywords. The analysis unit can also provide advice based on the user's past location information. For example, if a user frequently repeats behavior in a specific location, the analysis unit can provide advice to reduce the crime risk in that location. The analysis unit can also provide advice based on the user's past app usage history. For example, if the use of a specific app suddenly increases, the analysis unit can provide advice to reduce the crime risk associated with that app. This allows for more effective countermeasures by providing individually customized advice by taking into account the user's past behavioral history and patterns.

[0087] The analysis unit can use the emotion estimation function to analyze what emotions the user feels about the presented avoidance method and adjust the presentation method. The analysis unit, for example, uses the emotion estimation function to analyze what emotions the user feels about the presented avoidance method. For example, if the user feels anxious, the analysis unit presents the avoidance method in a tone that gives a sense of security. The analysis unit can also analyze the user's facial expression data to estimate emotions. For example, by analyzing changes in facial expression and, if the user feels anxious, the analysis unit presents the avoidance method in a tone that gives a sense of security. The analysis unit can also analyze the user's voice data to estimate emotions. For example, by analyzing the tone and speed of the voice and, if the user feels anxious, the analysis unit presents the avoidance method in a tone that gives a sense of security. In this way, by analyzing what emotions the user feels about the presented avoidance method and adjusting the presentation method, more effective countermeasures can be implemented.

[0088] The analysis unit can provide an interactive function that simulates a user actually taking action in addition to presenting the evasion methods. The analysis unit, for example, provides an interactive function that simulates a user actually taking action in addition to presenting the evasion methods. For example, the analysis unit simulates a method for avoiding phishing scams. The analysis unit can also provide a simulation for a user to deal with a crime case. For example, the analysis unit provides a simulation for dealing with credit card fraud. The analysis unit can also provide interactive training for a user to deal with a crime case. For example, the analysis unit provides training for dealing with fraud methods. In this way, by providing an interactive function that simulates a user actually taking action in addition to presenting the evasion methods, the user can prepare to put specific countermeasures into practice.

[0089] The analysis unit visually shows the evasion methods using videos or animations, allowing the user to understand them more intuitively. The analysis unit, for example, visually shows the evasion methods using videos or animations, allowing the user to understand them more intuitively. For example, it shows how to avoid phishing scams using animations. The analysis unit can also show a simulation of the evasion methods using videos. For example, it shows how to avoid credit card fraud using videos. The analysis unit can also show a reproduction of the evasion methods using animations. For example, it reproduces evasion methods for dealing with fraudulent techniques using animations. In this way, by visually showing the evasion methods using videos or animations, the user can understand them more intuitively.

[0090] The analysis unit can use the emotion estimation function to monitor in real time how the user feels about the presented avoidance method and optimize the presentation method. The analysis unit, for example, uses the emotion estimation function to monitor in real time how the user feels about the presented avoidance method. For example, if the user feels anxious, the analysis unit presents the avoidance method in a tone that conveys a sense of security. The analysis unit can also monitor the user's facial expression data in real time and estimate their emotions. For example, the analysis unit analyzes changes in facial expression in real time, and if the user feels anxious, the analysis unit presents the avoidance method in a tone that conveys a sense of security. The analysis unit can also monitor the user's voice data in real time and estimate their emotions. For example, the analysis unit analyzes the tone and speed of the voice in real time, and if the user feels anxious, the analysis unit presents the avoidance method in a tone that conveys a sense of security. In this way, by monitoring in real time how the user feels about the presented avoidance method and optimizing the presentation method, more effective countermeasures can be implemented.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] In addition to user behavior data, the user behavior monitoring unit can also collect user health data, which can be used to predict crimes. For example, it can collect a user's sleep patterns and determine whether abnormal sleep patterns may increase the risk of crime. It can also collect a user's dietary records and issue a warning if their nutritional status is deteriorating. It can also collect a user's exercise data and determine whether a lack of exercise can cause stress and anxiety, increasing the risk of crime. This allows for more accurate crime prediction by analyzing the user's health data.

[0093] In addition to user behavioral data, the analysis unit can also analyze a user's purchasing history to predict crime risks. For example, if a user frequently purchases expensive items, it can determine that there is a high risk of fraud. The analysis unit can also analyze a user's purchasing patterns and issue a warning if abnormal purchasing behavior is observed. For example, a warning can be issued if a user who normally purchases everyday items suddenly purchases an expensive electronic device. The analysis unit can also determine, based on the user's purchasing history, that the purchase of a specific product may increase the risk of crime. This makes it possible to predict crime with greater accuracy by analyzing a user's purchasing history.

[0094] The analysis unit can use the emotion estimation function to analyze the emotions a user feels when browsing a specific website and issue a warning if negative emotions are increasing. For example, a warning can be issued if a user feels anxiety or fear while browsing a fraudulent website. The analysis unit can also analyze the content of a user's social media posts and issue a warning if there is an increase in posts containing strong negative emotions. The analysis unit can also analyze the content of a user's emails and issue a warning if negative emotions are increasing. This allows for more effective warnings by analyzing the user's emotional state and issuing a warning if negative emotions are increasing.

