system

Abstract

The system according to this embodiment aims to provide appropriate advice in response to questions about crime prevention, identify crime risks, detect anomalies in real time, and instruct appropriate responses in emergencies. [Solution] The system according to this embodiment comprises an advice provision unit, a risk identification unit, an anomaly detection unit, and an emergency response unit. The advice provision unit provides appropriate advice to the user's questions regarding crime prevention. The risk identification unit identifies crime risks by analyzing past crime data. The anomaly detection unit detects anomalies by analyzing security camera footage in real time. The emergency response unit analyzes voice input from the user in an emergency and instructs appropriate action.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that providing appropriate advice for crime prevention-related questions, identifying crime risks, detecting abnormalities in real time, and instructing appropriate responses in an emergency have been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate advice for crime prevention-related questions, identify crime risks, detect abnormalities in real time, and instruct appropriate responses in an emergency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an advice provision unit, a risk identification unit, an anomaly detection unit, and an emergency response unit. The advice provision unit provides appropriate advice to the user's questions regarding crime prevention. The risk identification unit identifies crime risks by analyzing past crime data. The anomaly detection unit detects anomalies by analyzing security camera footage in real time. The emergency response unit analyzes voice input from the user in an emergency and instructs appropriate actions. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice in response to questions about crime prevention, identify crime risks, detect anomalies in real time, and instruct appropriate responses in emergencies. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The crime prevention system according to an embodiment of the present invention is a system that enhances crime prevention measures using an AI agent. This crime prevention system supports crime prevention advice, risk detection, and emergency response tailored to individual users and households. The crime prevention system works in conjunction with security cameras and sensors to automatically issue warnings when it detects abnormal behavior or suspicious persons. This mechanism allows users to live their daily lives with peace of mind. The crime prevention system provides appropriate advice to users' questions about crime prevention. For example, if a user asks, "What precautions should I take when going out at night?", the crime prevention system will provide advice such as "Choose well-lit areas to walk in, don't show valuables, and pay attention to your surroundings." In this case, it uses natural language processing technology to understand the user's question and generate an appropriate answer. The crime prevention system analyzes past crime data to identify and notify users of high-risk locations and times for crime. For example, if there is a high incidence of crime at night in a particular area, the crime prevention system will notify the user of this information and urge them to be careful. In this case, it uses machine learning technology to analyze past data and predict crime risk. The security system analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the security system analyzes the footage, detects the anomaly, and issues a warning. Computer vision technology is used to analyze the footage. In emergencies, the security system quickly understands voice input from the user and provides appropriate instructions. For example, if a user shouts "Help!", the security system recognizes the voice and notifies the police or emergency contacts. Voice recognition technology is used to analyze the user's voice and understand the situation. This mechanism allows the security system to provide real-time security measures to users, supporting a safe and secure daily life. For example, it can provide effective support to targets who require special security measures, such as children, women, and the elderly. In this way, the security system can strengthen users' security measures and support a safe and secure daily life.

[0029] The crime prevention system according to this embodiment comprises an advice provision unit, a risk identification unit, an anomaly detection unit, and an emergency response unit. The advice provision unit provides appropriate advice to the user's questions regarding crime prevention. For example, if the user asks, "What precautions should I take when going out at night?", the advice provision unit will provide advice such as, "Choose well-lit areas to walk in, do not show valuables, and pay attention to your surroundings." The advice provision unit uses natural language processing technology to understand the user's questions and generate appropriate answers. The risk identification unit identifies crime risks by analyzing past crime data. For example, if there is a high incidence of crime at night in a particular area, the risk identification unit will notify the user of this information and warn them to be careful. The risk identification unit uses machine learning technology to analyze past data and predict crime risks. The anomaly detection unit detects anomalies by analyzing security camera footage in real time. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit will analyze the footage, detect the anomaly, and issue a warning. The anomaly detection unit analyzes video using computer vision technology. The emergency response unit analyzes voice input from the user in an emergency and instructs appropriate action. For example, if the user shouts "Help!", the emergency response unit recognizes the voice and notifies the police or emergency contacts. The emergency response unit analyzes the user's voice using voice recognition technology to understand the situation. As a result, the crime prevention system according to this embodiment can enhance the user's crime prevention measures and support a safe and secure daily life.

