System

Abstract

To provide a system that predicts and warns of crimes based on user's online behavior, and promotes safe online activities.SOLUTION: A system comprises a learning unit, a monitoring unit, a warning unit, and a conversation unit. The learning unit learns data on past incidents, accidents, and crimes. The monitoring unit monitors user's behavior based on the data learned by the learning unit. The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. The conversation unit makes an interactive conversation based on the warning posted by the warning unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately predict or warn against crimes based on users' online behavior, and there is room for improvement.

[0005] The system according to the embodiment aims to predict and warn against crimes related to a user's online activities, and to promote safe online activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a monitoring unit, a warning unit, and a conversation unit. The learning unit learns data on past incidents, accidents, and crimes. The monitoring unit monitors user behavior based on the data learned by the learning unit. The warning unit posts a warning based on the user behavior monitored by the monitoring unit. The conversation unit conducts an interactive conversation based on the warning posted by the warning unit. Effect of the Invention

[0007] The system according to the embodiment can predict and warn against crimes against users' online activities, and promote safe online activities. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. 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 is a system for preventing crimes and damages via the Internet. This crime prevention system learns past incidents, accidents, and crime data, monitors the user's behavior, posts a warning when a crime is predicted, and provides interactive conversations and information to promote the user's understanding of safe online activities and risks. For example, the crime prevention system learns past incidents, accidents, and crime data. This data includes the type of crime, the location of the crime, the amount of damage, and the method of crime. For example, by learning data on fraud cases, it is possible to grasp the method of fraud and the amount of damage. Next, the crime prevention system monitors the user's behavior. The system analyzes the keywords searched by the user, the contents of posts, the contents of communications, and the like in real time. For example, if a user searches for "methods of fraud," the crime prevention system detects the behavior and determines that there is a possibility of fraud. If there is a prediction of a crime in the user's behavior, the crime prevention system posts a warning. For example, a user who searches for "methods of fraud" is warned that "there is a possibility of fraud. Fraud violates the law and may result in severe penalties." It also provides advice such as "avoid committing fraud" as a way to avoid fraud. Furthermore, the crime prevention system provides interactive conversations and information. When a user inputs a question, the crime prevention system provides an appropriate answer. For example, if the user asks, "What can I do to avoid becoming a victim of fraud?", the system will provide an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." In this way, the crime prevention system can help users understand safe online activities and risks, leading to crime prevention. This allows the crime prevention system to monitor user behavior, post warnings when a crime is predicted, and promote safe online activities through interactive conversations.

[0029] The crime prevention system according to the embodiment includes a learning unit, a monitoring unit, a warning unit, and a conversation unit. The learning unit learns past incident and accident data and crime data. The past incident and accident data includes, but is not limited to, police reports, news articles, and court records. The learning unit learns past incident and accident data using, for example, a machine learning algorithm. The learning unit can also use data mining technology to extract useful information from past incident and accident data and crime data. For example, the learning unit learns data on fraud cases and grasps the fraud method and amount of damage. The monitoring unit monitors the user's behavior based on the data learned by the learning unit. The monitoring unit analyzes, for example, keywords searched by the user, the contents of posts, and the contents of communication in real time. The monitoring unit collects, for example, search engine logs and browser history, and analyzes the user's search keywords. The monitoring unit can also collect SNS posts and forum comments and analyze the contents of users' posts. Furthermore, the monitoring unit can collect chat logs and email contents and analyze the contents of users' communication. For example, when a user searches for "methods of fraud," the monitoring unit detects the behavior and determines that there is a possibility of fraud. The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. For example, when the user's behavior indicates a crime, the warning unit posts the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime. The warning unit posts a warning using, for example, a pop-up message or an email notification. For example, the warning unit displays a warning such as "There is a possibility of fraud. Fraud is against the law and may result in severe penalties" to a user who searches for "methods of fraud." The warning unit also provides advice such as "Do not commit fraud." The conversation unit has an interactive conversation based on the warning posted by the warning unit. For example, when a user inputs a question, the conversation unit provides an appropriate answer. The conversation unit has an interactive conversation using, for example, a chatbot or a voice assistant.For example, in response to a user asking, "What should I do to avoid becoming a victim of fraud?", the conversation unit provides an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." In this way, the crime prevention system according to the embodiment can monitor the user's behavior, post a warning when a crime is predicted, and promote safe online activities through interactive conversation.

[0030] The learning unit learns past incident and accident data. Past incident and accident data includes, but is not limited to, police reports, news articles, and court records. The learning unit learns past incident and accident data using, for example, a machine learning algorithm. Specifically, the learning unit uses natural language processing technology to extract important information from a huge amount of text data and classify and organize it. For example, the learning unit extracts the location and time of the incident, the method of crime, and the victim's attributes from the police report, and grasps the background and social impact of the incident from the news article. From the court records, the learning unit can learn the motive of the criminal, the outcome of the trial, and the tendency of sentencing. Furthermore, the learning unit can also use data mining technology to extract useful information from past incident and accident data. For example, it analyzes the pattern of crime occurrence in a specific area or time period and builds a crime prediction model. In addition, the learning unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the learning unit can perform advanced analysis and prediction based on past data, improving the accuracy and reliability of the entire system.

[0031] The monitoring unit monitors the user's behavior based on the data learned by the learning unit. The monitoring unit, for example, analyzes the keywords searched by the user, the contents of posts, and the contents of communication in real time. Specifically, the monitoring unit monitors the user's Internet activity, and collects and analyzes search engine logs and browser history. For example, if a user searches for "methods of fraud," the monitoring unit detects the behavior and determines that there is a possibility of fraud. The monitoring unit can also collect SNS posts and forum comments and analyze the contents of the user's posts. For example, if a user posts "looking for information on fraud" on SNS, the monitoring unit detects the behavior and prepares to issue a warning. Furthermore, the monitoring unit can also collect chat logs and email contents and analyze the contents of the user's communication. For example, if a user requests by email "tell me the methods of fraud," the monitoring unit detects the behavior and immediately issues a warning. The monitoring unit can analyze these data in real time and respond quickly based on the user's behavior. Furthermore, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal behavior and issue an early warning. This enables the monitoring unit to closely monitor the user's behavior and respond quickly and appropriately if a crime is foreseen.

[0032] The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. For example, if the user's behavior indicates a crime, the warning unit posts the possibility of a crime, the expected name of the crime and the amount of damage, and a method to avoid the crime. Specifically, the warning unit posts a warning using a pop-up message or an email notification. For example, if a user searches for "methods of fraud," the warning unit immediately displays a warning such as "There is a possibility of fraud. Fraud violates the law and may result in severe penalties." The warning unit also provides advice such as "Do not commit fraud." Furthermore, the warning unit can generate individually customized warning messages based on the user's behavior history. For example, a stronger warning message is displayed to a user who has frequently performed fraud-related searches in the past, preventing criminal acts from occurring. The warning unit can also collect user feedback and continuously improve the accuracy and effectiveness of the warning content. For example, the warning unit analyzes the user's reaction to the warning message and seeks a more effective warning method. This allows the warning unit to provide a prompt and appropriate warning to the user and prevent criminal acts from occurring.

[0033] The conversation unit has an interactive conversation based on the warning posted by the warning unit. For example, when the user inputs a question, the conversation unit provides an appropriate answer. Specifically, the conversation unit has an interactive conversation using a chatbot or a voice assistant. For example, when the user asks, "What should I do to avoid becoming a victim of fraud?", the conversation unit provides an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." The conversation unit can also provide detailed information in response to the user's question. For example, when the user asks, "What are the methods of fraud?", the conversation unit provides specific information such as, "The methods of fraud include phishing fraud, bank transfer fraud, and online shopping fraud." Furthermore, the conversation unit can provide an answer that is individually customized based on the user's behavior history and the content of past questions. For example, for a user who has frequently asked fraud-related questions in the past, more detailed information and specific measures are provided to prevent criminal acts. The conversation unit can also collect user feedback and continuously improve the accuracy and effectiveness of the answer content. For example, the conversation unit analyzes the user's reaction and seeks more effective answering methods. This enables the conversation unit to provide users with quick and appropriate responses and promote safe online activity.