[0095] In addition to user behavioral data, the analysis unit can also analyze the user's hobbies and interests to predict crime risks. For example, if a user frequently visits websites related to a particular hobby, the analysis unit can determine that there is a high risk of fraud related to that hobby. The analysis unit can also predict crime risks associated with specific events or locations based on the user's interests. For example, if a user plans to attend a particular music festival, the analysis unit can warn the user of the crime risks associated with that event. The analysis unit can also predict the risk of purchasing a specific product based on the user's hobbies and interests. This allows for more accurate crime prediction by analyzing a user's hobbies and interests.

[0096] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when using a specific app and issue a warning if negative emotions are increasing. For example, a warning can be issued if the user is feeling anxious or stressed while using a financial app. The analysis unit can also analyze the user's usage of a game app and issue a warning if negative emotions are increasing. The analysis unit can also analyze the user's usage of a social media app and issue a warning if negative emotions are increasing. This allows for more effective warnings by analyzing the user's emotions in real time when using a specific app and issuing a warning if negative emotions are increasing.

[0097] In addition to user behavior data, the user behavior monitoring unit can analyze the user's communication history to predict criminal risks. For example, if a user frequently contacts a specific phone number, it can determine that there is a high risk of fraud related to that phone number. The user behavior monitoring unit can also analyze the user's email history to predict criminal risks related to a specific email address. For example, if a user frequently receives emails from a specific email address, it can determine that there is a high risk of fraud related to that email address. The user behavior monitoring unit can also analyze the user's messaging app history to predict criminal risks related to specific contacts. This allows for more accurate criminal prediction by analyzing the user's communication history.

[0098] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when they are in a specific location and issue a warning if negative emotions are increasing. For example, a warning can be issued if the user is feeling anxious or scared while in a specific area. The analysis unit can also analyze the user's movement patterns and issue a warning if negative emotions are increasing in a specific location. The analysis unit can also predict crime risks in specific locations based on the user's location information and issue a warning if negative emotions are increasing. This allows for more effective alerts by analyzing the user's emotions in real time when they are in a specific location and issuing a warning if negative emotions are increasing.

[0099] In addition to user behavioral data, the analysis unit can also analyze a user's learning history to predict criminal risk. For example, if a user is taking a specific online course, it can determine that there is a high risk of fraud related to that course. The analysis unit can also analyze a user's learning patterns and issue a warning if abnormal learning behavior is observed. For example, a warning can be issued if a user who normally takes technology-related courses suddenly starts taking finance-related courses. The analysis unit can also determine, based on the user's learning history, that certain learning content may increase the risk of criminal activity. This allows for more accurate crime prediction by analyzing a user's learning history.

[0100] The analysis unit can use the emotion estimation function to analyze the emotions of a user when viewing specific content in real time and issue a warning if negative emotions are increasing. For example, a warning is issued if the user feels anxiety or fear while viewing a news article. The analysis unit can also analyze the user's video viewing history and issue a warning if negative emotions are increasing. The analysis unit can also analyze the user's music listening history and issue a warning if negative emotions are increasing. This allows for more effective attention by analyzing the emotions of a user when viewing specific content in real time and issuing a warning if negative emotions are increasing.

[0101] In addition to user behavior data, the analysis unit can also analyze the user's travel history to predict crime risks. For example, if a user frequently travels to a particular country or region, it can determine that there is a high crime risk associated with that region. The analysis unit can also analyze the user's travel patterns and issue a warning if abnormal travel behavior is observed. For example, a warning can be issued if a user who normally travels domestically suddenly plans to travel abroad. The analysis unit can also determine, based on the user's travel history, that certain travel destinations may increase the risk of crime. This allows for more accurate crime prediction by analyzing the user's travel history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The user behavior monitoring unit collects user behavior data, such as location information, browsing history, and app usage history. Location information is collected using GPS, browsing history is collected by collecting web browser history data, and app usage history is collected by collecting smartphone app usage data. Step 2: The analysis unit analyzes the user behavior data collected by the user behavior monitoring unit. For example, the data is analyzed using machine learning algorithms, natural language processing technology, and data mining technology. A support vector machine is used as the machine learning algorithm, natural language processing technology is used to analyze text data, and data mining technology is used to extract useful information from large amounts of data. Step 3: The warning unit issues a warning if the analysis unit determines that a crime may have occurred. For example, it may display a pop-up message, send an email, or send an app notification. A pop-up message saying "This behavior may be fraudulent" is displayed, an email is sent to the user's registered email address, and an app notification is sent using the smartphone's notification function.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a user behavior monitoring unit that collects user behavior data; an analysis unit that analyzes the user behavior data collected by the user behavior monitoring unit; a warning unit that issues a warning when the analysis unit determines that a crime may have occurred. A system characterized by:

2. The user behavior monitoring unit In addition to the user behavior data, social media posts and comments will also be analyzed to predict crimes from a wider range of data.

2. The system of claim 1.

3. The warning unit When warning of the possibility of a crime, the system takes into account the user's past behavioral history and patterns and issues an individually customized warning.

2. The system of claim 1.

4. The analysis unit Refer to a past crime database and present specific crime cases based on the user's behavior.

2. The system of claim 1.

5. The analysis unit Analyzing the user's emotional state and providing special attention when stress or anxiety levels are high 2. The system of claim 1.

Images