[0030] The advice service provides appropriate advice to users regarding their crime prevention questions. For example, if a user asks, "What precautions should I take when going out at night?", the advice service will provide advice such as, "Choose well-lit areas to walk in, don't show valuables, and be aware of your surroundings." The advice service utilizes natural language processing technology to understand user questions and generate appropriate answers. Specifically, the advice service analyzes user questions and performs morphological and contextual analysis to understand the intent behind the questions. This identifies what kind of crime prevention measures the user's question relates to. Next, the advice service refers to past databases and expert knowledge bases to generate optimal advice. For example, if a user asks, "What crime prevention measures should I take at home?", the advice service will suggest specific measures such as "installing security cameras, locking windows and doors, and implementing a security system." Furthermore, the advice service can learn from users' past question history and behavioral patterns to provide advice optimized for each individual user. This allows the advice service to raise users' crime prevention awareness and encourage concrete actions. Furthermore, the advice provision department can collect feedback from users and continuously improve the accuracy and usefulness of its advice. For example, by providing feedback such as "it was helpful" or "I would like more specific information" regarding the advice provided, the advice provision department can review the content and adjust it to generate more effective advice. In this way, the advice provision department can provide flexible and effective crime prevention advice that meets the user's needs and ensures the user's safety.

[0031] The Risk Identification Unit identifies crime risks by analyzing past crime data. For example, if crime is frequent at night in a particular area, the Risk Identification Unit will notify users of this information and warn them to be careful. The Risk Identification Unit uses machine learning techniques to analyze past data and predict crime risks. Specifically, the Risk Identification Unit collects past crime data and stores it in a database. This data includes detailed information such as the location, time, type, and extent of damage of crimes. Next, the Risk Identification Unit analyzes this data and applies machine learning algorithms such as clustering and regression analysis to identify crime patterns and trends. For example, if crime is frequent in a particular area during a particular time period, the Unit performs a risk assessment for that area and time period and notifies users. Furthermore, the Risk Identification Unit can continuously monitor fluctuations in crime risk based on real-time updated data and provide the latest risk information. In addition, the Risk Identification Unit can perform individual risk assessments considering the user's location information and behavioral patterns. For example, if a user frequently visits a particular area, the Unit will prioritize providing risk information for that area and issue specific warnings to the user. This allows the risk identification unit to provide appropriate risk information to ensure user safety and raise user awareness of crime prevention. Furthermore, the risk identification unit can collaborate with other security systems and devices to achieve comprehensive crime prevention measures. For example, the risk identification unit can collaborate with security cameras and security systems to detect anomalies in real time and respond quickly. In this way, the risk identification unit can comprehensively support user safety and minimize the risk of crime.

[0032] The anomaly detection unit analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit analyzes the footage, detects the anomaly, and issues a warning. The anomaly detection unit utilizes computer vision technology to analyze the footage. Specifically, the anomaly detection unit receives video data transmitted from security cameras in real time and applies object detection algorithms and motion detection algorithms to detect movement and objects in the footage. For example, the anomaly detection unit uses background subtraction or optical flow methods to detect movement in the footage and identify suspicious movements or abnormal behavior. The anomaly detection unit also uses deep learning technology to identify people and objects in the footage and identify suspicious persons or dangerous objects. For example, the anomaly detection unit uses facial recognition technology to identify the faces of people in the footage and detect suspicious persons by comparing them with a list of previously registered suspicious persons. Furthermore, the anomaly detection unit can integrate and analyze footage from multiple cameras to improve the accuracy of anomaly detection. For example, the anomaly detection unit integrates video from multiple cameras and detects anomalies across a wide field of view, thereby achieving more accurate anomaly detection. Furthermore, the anomaly detection unit can immediately issue warnings and notify users and security personnel of detected anomalies. For instance, when an anomaly is detected, the unit can sound an alarm or send a notification to a smartphone to encourage a quick response. This allows the anomaly detection unit to detect anomalies in real time and support a rapid and appropriate response. Additionally, the anomaly detection unit can accumulate data on detected anomalies and utilize it for later analysis and improvement. For example, the anomaly detection unit can learn from past anomaly detection data to improve the accuracy of its anomaly detection algorithm. This allows the anomaly detection unit to continuously improve the accuracy of anomaly detection, enhancing the overall reliability and security of the system.