[0034] The learning unit can learn data on the type of crime, the location of occurrence, the amount of damage, and the method of crime. The learning unit learns data on the type of crime, the location of occurrence, the amount of damage, and the method of crime, for example. The type of crime includes, but is not limited to, theft, fraud, and assault, for example. The location of the crime includes, but is not limited to, a city, a region, a specific facility, for example. The amount of damage includes, but is not limited to, monetary damage, property damage, and the like. The method of crime includes, but is not limited to, details of the method, tools used, and the like, for example. By learning detailed data such as the type of crime and the method of crime, more accurate predictions are possible. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data into a generation AI and have the generation AI learn the data.

[0035] The monitoring unit can analyze the keywords searched by the user, the contents of posts, and the contents of communication in real time. The monitoring unit, for example, analyzes the keywords searched by the user, the contents of posts, and the contents of communication in real time. The search keywords include, for example, search engine logs and browser history, but are not limited to such examples. The contents of posts include, for example, SNS posts and forum comments, but are not limited to such examples. The contents of communication include, for example, chat logs and email contents, but are not limited to such examples. This allows for a quick warning by analyzing the user's behavior in real time. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's behavior data into the generation AI, and the generation AI can analyze the data.

[0036] The warning unit can display the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime, when the user's behavior predicts a crime. For example, when the user's behavior predicts a crime, the warning unit displays the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime. Foresight of a crime includes, for example, a prediction model based on past data and an analysis of behavioral patterns, but is not limited to such examples. Foresight of a crime includes, for example, a classification based on the law and past precedents, but is not limited to such examples. Foresight of a crime includes, for example, a safe behavioral guideline and preventive measures, but is not limited to such examples. In this way, by displaying a specific warning when a crime is predicted, the user's behavior can be deterred. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the user's behavioral data into a generation AI, and the generation AI can generate a warning message.

[0037] The conversation unit can provide an appropriate answer when the user inputs a question. The conversation unit can provide an appropriate answer when the user inputs a question, for example. Appropriate answers include, but are not limited to, an FAQ database or expert opinions. This allows the user to deepen their understanding by providing an appropriate answer to the user's question. Some or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI, or can be performed without using AI. For example, the conversation unit can input the user's question data to a generation AI, and the generation AI can generate an answer.

[0038] The learning unit can extract a specific pattern from past crime data during learning and optimize the crime prediction model. The learning unit can extract a specific pattern from past crime data during learning, for example, and optimize the crime prediction model. The specific pattern includes, for example, a frequent pattern and an abnormal pattern, but is not limited to such examples. The crime prediction model includes, for example, a machine learning model and a statistical model, but is not limited to such examples. For example, the learning unit extracts a specific method or time pattern from fraud case data and optimizes the prediction model. The learning unit can also extract a specific IP address or domain pattern from cybercrime data and optimize the prediction model. Furthermore, the learning unit can extract a specific area or time pattern from physical crime data and optimize the prediction model. As a result, the accuracy of the prediction model is improved by extracting the specific pattern. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input crime data to a generation AI and have the generation AI extract a specific pattern.

[0039] The learning unit can learn to respond to the latest crime modus operandi by taking into account the temporal changes in the crime data during learning. The learning unit, for example, learns to respond to the latest crime modus operandi by taking into account the temporal changes in the crime data during learning. The temporal changes include, for example, but are not limited to, analysis of time-series data and trend analysis. The latest crime modus operandi includes, for example, but are not limited to, recent incident reports and news articles. For example, the learning unit preferentially learns data related to the latest fraud modus operandi and updates the prediction model. The learning unit can also learn newly emerged cyber attack methods and reflect them in the prediction model. Furthermore, the learning unit can also learn recent physical crime trends and keep the prediction model up to date. This makes it possible to update the prediction model by learning to respond to the latest crime modus operandi. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the latest crime data into the generation AI and have the generation AI learn the data.

[0040] The learning unit can compare crime data of different regions during learning and learn crime trends specific to the region. The learning unit, for example, compares crime data of different regions during learning and learns crime trends specific to the region. Different regions include, for example, countries, states, cities, etc., but are not limited to such examples. Crime trends specific to the region include, for example, crime statistics by region and characteristics of the region, but are not limited to such examples. For example, the learning unit compares crime data of urban areas and rural areas and learns crime trends specific to each region. The learning unit can also compare crime data by country and learn crime trends specific to the country. Furthermore, the learning unit can compare crime data within a specific region and learn crime trends within the region. As a result, predictions according to the region can be made by learning crime trends specific to the region. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data by region to the generation AI, and the generation AI can compare and learn the data.

[0041] The learning unit can extract data related to a specific age group or gender from the crime data during learning, and perform learning specialized for the target group. For example, the learning unit extracts data related to a specific age group or gender from the crime data during learning, and performs learning specialized for the target group. The specific age group or gender includes, but is not limited to, young people, elderly people, men, women, etc., for example. The learning specialized for the target group includes, but is not limited to, behavioral patterns and characteristics of the target group, for example. For example, the learning unit extracts crime data specific to young people and learns a prediction model for young people. The learning unit can also extract crime data specific to elderly people and learn a prediction model for elderly people. Furthermore, the learning unit can extract crime data related to gender and learn a prediction model for each gender. As a result, by performing learning specialized for a specific age group or gender, prediction according to the target group is possible. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data related to a specific age group or gender into the generation AI, which can then extract and learn from the data.

[0042] The monitoring unit can analyze the user's behavior history during monitoring and detect abnormal behavior based on past behavior patterns. The monitoring unit, for example, analyzes the user's behavior history during monitoring and detects abnormal behavior based on past behavior patterns. The behavior history includes, for example, log data and behavior tracking, but is not limited to such examples. The abnormal behavior includes, for example, deviation from a normal behavior pattern and an abnormal behavior frequency, but is not limited to such examples. For example, the monitoring unit analyzes the user's past search history and detects an abnormal search pattern. The monitoring unit can also analyze the user's past writing history and detect an abnormal writing pattern. Furthermore, the monitoring unit can analyze the user's past communication history and detect an abnormal communication pattern. This makes it possible to detect abnormal behavior based on past behavior patterns and respond quickly. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's behavior history data to the generation AI and detect abnormal behavior using the generation AI.

[0043] The monitoring unit can monitor device-specific risks by taking into account the device information of the user during monitoring. The monitoring unit, for example, monitors device-specific risks by taking into account the device information of the user during monitoring. The device information includes, for example, the type of device and the usage status, but is not limited to such examples. The device-specific risks include, for example, the vulnerability and security risk of the device, but is not limited to such examples. For example, the monitoring unit monitors the security settings of the device used by the user and detects risks. The monitoring unit can also monitor the network connection status of the device used by the user and detect risks. Furthermore, the monitoring unit can also monitor the application usage status of the device used by the user and detect risks. Thereby, by monitoring the device-specific risks, security measures according to the device are possible. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the device information of the user to the generation AI and monitor the risks by the generation AI.

[0044] The monitoring unit can analyze the user's social media activity during monitoring and monitor related risks. The monitoring unit, for example, analyzes the user's social media activity during monitoring and monitors related risks. Social media activities include, for example, but are not limited to, posted content and follower trends. Related risks include, for example, but are not limited to, slander and privacy violations. For example, the monitoring unit analyzes the user's social media posted content and detects risks. The monitoring unit can also analyze the user's social media friendships and detect risks. Furthermore, the monitoring unit can analyze the user's social media activity and detect risks. As a result, related risks can be detected early by analyzing social media activities. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and monitor the risks using the generation AI.

[0045] The monitoring unit can monitor risks specific to a region, taking into account the geographical location information of the user during monitoring. The monitoring unit, for example, monitors risks specific to a region, taking into account the geographical location information of the user during monitoring. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The risks specific to a region include, for example, crime rates in a region and risks of natural disasters, but is not limited to such examples. For example, when the user is in a specific region, the monitoring unit monitors crime risks specific to that region. In addition, when the user is moving, the monitoring unit can also monitor risks specific to the region of the user's destination. Furthermore, when the user is participating in a specific event, the monitoring unit can also monitor risks specific to the event. This makes it possible to take security measures according to the region by monitoring risks specific to the region. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and monitor the risks using the generation AI.

[0046] The warning unit can select the optimal warning content by referring to the user's past behavior history at the time of warning. The warning unit, for example, selects the optimal warning content by referring to the user's past behavior history at the time of warning. The optimal warning content includes, for example, past behavior patterns and the magnitude of risk, but is not limited to such examples. For example, if the user has taken a similar action in the past, the warning content is selected based on that action. In addition, a warning can be displayed for high-risk actions from the user's past behavior history. Furthermore, the user's past behavior history can be analyzed and the most effective warning content can be selected. As a result, the optimal warning content can be selected by referring to the past behavior history. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's behavior history data to a generation AI and select the warning content by the generation AI.

[0047] The warning unit can generate different warning messages according to the type of crime when issuing a warning. The warning unit, for example, generates different warning messages according to the type of crime when issuing a warning. The different warning messages include, for example, message templates according to the type of crime, but are not limited to such examples. For example, the warning unit generates a warning message regarding fraud when there is a possibility of fraud. Furthermore, the warning unit can also generate a warning message regarding a cyber attack when there is a possibility of a cyber attack. Furthermore, the warning unit can also generate a warning message regarding a physical crime when there is a possibility of a physical crime. This allows for a more specific warning by generating a warning message according to the type of crime. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input data according to the type of crime into the generation AI, and the generation AI can generate a warning message.

[0048] The warning unit can generate a device-specific warning message in consideration of the device information of the user at the time of warning. The warning unit, for example, generates a device-specific warning message in consideration of the device information of the user at the time of warning. The device-specific warning message includes, for example, a message according to the vulnerability of the device, but is not limited to such an example. For example, when the user is using a smartphone, the warning unit generates a warning message optimized for the smartphone. In addition, when the user is using a tablet, the warning unit can also generate a warning message optimized for the tablet. Furthermore, when the user is using a personal computer, the warning unit can also generate a warning message optimized for the personal computer. Thereby, by generating a device-specific warning message, it is possible to provide an appropriate warning according to the device. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the device information of the user to the generation AI, and the generation AI can generate a warning message.

[0049] The warning unit can generate a region-specific warning message in consideration of the geographical location information of the user at the time of warning. The warning unit generates a region-specific warning message in consideration of the geographical location information of the user at the time of warning, for example. The region-specific warning message includes, for example, a message according to the crime trend of the region, but is not limited to such an example. For example, when the user is in a specific region, the warning unit generates a warning message regarding a crime risk specific to the region. In addition, when the user is moving, the warning unit can also generate a warning message regarding a risk specific to the destination region. Furthermore, when the user is participating in a specific event, the warning unit can also generate a warning message regarding a risk specific to the event. As a result, by generating a region-specific warning message, it is possible to provide an appropriate warning according to the region. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the geographical location information of the user to the generation AI, and the generation AI can generate a warning message.

[0050] The conversation unit can provide an optimal answer by referring to the user's past question history during a conversation. The conversation unit can provide an optimal answer by referring to the user's past question history during a conversation, for example. The past question history includes, but is not limited to, a chat log and a question database. For example, a related answer is provided based on the content of a question asked by the user in the past. The conversation unit can also select and provide the most appropriate answer from the user's past question history. Furthermore, the conversation unit can analyze the user's past question history and prepare an answer to a predicted question. In this way, the optimal answer can be provided to the user by referring to the past question history. A part or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI or can be performed without using AI. For example, the conversation unit can input the user's question history data to the generation AI, and the generation AI can select the optimal answer.

[0051] The conversation unit can provide appropriate advice in real time according to the user's current situation during conversation. The conversation unit, for example, provides appropriate advice in real time according to the user's current situation during conversation. The current situation includes, for example, real-time data and the user's behavioral situation, but is not limited to such examples. For example, a solution is provided in real time for a problem that the user is currently facing. The conversation unit can also provide appropriate advice in real time based on the user's current behavior. Furthermore, the conversation unit can analyze the user's current situation and provide optimal advice in real time. This enables a quick response by providing advice according to the user's current situation in real time. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's current situation data to the generation AI and provide advice by the generation AI.

[0052] The conversation unit can provide device-specific advice in consideration of the device information of the user during conversation. The conversation unit can provide device-specific advice in consideration of the device information of the user during conversation, for example. The device-specific advice includes, for example, advice according to the settings of the device, but is not limited to such an example. For example, if the user is using a smartphone, the conversation unit can provide advice optimized for the smartphone. In addition, if the user is using a tablet, the conversation unit can also provide advice optimized for the tablet. Furthermore, if the user is using a personal computer, the conversation unit can also provide advice optimized for the personal computer. As a result, by providing device-specific advice, appropriate advice according to the device is possible. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the device information of the user to the generation AI, and the generation AI can provide advice.

[0053] The conversation unit can provide region-specific advice in consideration of the geographical location information of the user during conversation. The conversation unit can provide region-specific advice in consideration of the geographical location information of the user during conversation. Region-specific advice includes, for example, advice according to the characteristics of the region, but is not limited to such examples. For example, when the user is in a specific region, the conversation unit provides advice on risks specific to the region. In addition, when the user is moving, the conversation unit can also provide advice on risks specific to the destination region. Furthermore, when the user is participating in a specific event, the conversation unit can also provide advice on risks specific to the event. As a result, by providing region-specific advice, appropriate advice according to the region is possible. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the geographical location information of the user to the generation AI, and the generation AI can provide advice.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The crime prevention system may further include a risk assessment unit that analyzes the user's behavioral patterns and evaluates whether a particular behavior increases the risk of crime. For example, the risk assessment unit may analyze the usage history of websites and applications frequently visited by the user to identify high-risk behavior. The risk assessment unit may also monitor the user's online transaction history to detect signs of fraudulent transactions. Furthermore, the risk assessment unit may analyze the user's social media activities to evaluate the risk of fraud and phishing. This allows the system to evaluate the risk based on the user's behavioral patterns and take appropriate measures.

[0056] The crime prevention system may further include an anomaly detection unit that analyzes the user's behavior history and detects abnormal behavior based on past behavior patterns. For example, the anomaly detection unit may analyze the user's past search history and detect abnormal search patterns. The anomaly detection unit may also analyze the user's past posting history and detect abnormal posting patterns. The anomaly detection unit may also analyze the user's past communication history and detect abnormal communication patterns. This allows abnormal behavior to be detected based on past behavior patterns, enabling rapid response.

[0057] The crime prevention system can further include a device monitoring unit that takes into account the user's device information and monitors risks specific to the device. For example, the device monitoring unit monitors the security settings of the device used by the user and detects risks. The device monitoring unit can also monitor the network connection status of the device used by the user and detect risks. Furthermore, the device monitoring unit can monitor the application usage status of the device used by the user and detect risks. This makes it possible to monitor risks specific to the device and take security measures according to the device.

[0058] The crime prevention system can further include a region monitoring unit that monitors region-specific risks by taking into account the geographical location information of the user. For example, when the user is in a particular region, the region monitoring unit monitors the crime risks specific to that region. In addition, when the user is traveling, the region monitoring unit can also monitor the risks specific to the region to which the user travels. Furthermore, when the user is participating in a particular event, the region monitoring unit can also monitor the risks specific to that event. This makes it possible to monitor region-specific risks and implement security measures according to the region.