[0033] The emergency response unit analyzes voice input from users in emergencies and directs appropriate responses. For example, if a user shouts "Help!", the emergency response unit recognizes the voice and notifies the police and emergency contacts. The emergency response unit uses voice recognition technology to analyze the user's voice and understand the situation. Specifically, the emergency response unit receives voice input from users in real time and applies a voice recognition algorithm to convert the voice data into text. Next, the emergency response unit analyzes the converted text data and uses natural language processing technology to determine the nature and urgency of the emergency. For example, the emergency response unit extracts keywords such as "Help," "Fire," and "Robbery" from the voice input to identify the type of emergency. The emergency response unit can also analyze the tone and emotion of the voice to assess the user's urgency. This allows the emergency response unit to accurately grasp the user's situation and direct appropriate responses. Furthermore, the emergency response unit can acquire the user's location information to expedite the emergency response. For example, the emergency response unit can use the GPS function of the user's smartphone to determine the user's current location and notify the nearest police station or emergency contact. The emergency response unit can also refer to the user's past activity history and emergency contact information to provide a swift and appropriate response. This allows the emergency response unit to ensure the user's safety and support a rapid response. Furthermore, the emergency response unit can provide follow-up advice and support to the user after the emergency. For example, after an emergency, the emergency response unit can advise the user to "evacuate to a safe place" or "wait until the police arrive," ensuring the user's safety. The emergency response unit can also accumulate emergency data and use it for future analysis and improvement. This allows the emergency response unit to continuously improve the accuracy and effectiveness of emergency responses, enhancing the overall reliability and security of the system.

[0034] The security system includes a coordinating unit that works in conjunction with security cameras and sensors. The coordinating unit improves the accuracy of anomaly detection by working with security cameras and sensors. For example, the coordinating unit analyzes video data from security cameras in real time to detect anomalies. It can also analyze sensor data in real time to detect anomalies. Furthermore, it can integrate and analyze data from security cameras and sensors to improve the accuracy of anomaly detection. This improves the accuracy of anomaly detection through collaboration with security cameras and sensors. Some or all of the above-described processes in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input video data from security cameras into a generating AI and have the generating AI perform anomaly detection.

[0035] The security system includes a voice analysis unit that analyzes the user's voice input. By analyzing the user's voice input, the voice analysis unit enables a quick and appropriate response in emergencies. For example, if a user shouts "Help!", the voice analysis unit recognizes the voice and notifies the police or emergency contacts. The voice analysis unit uses voice recognition technology to analyze the user's voice and understand the situation. For example, the voice analysis unit analyzes the user's voice data in real time and gives instructions for emergency response. The voice analysis unit can also analyze the voice data and estimate the user's emotions. This enables a quick and appropriate response in emergencies by analyzing the user's voice input. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the user's voice data into a generating AI and have the generating AI perform the voice analysis.

[0036] The advice-providing unit can analyze a user's past question history and provide individually customized advice. For example, the advice-providing unit can provide new, relevant advice based on the content of questions the user has asked in the past. The advice-providing unit can also evaluate the effectiveness of advice the user has received in the past and provide advice that reflects areas for improvement. The advice-providing unit can also identify specific patterns from the user's past question history and provide advice based on those patterns. This makes it possible to implement more effective security measures by providing advice based on the user's past question history. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the user's question history data into a generating AI and have the generating AI generate customized advice.