[0059] The crime prevention system may further include a warning selection unit that refers to the user's past behavior history and selects the most appropriate warning content. For example, if the user has performed a similar behavior in the past, the warning selection unit selects the warning content based on that behavior. The warning selection unit may also display a warning for high-risk behavior based on the user's past behavior history. Furthermore, the warning selection unit may analyze the user's past behavior history and select the most effective warning content. In this way, the most appropriate warning content can be selected by referring to the past behavior history.

[0060] The crime prevention system may further include an answer selection unit that provides the most appropriate answer by referring to the user's past question history. For example, the answer selection unit provides a related answer based on the content of questions asked by the user in the past. The answer selection unit may also select and provide the most appropriate answer from the user's past question history. Furthermore, the answer selection unit may analyze the user's past question history and prepare answers to predicted questions. In this way, the most appropriate answer can be provided to the user by referring to the past question history.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The learning module learns from past incident and crime data. Past incident and crime data includes police reports, news articles, court records, etc. The learning module uses machine learning algorithms and data mining techniques to extract useful information from this data. Step 2: The monitoring unit monitors user behavior based on the data learned by the learning unit. The monitoring unit analyzes the keywords searched by users, the content of posts, and the content of communications in real time. For example, it collects and analyzes search engine logs, browser history, social media posts, forum comments, chat logs, and email content. Step 3: The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. If the user's behavior indicates a crime, the warning unit posts a warning about the possibility of a crime, the expected name of the crime and the amount of damage, and how to avoid the crime. For example, the warning is posted using a pop-up message or an email notification. Step 4: The conversation unit performs an interactive conversation based on the warning posted by the warning unit. When the user inputs a question, the conversation unit provides an appropriate answer. For example, an interactive conversation is performed using a chatbot or a voice assistant.

[0063] (Example 2) A crime prevention system according to an embodiment of the present invention is a system for preventing crimes and damages via the Internet. This crime prevention system learns past incidents, accidents, and crime data, monitors the user's behavior, posts a warning when a crime is predicted, and provides interactive conversations and information to promote the user's understanding of safe online activities and risks. For example, the crime prevention system learns past incidents, accidents, and crime data. This data includes the type of crime, the location of the crime, the amount of damage, and the method of crime. For example, by learning data on fraud cases, it is possible to grasp the method of fraud and the amount of damage. Next, the crime prevention system monitors the user's behavior. The system analyzes the keywords searched by the user, the contents of posts, the contents of communications, and the like in real time. For example, if a user searches for "methods of fraud," the crime prevention system detects the behavior and determines that there is a possibility of fraud. If there is a prediction of a crime in the user's behavior, the crime prevention system posts a warning. For example, a user who searches for "methods of fraud" is warned that "there is a possibility of fraud. Fraud violates the law and may result in severe penalties." It also provides advice such as "avoid committing fraud" as a way to avoid fraud. Furthermore, the crime prevention system provides interactive conversations and information. When a user inputs a question, the crime prevention system provides an appropriate answer. For example, if the user asks, "What can I do to avoid becoming a victim of fraud?", the system will provide an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." In this way, the crime prevention system can help users understand safe online activities and risks, leading to crime prevention. This allows the crime prevention system to monitor user behavior, post warnings when a crime is predicted, and promote safe online activities through interactive conversations.

[0064] The crime prevention system according to the embodiment includes a learning unit, a monitoring unit, a warning unit, and a conversation unit. The learning unit learns past incident and accident data and crime data. The past incident and accident data includes, but is not limited to, police reports, news articles, and court records. The learning unit learns past incident and accident data using, for example, a machine learning algorithm. The learning unit can also use data mining technology to extract useful information from past incident and accident data and crime data. For example, the learning unit learns data on fraud cases and grasps the fraud method and amount of damage. The monitoring unit monitors the user's behavior based on the data learned by the learning unit. The monitoring unit analyzes, for example, keywords searched by the user, the contents of posts, and the contents of communication in real time. The monitoring unit collects, for example, search engine logs and browser history, and analyzes the user's search keywords. The monitoring unit can also collect SNS posts and forum comments and analyze the contents of users' posts. Furthermore, the monitoring unit can collect chat logs and email contents and analyze the contents of users' communication. For example, when a user searches for "methods of fraud," the monitoring unit detects the behavior and determines that there is a possibility of fraud. The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. For example, when the user's behavior indicates a crime, the warning unit posts the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime. The warning unit posts a warning using, for example, a pop-up message or an email notification. For example, the warning unit displays a warning such as "There is a possibility of fraud. Fraud is against the law and may result in severe penalties" to a user who searches for "methods of fraud." The warning unit also provides advice such as "Do not commit fraud." The conversation unit has an interactive conversation based on the warning posted by the warning unit. For example, when a user inputs a question, the conversation unit provides an appropriate answer. The conversation unit has an interactive conversation using, for example, a chatbot or a voice assistant.For example, in response to a user asking, "What should I do to avoid becoming a victim of fraud?", the conversation unit provides an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." In this way, the crime prevention system according to the embodiment can monitor the user's behavior, post a warning when a crime is predicted, and promote safe online activities through interactive conversation.

[0065] The learning unit learns past incident and accident data. Past incident and accident data includes, but is not limited to, police reports, news articles, and court records. The learning unit learns past incident and accident data using, for example, a machine learning algorithm. Specifically, the learning unit uses natural language processing technology to extract important information from a huge amount of text data and classify and organize it. For example, the learning unit extracts the location and time of the incident, the method of crime, and the victim's attributes from the police report, and grasps the background and social impact of the incident from the news article. From the court records, the learning unit can learn the motive of the criminal, the outcome of the trial, and the tendency of sentencing. Furthermore, the learning unit can also use data mining technology to extract useful information from past incident and accident data. For example, it analyzes the pattern of crime occurrence in a specific area or time period and builds a crime prediction model. In addition, the learning unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the learning unit can perform advanced analysis and prediction based on past data, improving the accuracy and reliability of the entire system.

[0066] The monitoring unit monitors the user's behavior based on the data learned by the learning unit. The monitoring unit, for example, analyzes the keywords searched by the user, the contents of posts, and the contents of communication in real time. Specifically, the monitoring unit monitors the user's Internet activity, and collects and analyzes search engine logs and browser history. For example, if a user searches for "methods of fraud," the monitoring unit detects the behavior and determines that there is a possibility of fraud. The monitoring unit can also collect SNS posts and forum comments and analyze the contents of the user's posts. For example, if a user posts "looking for information on fraud" on SNS, the monitoring unit detects the behavior and prepares to issue a warning. Furthermore, the monitoring unit can also collect chat logs and email contents and analyze the contents of the user's communication. For example, if a user requests by email "tell me the methods of fraud," the monitoring unit detects the behavior and immediately issues a warning. The monitoring unit can analyze these data in real time and respond quickly based on the user's behavior. Furthermore, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal behavior and issue an early warning. This enables the monitoring unit to closely monitor the user's behavior and respond quickly and appropriately if a crime is foreseen.

[0067] The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. For example, if the user's behavior indicates a crime, the warning unit posts the possibility of a crime, the expected name of the crime and the amount of damage, and a method to avoid the crime. Specifically, the warning unit posts a warning using a pop-up message or an email notification. For example, if a user searches for "methods of fraud," the warning unit immediately displays a warning such as "There is a possibility of fraud. Fraud violates the law and may result in severe penalties." The warning unit also provides advice such as "Do not commit fraud." Furthermore, the warning unit can generate individually customized warning messages based on the user's behavior history. For example, a stronger warning message is displayed to a user who has frequently performed fraud-related searches in the past, preventing criminal acts from occurring. The warning unit can also collect user feedback and continuously improve the accuracy and effectiveness of the warning content. For example, the warning unit analyzes the user's reaction to the warning message and seeks a more effective warning method. This allows the warning unit to provide a prompt and appropriate warning to the user and prevent criminal acts from occurring.