[0037] The advice-providing unit can provide region-specific crime prevention advice, taking into account the user's current location. For example, if the user is in a specific area, the advice-providing unit can provide advice based on crime trends in that area. If the user is traveling, the advice-providing unit can also provide crime prevention measures for the area they are visiting. If the user is at home, the advice-providing unit can also provide advice based on crime prevention information around their home. This allows for addressing region-specific risks by providing crime prevention advice based on the user's current location. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not. For example, the advice-providing unit can input the user's location data into a generating AI and have the generating AI generate region-specific crime prevention advice.

[0038] The advice service can analyze a user's social media activity and provide relevant crime prevention advice. For example, the advice service can provide privacy protection advice based on information a user has made public on social media. If a user has shared on social media their plans to attend a specific event, the advice service can also provide crime prevention advice related to that event. The advice service can also predict specific risks from a user's social media activity and provide advice based on those predictions. This enables privacy protection and risk avoidance by providing crime prevention advice based on a user's social media activity. Some or all of the above processes in the advice service may be performed using AI, for example, or not using AI. For example, the advice service can input a user's social media data into a generating AI and have the generating AI generate relevant crime prevention advice.

[0039] The advice-providing unit can provide targeted advice based on the user's family structure and lifestyle. For example, if the user has children, the advice-providing unit can provide advice on child safety. If the user lives alone, the advice-providing unit can also provide crime prevention advice tailored to single-person households. If the user is elderly, the advice-providing unit can also provide crime prevention advice tailored to the elderly. By providing crime prevention advice based on the user's family structure and lifestyle, more effective crime prevention measures become possible. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the user's family structure data and lifestyle data into a generating AI and have the generating AI generate targeted advice.

[0040] The risk identification unit can provide up-to-date risk information by analyzing real-time news and social media information in addition to historical crime data. For example, the risk identification unit can analyze real-time news information to identify the latest crime risks. The risk identification unit can also analyze social media posts to predict crime risks in specific areas. The risk identification unit can also provide more accurate risk information by combining historical crime data with real-time information. This allows the system to provide up-to-date risk information by analyzing real-time information. Some or all of the above-described processes in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input news feeds and social media data into a generating AI and have the generating AI generate the latest risk information.

[0041] The risk identification unit can learn the user's behavior patterns and perform individually customized risk predictions. For example, the risk identification unit can predict risks at specific times and locations based on the user's past behavior patterns. The risk identification unit can also analyze the user's travel history and predict risks along specific routes. The risk identification unit can also perform individually customized risk predictions considering the user's lifestyle. This enables more effective crime prevention measures by performing risk predictions based on the user's behavior patterns. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input user behavior pattern data into a generating AI and have the generating AI perform customized risk predictions.

[0042] The risk identification unit can provide region-specific risk information, taking into account the user's geographical location. For example, if the user is in a specific region, the risk identification unit can provide risk information based on crime trends in that region. If the user is traveling, the risk identification unit can also provide risk information for the region they are visiting. If the user is at home, the risk identification unit can also provide notifications based on risk information around their home. This allows for addressing region-specific risks by providing risk information based on the user's geographical location. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input the user's geographical location data into a generating AI and have the generating AI generate region-specific risk information.

[0043] The risk identification unit can analyze the user's past security measures history and propose effective risk avoidance measures. For example, the risk identification unit can evaluate the effectiveness of security measures previously implemented by the user and propose areas for improvement. The risk identification unit can also propose effective avoidance measures for specific risks based on the user's past security measures history. The risk identification unit can also propose individually customized risk avoidance measures based on the user's security measures history. This enables more effective security measures by proposing risk avoidance measures based on the user's past security measures history. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input the user's security measures history data into a generating AI and have the generating AI generate effective risk avoidance measures.

[0044] The anomaly detection unit can improve the accuracy of anomaly detection by analyzing not only security camera footage but also audio and temperature sensor data. For example, the anomaly detection unit can detect anomalies by combining security camera footage and audio data. The anomaly detection unit can also detect anomalies by combining security camera footage and temperature sensor data. The anomaly detection unit can also improve the accuracy of anomaly detection by integrating security camera footage, audio data, and temperature sensor data. As a result, the accuracy of anomaly detection is improved by analyzing audio and temperature sensor data in addition to security camera footage. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input security camera footage, audio data, and temperature sensor data into a generating AI and have the generating AI perform anomaly detection.

[0045] The anomaly detection unit can take a quicker and more accurate response when an anomaly is detected by referring to past anomaly detection history. For example, the anomaly detection unit can identify anomaly patterns based on past anomaly detection history and take a rapid response. The anomaly detection unit can also analyze the anomaly detection history and take measures to reduce false positives. The anomaly detection unit can also refer to the anomaly detection history to provide the optimal countermeasure for a specific anomaly. This makes a quicker and more accurate response possible by referring to past anomaly detection history. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input past anomaly detection history data into a generating AI and have the generating AI perform the generation of a quick and accurate countermeasure.

[0046] The anomaly detection unit can detect anomalies in real time and notify the user. For example, the anomaly detection unit can analyze security camera footage in real time and detect anomalies. When the anomaly detection unit detects an anomaly, it can immediately notify the user. By detecting anomalies in real time and notifying the user, the anomaly detection unit enables a rapid response. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input security camera footage into a generating AI and have the generating AI perform real-time anomaly detection.

[0047] The emergency response unit can provide the optimal response in an emergency by referring to the user's past emergency response history. For example, the emergency response unit can provide the optimal response based on the user's history of past emergencies. The emergency response unit can also propose effective response measures based on the user's past emergency response history. The emergency response unit can also analyze the user's emergency response history and provide response measures that reflect areas for improvement. This enables a quick and appropriate response by providing the optimal response measures based on the user's past emergency response history. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's emergency response history data into a generating AI and have the generating AI generate the optimal response measures.

[0048] The emergency response unit can, in an emergency, take into account the user's current location and notify the nearest police station or emergency contact. For example, if the user is at home, the emergency response unit will notify the nearest police station. If the user is out, the emergency response unit can also notify the nearest police station based on their current location. If the user is traveling, the emergency response unit can also notify the nearest police station in the area they are visiting. This enables a rapid response by notifying the nearest police station or emergency contact based on the user's current location. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or not using AI. For example, the emergency response unit can input the user's location data into a generating AI and have the generating AI execute the notification to the nearest police station or emergency contact.

[0049] The emergency response unit can simultaneously notify the user's family and emergency contacts in the event of an emergency. For example, the emergency response unit can automatically notify the user's family in the event of an emergency. The emergency response unit can also automatically notify the user's emergency contacts in the event of an emergency. The emergency response unit can also simultaneously notify the user's family and emergency contacts in the event of an emergency to encourage a swift response. This enables a swift response by simultaneously notifying the user's family and emergency contacts in the event of an emergency. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's emergency contact data into a generating AI and have the generating AI execute the notification.

[0050] The emergency response unit can provide the optimal response in an emergency, taking into account the user's device information. For example, if the user is using a smartphone, the emergency response unit can provide a response optimized for the smartphone. If the user is using a tablet, the emergency response unit can also provide a response optimized for the tablet. If the user is using a smartwatch, the emergency response unit can also provide a response optimized for the smartwatch. This enables a quick and appropriate response by providing the optimal response based on the user's device information. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's device information into a generating AI and have the generating AI generate the optimal response.

[0051] The integration unit can analyze data from security cameras and sensors in real time to improve the accuracy of anomaly detection. For example, the integration unit can analyze video data from security cameras in real time to detect anomalies. The integration unit can also analyze sensor data in real time to detect anomalies. The integration unit can also integrate and analyze data from security cameras and sensors to improve the accuracy of anomaly detection. As a result, the accuracy of anomaly detection is improved by analyzing data from security cameras and sensors in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input video data from security cameras and sensor data into a generating AI and have the generating AI perform real-time anomaly detection.