[0068] The conversation unit has an interactive conversation based on the warning posted by the warning unit. For example, when the user inputs a question, the conversation unit provides an appropriate answer. Specifically, the conversation unit has an interactive conversation using a chatbot or a voice assistant. For example, when the user asks, "What should I do to avoid becoming a victim of fraud?", the conversation unit provides an answer such as, "To avoid becoming a victim of fraud, do not respond to suspicious emails or phone calls." The conversation unit can also provide detailed information in response to the user's question. For example, when the user asks, "What are the methods of fraud?", the conversation unit provides specific information such as, "The methods of fraud include phishing fraud, bank transfer fraud, and online shopping fraud." Furthermore, the conversation unit can provide an answer that is individually customized based on the user's behavior history and the content of past questions. For example, for a user who has frequently asked fraud-related questions in the past, more detailed information and specific measures are provided to prevent criminal acts. The conversation unit can also collect user feedback and continuously improve the accuracy and effectiveness of the answer content. For example, the conversation unit analyzes the user's reaction and seeks more effective answering methods. This enables the conversation unit to provide users with quick and appropriate responses and promote safe online activity.

[0069] The learning unit can learn data on the type of crime, the location of occurrence, the amount of damage, and the method of crime. The learning unit learns data on the type of crime, the location of occurrence, the amount of damage, and the method of crime, for example. The type of crime includes, but is not limited to, theft, fraud, and assault, for example. The location of the crime includes, but is not limited to, a city, a region, a specific facility, for example. The amount of damage includes, but is not limited to, monetary damage, property damage, and the like. The method of crime includes, but is not limited to, details of the method, tools used, and the like, for example. By learning detailed data such as the type of crime and the method of crime, more accurate predictions are possible. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data into a generation AI and have the generation AI learn the data.

[0070] The monitoring unit can analyze the keywords searched by the user, the contents of posts, and the contents of communication in real time. The monitoring unit, for example, analyzes the keywords searched by the user, the contents of posts, and the contents of communication in real time. The search keywords include, for example, search engine logs and browser history, but are not limited to such examples. The contents of posts include, for example, SNS posts and forum comments, but are not limited to such examples. The contents of communication include, for example, chat logs and email contents, but are not limited to such examples. This allows for a quick warning by analyzing the user's behavior in real time. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's behavior data into the generation AI, and the generation AI can analyze the data.

[0071] The warning unit can display the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime, when the user's behavior predicts a crime. For example, when the user's behavior predicts a crime, the warning unit displays the possibility of a crime, the expected name of the crime and the amount of damage, and a method of avoiding the crime. Foresight of a crime includes, for example, a prediction model based on past data and an analysis of behavioral patterns, but is not limited to such examples. Foresight of a crime includes, for example, a classification based on the law and past precedents, but is not limited to such examples. Foresight of a crime includes, for example, a safe behavioral guideline and preventive measures, but is not limited to such examples. In this way, by displaying a specific warning when a crime is predicted, the user's behavior can be deterred. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the user's behavioral data into a generation AI, and the generation AI can generate a warning message.

[0072] The conversation unit can provide an appropriate answer when the user inputs a question. The conversation unit can provide an appropriate answer when the user inputs a question, for example. Appropriate answers include, but are not limited to, an FAQ database or expert opinions. This allows the user to deepen their understanding by providing an appropriate answer to the user's question. Some or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI, or can be performed without using AI. For example, the conversation unit can input the user's question data to a generation AI, and the generation AI can generate an answer.

[0073] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and selects learning data based on the estimated user's emotion. The user's emotion includes, for example, anxiety, excitement, relaxation, etc., but is not limited to such examples. The selection of learning data includes, for example, reliability and relevance of data, but is not limited to such examples. For example, when the user feels anxious, information that gives a sense of security from past crime data is preferentially learned. In addition, when the user is excited, data related to crime prevention for regaining composure can also be preferentially learned. Furthermore, when the user is relaxed, general crime data can also be learned in a balanced manner. This enables more effective learning by selecting data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the learning unit can input a user's emotional data into the generation AI, and the generation AI can select learning data.

[0074] The learning unit can extract specific patterns from past crime data during learning and optimize the crime prediction model. The learning unit can extract specific patterns from past crime data during learning, for example, and optimize the crime prediction model. The specific patterns include, for example, frequent patterns and abnormal patterns, but are not limited to such examples. The crime prediction model includes, for example, machine learning models and statistical models, but are not limited to such examples. For example, the learning unit extracts specific methods and time patterns from fraud case data and optimizes the prediction model. The learning unit can also extract specific IP address and domain patterns from cybercrime data and optimize the prediction model. Furthermore, the learning unit can extract specific area and time patterns from physical crime data and optimize the prediction model. As a result, the accuracy of the prediction model is improved by extracting specific patterns. Some or all of the above-mentioned processes in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input crime data to a generation AI and have the generation AI extract specific patterns.

[0075] The learning unit can learn to respond to the latest crime modus operandi by taking into account the temporal changes in the crime data during learning. The learning unit, for example, learns to respond to the latest crime modus operandi by taking into account the temporal changes in the crime data during learning. The temporal changes include, for example, but are not limited to, analysis of time-series data and trend analysis. The latest crime modus operandi includes, for example, but are not limited to, recent incident reports and news articles. For example, the learning unit preferentially learns data related to the latest fraud modus operandi and updates the prediction model. The learning unit can also learn newly emerged cyber attack methods and reflect them in the prediction model. Furthermore, the learning unit can also learn recent physical crime trends and keep the prediction model up to date. This makes it possible to update the prediction model by learning to respond to the latest crime modus operandi. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the latest crime data into the generation AI and have the generation AI learn the data.

[0076] The learning unit can estimate the user's emotion and adjust the learning frequency based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and adjusts the learning frequency based on the estimated user's emotion. The learning frequency includes, for example, periodic learning and real-time learning, but is not limited to such examples. For example, when the user is stressed, the learning frequency is reduced to reduce the burden. Also, when the user is relaxed, the learning frequency can be increased to efficiently acquire data. Furthermore, when the user is excited, the learning frequency can be adjusted to a moderate level to achieve balance. In this way, the burden on the user can be reduced by adjusting the learning frequency according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the learning frequency.

[0077] The learning unit can compare crime data of different regions during learning and learn crime trends specific to the region. The learning unit, for example, compares crime data of different regions during learning and learns crime trends specific to the region. Different regions include, for example, countries, states, cities, etc., but are not limited to such examples. Crime trends specific to the region include, for example, crime statistics by region and characteristics of the region, but are not limited to such examples. For example, the learning unit compares crime data of urban areas and rural areas and learns crime trends specific to each region. The learning unit can also compare crime data by country and learn crime trends specific to the country. Furthermore, the learning unit can compare crime data within a specific region and learn crime trends within the region. As a result, predictions according to the region can be made by learning crime trends specific to the region. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data by region to the generation AI, and the generation AI can compare and learn the data.

[0078] The learning unit can extract data related to a specific age group or gender from the crime data during learning, and perform learning specialized for the target group. For example, the learning unit extracts data related to a specific age group or gender from the crime data during learning, and performs learning specialized for the target group. The specific age group or gender includes, but is not limited to, young people, elderly people, men, women, etc., for example. The learning specialized for the target group includes, but is not limited to, behavioral patterns and characteristics of the target group, for example. For example, the learning unit extracts crime data specific to young people and learns a prediction model for young people. The learning unit can also extract crime data specific to elderly people and learn a prediction model for elderly people. Furthermore, the learning unit can extract crime data related to gender and learn a prediction model for each gender. As a result, by performing learning specialized for a specific age group or gender, prediction according to the target group is possible. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input crime data related to a specific age group or gender into the generation AI, which can then extract and learn from the data.