[0052] The integration unit can save security camera and sensor data to the cloud for later analysis. For example, the integration unit can save security camera video data to the cloud for later analysis. The integration unit can also save sensor data to the cloud for later analysis. The integration unit can save security camera and sensor data to the cloud and integrate and analyze it later. This makes it possible to analyze security camera and sensor data later by saving it to the cloud. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input security camera video data and sensor data into a generating AI and have the generating AI perform the process of saving it to the cloud.

[0053] The audio analysis unit can improve the accuracy of the analysis by removing background noise and other noise during audio analysis. For example, the audio analysis unit can improve the accuracy of the analysis by removing background noise during audio analysis. The audio analysis unit can also improve the accuracy of the analysis by removing noise during audio analysis. The audio analysis unit can also improve the accuracy of the analysis by simultaneously removing background noise and other noise during audio analysis. As a result, the accuracy of the analysis is improved by removing background noise and other noise during audio analysis. Some or all of the above processing in the audio analysis unit may be performed using AI, for example, or without using AI. For example, the audio analysis unit can input audio data into a generating AI and have the generating AI perform the removal of background noise and other noise.

[0054] The voice analysis unit can improve the accuracy of its analysis by referring to the user's past voice data during voice analysis. For example, the voice analysis unit can improve the accuracy of its analysis based on the user's past voice data. The voice analysis unit can also improve the accuracy of its analysis by referring to the user's past voice data to find specific patterns. The voice analysis unit can also improve the accuracy of noise reduction by analyzing the user's past voice data. As a result, the accuracy of the analysis is improved by referring to the user's past voice data. Some or all of the above processes in the voice analysis unit may be performed using AI, for example, or without using AI. For example, the voice analysis unit can input the user's past voice data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] Security systems can learn users' behavioral patterns and provide individually customized security advice. For example, if a user frequently goes out during certain times, the system can provide security advice tailored to those times. If a user frequently uses a particular route, it can also provide security advice based on that route. Furthermore, it can analyze users' behavioral patterns, predict potential risks, and issue warnings in advance. This allows for more effective security measures by providing security advice based on users' behavioral patterns.

[0057] Security systems can monitor a user's health and provide crime prevention advice based on that health status. For example, if a user is tired, it can advise them to stay indoors. If a user is feeling unwell, it can also provide information about nearby medical facilities. Furthermore, if a user is feeling stressed, it can suggest ways to relax. In this way, by providing crime prevention advice based on the user's health status, it can support a safer life.

[0058] The security system can analyze a user's past security advice history and provide effective advice. For example, it can evaluate the effectiveness of past advice and provide advice that reflects areas for improvement. It can also provide new advice based on the results of security measures the user has taken in the past. Furthermore, it can identify specific patterns from a user's past security advice history and provide advice based on those patterns. In this way, by providing effective advice based on a user's past security advice history, it can support a safer life.

[0059] A security system can provide targeted crime prevention advice based on the user's family structure and lifestyle. For example, if the user has children, it can provide advice on child safety. If the user lives alone, it can provide crime prevention advice tailored to single-person households. Furthermore, if the user is elderly, it can provide crime prevention advice for seniors. By providing crime prevention advice based on the user's family structure and lifestyle, more effective crime prevention measures become possible.

[0060] A security system can analyze a user's social media activity and provide relevant security advice. For example, it can provide privacy protection advice based on information a user has made public on social media. If a user shares their plans to attend a specific event on social media, it can also provide security advice related to that event. Furthermore, it can predict specific risks from a user's social media activity and provide advice based on those risks. This enables privacy protection and risk avoidance by providing security advice based on a user's social media activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The advice-providing unit provides appropriate advice to users regarding safety-related questions. For example, if a user asks, "What precautions should I take when going out at night?", the advice-providing unit will provide advice such as, "Choose well-lit areas to walk in, don't show your valuables, and pay attention to your surroundings." The advice-providing unit uses natural language processing technology to understand the user's question and generate an appropriate answer. Step 2: The risk identification unit analyzes past crime data to identify crime risks. For example, if there is a high incidence of crime at night in a particular area, the risk identification unit will notify the user of this information and warn them to be careful. The risk identification unit uses machine learning technology to analyze past data and predict crime risks. Step 3: The anomaly detection unit analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit analyzes the footage, detects the anomaly, and issues a warning. The anomaly detection unit uses computer vision technology to analyze the footage. Step 4: The emergency response unit analyzes the user's voice input in an emergency and directs the appropriate response. For example, if the user shouts "Help!", the emergency response unit recognizes the voice and notifies the police or emergency contacts. The emergency response unit uses voice recognition technology to analyze the user's voice and understand the situation.