[0079] The monitoring unit can estimate the user's emotion and adjust the intensity of monitoring based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the intensity of monitoring based on the estimated user's emotion. The intensity of monitoring includes, for example, a monitoring frequency and a monitoring range, but is not limited to such examples. For example, when the user feels anxious, the intensity of monitoring is increased to ensure safety. Also, when the user is relaxed, the intensity of monitoring can be reduced to respect privacy. Furthermore, when the user is excited, the intensity of monitoring can be adjusted to a moderate level to achieve a balance. In this way, by adjusting the monitoring intensity according to the user's emotion, it is possible to ensure safety while respecting privacy. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI, and the generation AI can adjust the monitoring intensity.

[0080] The monitoring unit can analyze the user's behavior history during monitoring and detect abnormal behavior based on past behavior patterns. The monitoring unit, for example, analyzes the user's behavior history during monitoring and detects abnormal behavior based on past behavior patterns. The behavior history includes, for example, log data and behavior tracking, but is not limited to such examples. The abnormal behavior includes, for example, deviation from a normal behavior pattern and an abnormal behavior frequency, but is not limited to such examples. For example, the monitoring unit analyzes the user's past search history and detects an abnormal search pattern. The monitoring unit can also analyze the user's past writing history and detect an abnormal writing pattern. Furthermore, the monitoring unit can analyze the user's past communication history and detect an abnormal communication pattern. This makes it possible to detect abnormal behavior based on past behavior patterns and respond quickly. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's behavior history data to the generation AI and detect abnormal behavior using the generation AI.

[0081] The monitoring unit can monitor device-specific risks by taking into account the device information of the user during monitoring. The monitoring unit, for example, monitors device-specific risks by taking into account the device information of the user during monitoring. The device information includes, for example, the type of device and the usage status, but is not limited to such examples. The device-specific risks include, for example, the vulnerability and security risk of the device, but is not limited to such examples. For example, the monitoring unit monitors the security settings of the device used by the user and detects risks. The monitoring unit can also monitor the network connection status of the device used by the user and detect risks. Furthermore, the monitoring unit can also monitor the application usage status of the device used by the user and detect risks. Thereby, by monitoring the device-specific risks, security measures according to the device are possible. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the device information of the user to the generation AI and monitor the risks by the generation AI.

[0082] The monitoring unit can estimate the user's emotion and determine the priority of the behavior to be monitored based on the estimated user's emotion. The monitoring unit can estimate the user's emotion, for example, and determine the priority of the behavior to be monitored based on the estimated user's emotion. The priority of the behavior includes, for example, the magnitude of risk and the importance of the behavior, but is not limited to such examples. For example, when the user feels anxious, important behavior is monitored preferentially. In addition, when the user is relaxed, general behavior can be monitored preferentially. Furthermore, when the user is excited, risky behavior can be monitored preferentially. This enables efficient monitoring by determining the priority of the behavior according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the behavior.

[0083] The monitoring unit can analyze the user's social media activity during monitoring and monitor related risks. The monitoring unit, for example, analyzes the user's social media activity during monitoring and monitors related risks. Social media activities include, for example, but are not limited to, posted content and follower trends. Related risks include, for example, but are not limited to, slander and privacy violations. For example, the monitoring unit analyzes the user's social media posted content and detects risks. The monitoring unit can also analyze the user's social media friendships and detect risks. Furthermore, the monitoring unit can analyze the user's social media activity and detect risks. As a result, related risks can be detected early by analyzing social media activities. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and monitor the risks using the generation AI.

[0084] The monitoring unit can monitor risks specific to a region, taking into account the geographical location information of the user during monitoring. The monitoring unit, for example, monitors risks specific to a region, taking into account the geographical location information of the user during monitoring. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The risks specific to a region include, for example, crime rates in a region and risks of natural disasters, but is not limited to such examples. For example, when the user is in a specific region, the monitoring unit monitors crime risks specific to that region. In addition, when the user is moving, the monitoring unit can also monitor risks specific to the region of the user's destination. Furthermore, when the user is participating in a specific event, the monitoring unit can also monitor risks specific to the event. This makes it possible to take security measures according to the region by monitoring risks specific to the region. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and monitor the risks using the generation AI.

[0085] The warning unit can estimate the user's emotion and adjust the warning expression method based on the estimated user's emotion. The warning unit can estimate the user's emotion, for example, and adjust the warning expression method based on the estimated user's emotion. The warning expression method includes, for example, a text message and a voice alert, but is not limited to such examples. For example, if the user feels anxious, a warning can be displayed in gentle words. Also, if the user is relaxed, a warning including detailed information can be displayed. Furthermore, if the user is excited, a warning encouraging the user to stay calm can be displayed. This enables an effective warning by adjusting the warning expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data into the generation AI, and the generation AI can adjust the warning expression method.

[0086] The warning unit can select the optimal warning content by referring to the user's past behavior history at the time of warning. The warning unit, for example, selects the optimal warning content by referring to the user's past behavior history at the time of warning. The optimal warning content includes, for example, past behavior patterns and the magnitude of risk, but is not limited to such examples. For example, if the user has taken a similar action in the past, the warning content is selected based on that action. In addition, a warning can be displayed for high-risk actions from the user's past behavior history. Furthermore, the user's past behavior history can be analyzed and the most effective warning content can be selected. As a result, the optimal warning content can be selected by referring to the past behavior history. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's behavior history data to a generation AI and select the warning content by the generation AI.

[0087] The warning unit can generate different warning messages according to the type of crime when issuing a warning. The warning unit, for example, generates different warning messages according to the type of crime when issuing a warning. The different warning messages include, for example, message templates according to the type of crime, but are not limited to such examples. For example, the warning unit generates a warning message regarding fraud when there is a possibility of fraud. Furthermore, the warning unit can also generate a warning message regarding a cyber attack when there is a possibility of a cyber attack. Furthermore, the warning unit can also generate a warning message regarding a physical crime when there is a possibility of a physical crime. This allows for a more specific warning by generating a warning message according to the type of crime. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input data according to the type of crime into the generation AI, and the generation AI can generate a warning message.

[0088] The warning unit can estimate the user's emotion and determine the priority of the warning based on the estimated user's emotion. The warning unit can estimate the user's emotion, for example, and determine the priority of the warning based on the estimated user's emotion. The priority of the warning includes, for example, the magnitude of risk and the urgency, but is not limited to such examples. For example, when the user feels anxious, important warnings can be displayed preferentially. In addition, when the user is relaxed, general warnings can be displayed preferentially. Furthermore, when the user is excited, high-risk warnings can be displayed preferentially. In this way, by determining the priority of warnings according to the user's emotion, important warnings can be displayed preferentially. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the warning.

[0089] The warning unit can generate a device-specific warning message in consideration of the device information of the user at the time of warning. The warning unit, for example, generates a device-specific warning message in consideration of the device information of the user at the time of warning. The device-specific warning message includes, for example, a message according to the vulnerability of the device, but is not limited to such an example. For example, when the user is using a smartphone, the warning unit generates a warning message optimized for the smartphone. In addition, when the user is using a tablet, the warning unit can also generate a warning message optimized for the tablet. Furthermore, when the user is using a personal computer, the warning unit can also generate a warning message optimized for the personal computer. Thereby, by generating a device-specific warning message, it is possible to provide an appropriate warning according to the device. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the device information of the user to the generation AI, and the generation AI can generate a warning message.

[0090] The warning unit can generate a region-specific warning message in consideration of the geographical location information of the user at the time of warning. The warning unit generates a region-specific warning message in consideration of the geographical location information of the user at the time of warning, for example. The region-specific warning message includes, for example, a message according to the crime trend of the region, but is not limited to such an example. For example, when the user is in a specific region, the warning unit generates a warning message regarding a crime risk specific to the region. In addition, when the user is moving, the warning unit can also generate a warning message regarding a risk specific to the destination region. Furthermore, when the user is participating in a specific event, the warning unit can also generate a warning message regarding a risk specific to the event. As a result, by generating a region-specific warning message, it is possible to provide an appropriate warning according to the region. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the geographical location information of the user to the generation AI, and the generation AI can generate a warning message.

[0091] The conversation unit can estimate the user's emotion and adjust the tone and content of the conversation based on the estimated user's emotion. The conversation unit, for example, estimates the user's emotion and adjusts the tone and content of the conversation based on the estimated user's emotion. The tone and content of the conversation include, for example, changing the tone and selecting the content according to the user's emotion, but are not limited to such examples. For example, when the user feels anxious, the conversation can be conducted in a gentle tone with content that gives a sense of security. In addition, when the user is relaxed, the conversation can be conducted in a friendly tone to provide detailed information. Furthermore, when the user is excited, the conversation can be conducted in a calm tone with content that encourages calmness. This enables more effective communication by adjusting the tone and content of the conversation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotional data into the generation AI, which can then adjust the tone and content of the conversation.

[0092] The conversation unit can provide an optimal answer by referring to the user's past question history during a conversation. The conversation unit can provide an optimal answer by referring to the user's past question history during a conversation, for example. The past question history includes, but is not limited to, a chat log and a question database. For example, a related answer is provided based on the content of a question asked by the user in the past. The conversation unit can also select and provide the most appropriate answer from the user's past question history. Furthermore, the conversation unit can analyze the user's past question history and prepare an answer to a predicted question. In this way, the optimal answer can be provided to the user by referring to the past question history. A part or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI or can be performed without using AI. For example, the conversation unit can input the user's question history data to the generation AI, and the generation AI can select the optimal answer.

[0093] The conversation unit can provide appropriate advice in real time according to the user's current situation during conversation. The conversation unit, for example, provides appropriate advice in real time according to the user's current situation during conversation. The current situation includes, for example, real-time data and the user's behavioral situation, but is not limited to such examples. For example, a solution is provided in real time for a problem that the user is currently facing. The conversation unit can also provide appropriate advice in real time based on the user's current behavior. Furthermore, the conversation unit can analyze the user's current situation and provide optimal advice in real time. This enables a quick response by providing advice according to the user's current situation in real time. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's current situation data to the generation AI and provide advice by the generation AI.

[0094] The conversation unit can estimate the user's emotion and determine the priority of the conversation based on the estimated user's emotion. The conversation unit can estimate the user's emotion and determine the priority of the conversation based on the estimated user's emotion. The priority of the conversation includes, for example, the urgency and importance of the user, but is not limited to such examples. For example, when the user feels anxious, important conversations can be prioritized. Furthermore, when the user is relaxed, general conversations can be prioritized. Furthermore, when the user is excited, high-risk conversations can be prioritized. In this way, important conversations can be prioritized by determining the priority of the conversation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotion data to the generation AI and the generation AI can determine the priority of the conversation.

[0095] The conversation unit can provide device-specific advice in consideration of the device information of the user during conversation. The conversation unit can provide device-specific advice in consideration of the device information of the user during conversation, for example. The device-specific advice includes, for example, advice according to the settings of the device, but is not limited to such an example. For example, if the user is using a smartphone, the conversation unit can provide advice optimized for the smartphone. In addition, if the user is using a tablet, the conversation unit can also provide advice optimized for the tablet. Furthermore, if the user is using a personal computer, the conversation unit can also provide advice optimized for the personal computer. As a result, by providing device-specific advice, appropriate advice according to the device is possible. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the device information of the user to the generation AI, and the generation AI can provide advice.

[0096] The conversation unit can provide region-specific advice in consideration of the geographical location information of the user during conversation. The conversation unit can provide region-specific advice in consideration of the geographical location information of the user during conversation. Region-specific advice includes, for example, advice according to the characteristics of the region, but is not limited to such examples. For example, when the user is in a specific region, the conversation unit provides advice on risks specific to the region. In addition, when the user is moving, the conversation unit can also provide advice on risks specific to the destination region. Furthermore, when the user is participating in a specific event, the conversation unit can also provide advice on risks specific to the event. As a result, by providing region-specific advice, appropriate advice according to the region is possible. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the geographical location information of the user to the generation AI, and the generation AI can provide advice.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The crime prevention system may further include a risk assessment unit that analyzes the user's behavioral patterns and evaluates whether a particular behavior increases the risk of crime. For example, the risk assessment unit may analyze the usage history of websites and applications frequently visited by the user to identify high-risk behavior. The risk assessment unit may also monitor the user's online transaction history to detect signs of fraudulent transactions. Furthermore, the risk assessment unit may analyze the user's social media activities to evaluate the risk of fraud and phishing. This allows the system to evaluate the risk based on the user's behavioral patterns and take appropriate measures.

[0099] The crime prevention system may further include an emotion prediction unit that estimates the emotion of the user and predicts the user's behavior based on the estimated emotion. For example, the emotion prediction unit predicts that the user is likely to take risky behavior when the user is stressed. The emotion prediction unit may also predict that the user is likely to take safe behavior when the user is relaxed. The emotion prediction unit may also predict that the user is likely to take impulsive behavior when the user is excited. This makes it possible to predict behavior based on the user's emotion and take appropriate measures.

[0100] The crime prevention system may further include an anomaly detection unit that analyzes the user's behavior history and detects abnormal behavior based on past behavior patterns. For example, the anomaly detection unit may analyze the user's past search history and detect abnormal search patterns. The anomaly detection unit may also analyze the user's past posting history and detect abnormal posting patterns. The anomaly detection unit may also analyze the user's past communication history and detect abnormal communication patterns. This allows abnormal behavior to be detected based on past behavior patterns, enabling rapid response.

[0101] The crime prevention system can further include a device monitoring unit that takes into account the user's device information and monitors risks specific to the device. For example, the device monitoring unit monitors the security settings of the device used by the user and detects risks. The device monitoring unit can also monitor the network connection status of the device used by the user and detect risks. Furthermore, the device monitoring unit can monitor the application usage status of the device used by the user and detect risks. This makes it possible to monitor risks specific to the device and take security measures according to the device.

[0102] The crime prevention system can further include a region monitoring unit that monitors region-specific risks by taking into account the geographical location information of the user. For example, when the user is in a particular region, the region monitoring unit monitors the crime risks specific to that region. In addition, when the user is traveling, the region monitoring unit can also monitor the risks specific to the region to which the user travels. Furthermore, when the user is participating in a particular event, the region monitoring unit can also monitor the risks specific to that event. This makes it possible to monitor region-specific risks and implement security measures according to the region.

[0103] The crime prevention system may further include a warning adjustment unit that estimates the user's emotions and adjusts the way the warning is expressed based on the estimated emotions. For example, if the user feels anxious, the warning adjustment unit displays a warning in gentle words. If the user feels relaxed, the warning adjustment unit may also display a warning including detailed information. If the user feels excited, the warning adjustment unit may also display a warning encouraging the user to stay calm. This allows the way the warning is expressed to be adjusted according to the user's emotions, enabling effective warnings.

[0104] The crime prevention system may further include a warning selection unit that refers to the user's past behavior history and selects the most appropriate warning content. For example, if the user has performed a similar behavior in the past, the warning selection unit selects the warning content based on that behavior. The warning selection unit may also display a warning for high-risk behavior based on the user's past behavior history. Furthermore, the warning selection unit may analyze the user's past behavior history and select the most effective warning content. In this way, the most appropriate warning content can be selected by referring to the past behavior history.

[0105] The crime prevention system may further include a conversation adjustment unit that estimates the user's emotions and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the user feels anxious, the conversation adjustment unit may conduct the conversation in a gentle tone with content that gives the user a sense of security. If the user feels relaxed, the conversation adjustment unit may also conduct the conversation in a friendly tone with detailed information. If the user feels excited, the conversation adjustment unit may also conduct the conversation in a calm tone with content that encourages the user to stay calm. This allows the tone and content of the conversation to be adjusted according to the user's emotions, enabling more effective communication.

[0106] The crime prevention system may further include an answer selection unit that provides the most appropriate answer by referring to the user's past question history. For example, the answer selection unit provides a related answer based on the content of questions asked by the user in the past. The answer selection unit may also select and provide the most appropriate answer from the user's past question history. Furthermore, the answer selection unit may analyze the user's past question history and prepare answers to predicted questions. In this way, the most appropriate answer can be provided to the user by referring to the past question history.

[0107] The crime prevention system may further include a conversation priority unit that estimates the user's emotions and determines the priority of conversations based on the estimated emotions. For example, the conversation priority unit may prioritize important conversations when the user feels anxious. The conversation priority unit may also prioritize general conversations when the user feels relaxed. The conversation priority unit may also prioritize risky conversations when the user feels excited. In this way, the priority of conversations can be determined according to the user's emotions, and important conversations can be prioritized.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The learning module learns from past incident and crime data. Past incident and crime data includes police reports, news articles, court records, etc. The learning module uses machine learning algorithms and data mining techniques to extract useful information from this data. Step 2: The monitoring unit monitors user behavior based on the data learned by the learning unit. The monitoring unit analyzes the keywords searched by users, the content of posts, and the content of communications in real time. For example, it collects and analyzes search engine logs, browser history, social media posts, forum comments, chat logs, and email content. Step 3: The warning unit posts a warning based on the user's behavior monitored by the monitoring unit. If the user's behavior indicates a crime, the warning unit posts a warning about the possibility of a crime, the expected name of the crime and the amount of damage, and how to avoid the crime. For example, the warning is posted using a pop-up message or an email notification. Step 4: The conversation unit performs an interactive conversation based on the warning posted by the warning unit. When the user inputs a question, the conversation unit provides an appropriate answer. For example, an interactive conversation is performed using a chatbot or a voice assistant.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the learning unit, monitoring unit, warning unit, and conversation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past incidents, accidents, and crime data. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the user's behavior. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and posts a warning based on the user's behavior. The conversation unit is realized, for example, by the control unit 46A of the smart device 14, and performs interactive conversation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the learning unit, monitoring unit, warning unit, and conversation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past incidents, accidents, and crime data. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the user's behavior. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and posts a warning based on the user's behavior. The conversation unit is realized, for example, by the control unit 46A of the smart glasses 214, and performs interactive conversation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the learning unit, monitoring unit, warning unit, and conversation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past incident and accident data and crime data. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the user's behavior. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and posts a warning based on the user's behavior. The conversation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and performs interactive conversation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] Each of the multiple elements including the learning unit, monitoring unit, warning unit, and conversation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past incidents, accidents, and crime data. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the user's behavior. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and posts a warning based on the user's behavior. The conversation unit is realized, for example, by the control unit 46A of the robot 414, and performs interactive conversation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A learning section that studies past incidents, accidents and crime data, A monitoring unit that monitors user behavior based on the data learned by the learning unit; a warning unit that issues a warning based on the user's behavior monitored by the monitoring unit; A conversation unit that performs an interactive conversation based on the warning posted by the warning unit. A system characterized by: (Appendix 2) The learning unit is Learn data on crime types, locations, damage amounts, and methods of crime 2. The system of claim 1, (Appendix 3) The monitoring unit is Analyze users' search keywords, posts, and communication content in real time 2. The system of claim 1, (Appendix 4) The warning unit is If a user's behavior indicates a crime, the system will display information about the possibility of a crime, the name of the crime and the amount of damage, and how to avoid the crime. 2. The system of claim 1, (Appendix 5) The conversation unit includes: Users enter a question and it provides the appropriate answer 2. The system of claim 1, (Appendix 6) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 2. The system of claim 1, (Appendix 7) The learning unit is During training, specific patterns are extracted from past crime data to optimize the crime prediction model. 2. The system of claim 1, (Appendix 8) The learning unit is During training, the system takes into account changes in crime data over time and learns to respond to the latest criminal methods. 2. The system of claim 1, (Appendix 9) The learning unit is Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The learning unit is During training, the system compares crime data from different regions to learn about region-specific crime trends. 2. The system of claim 1, (Appendix 11) The learning unit is During training, data related to specific age groups and genders is extracted from the crime data, and training is specialized for the target groups. 2. The system of claim 1, (Appendix 12) The monitoring unit is Estimating a user's emotion and adjusting the monitoring intensity based on the estimated user's emotion 2. The system of claim 1, (Appendix 13) The monitoring unit is During monitoring, the user's behavior history is analyzed and abnormal behavior is detected based on past behavior patterns. 2. The system of claim 1, (Appendix 14) The monitoring unit is During monitoring, take into account user device information to monitor device-specific risks 2. The system of claim 1, (Appendix 15) The monitoring unit is Estimate the user's emotions and determine the priority of actions to be monitored based on the estimated user's emotions. 2. The system of claim 1, (Appendix 16) The monitoring unit is During monitoring, analyze users' social media activities and monitor associated risks 2. The system of claim 1, (Appendix 17) The monitoring unit is During monitoring, the geographic location of the user is taken into account to monitor region-specific risks 2. The system of claim 1, (Appendix 18) The warning unit is Infer user emotions and adjust warning wording based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The warning unit is When issuing a warning, the system refers to the user's past behavior history to select the most appropriate warning content. 2. The system of claim 1, (Appendix 20) The warning unit is When alerted, generate different warning messages depending on the type of crime 2. The system of claim 1, (Appendix 21) The warning unit is Infer user emotions and prioritize alerts based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The warning unit is When alerting, it takes into account the user's device information to generate device-specific alert messages 2. The system of claim 1, (Appendix 23) The warning unit is At the time of alert, it takes into account the user's geographic location to generate region-specific alert messages. 2. The system of claim 1, (Appendix 24) The conversation unit includes: Infer user emotions and adjust the tone and content of conversations based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The conversation unit includes: During a conversation, the system references the user's past question history to provide the most appropriate answer 2. The system of claim 1, (Appendix 26) The conversation unit includes: During a conversation, provide relevant advice in real time based on the user's current situation 2. The system of claim 1, (Appendix 27) The conversation unit includes: Estimate user emotions and prioritize conversations based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The conversation unit includes: During a conversation, take into account the user's device information to provide device-specific advice 2. The system of claim 1, (Appendix 29) The conversation unit includes: During a conversation, the app takes into account the user's geographic location to provide location-specific advice 2. The system of claim 1, [Explanation of symbols]

[0182] 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 learning section that studies past incidents, accidents and crime data, A monitoring unit that monitors user behavior based on the data learned by the learning unit; a warning unit that issues a warning based on the user's behavior monitored by the monitoring unit; A conversation unit that performs an interactive conversation based on the warning posted by the warning unit. A system characterized by:

2. The learning unit is Learn data on crime types, locations, damage amounts, and methods of crime 2. The system of claim 1 .

3. The monitoring unit is Analyze users' search keywords, posts, and communication content in real time 2. The system of claim 1 .

4. The warning unit is If a user's behavior indicates a crime, the system will display information about the possibility of a crime, the name of the crime and the amount of damage, and how to avoid the crime.

2. The system of claim 1 .

5. The conversation unit includes: Users enter a question and it provides the appropriate answer 2. The system of claim 1 .

6. The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions.

2. The system of claim 1 .

7. The learning unit is During training, specific patterns are extracted from past crime data to optimize the crime prediction model.

2. The system of claim 1 .

8. The learning unit is During training, the system takes into account changes in crime data over time and learns to respond to the latest criminal methods.

2. The system of claim 1 .

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