[0063] (Example of form 2) The crime prevention system according to an embodiment of the present invention is a system that enhances crime prevention measures using an AI agent. This crime prevention system supports crime prevention advice, risk detection, and emergency response tailored to individual users and households. The crime prevention system works in conjunction with security cameras and sensors to automatically issue warnings when it detects abnormal behavior or suspicious persons. This mechanism allows users to live their daily lives with peace of mind. The crime prevention system provides appropriate advice to users' questions about crime prevention. For example, if a user asks, "What precautions should I take when going out at night?", the crime prevention system will provide advice such as "Choose well-lit areas to walk in, don't show valuables, and pay attention to your surroundings." In this case, it uses natural language processing technology to understand the user's question and generate an appropriate answer. The crime prevention system analyzes past crime data to identify and notify users of high-risk locations and times for crime. For example, if there is a high incidence of crime at night in a particular area, the crime prevention system will notify the user of this information and urge them to be careful. In this case, it uses machine learning technology to analyze past data and predict crime risk. The security system analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the security system analyzes the footage, detects the anomaly, and issues a warning. Computer vision technology is used to analyze the footage. In emergencies, the security system quickly understands voice input from the user and provides appropriate instructions. For example, if a user shouts "Help!", the security system recognizes the voice and notifies the police or emergency contacts. Voice recognition technology is used to analyze the user's voice and understand the situation. This mechanism allows the security system to provide real-time security measures to users, supporting a safe and secure daily life. For example, it can provide effective support to targets who require special security measures, such as children, women, and the elderly. In this way, the security system can strengthen users' security measures and support a safe and secure daily life.

[0064] The crime prevention system according to this embodiment comprises an advice provision unit, a risk identification unit, an anomaly detection unit, and an emergency response unit. The advice provision unit provides appropriate advice to the user's questions regarding crime prevention. For example, if the user asks, "What precautions should I take when going out at night?", the advice provision unit will provide advice such as, "Choose well-lit areas to walk in, do not show valuables, and pay attention to your surroundings." The advice provision unit uses natural language processing technology to understand the user's questions and generate appropriate answers. The risk identification unit identifies crime risks by analyzing past crime data. For example, if there is a high incidence of crime at night in a particular area, the risk identification unit will notify the user of this information and warn them to be careful. The risk identification unit uses machine learning technology to analyze past data and predict crime risks. The anomaly detection unit detects anomalies by analyzing security camera footage in real time. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit will analyze the footage, detect the anomaly, and issue a warning. The anomaly detection unit analyzes video using computer vision technology. The emergency response unit analyzes voice input from the user in an emergency and instructs appropriate action. For example, if the user shouts "Help!", the emergency response unit recognizes the voice and notifies the police or emergency contacts. The emergency response unit analyzes the user's voice using voice recognition technology to understand the situation. As a result, the crime prevention system according to this embodiment can enhance the user's crime prevention measures and support a safe and secure daily life.

[0065] The advice service provides appropriate advice to users regarding their crime prevention questions. For example, if a user asks, "What precautions should I take when going out at night?", the advice service will provide advice such as, "Choose well-lit areas to walk in, don't show valuables, and be aware of your surroundings." The advice service utilizes natural language processing technology to understand user questions and generate appropriate answers. Specifically, the advice service analyzes user questions and performs morphological and contextual analysis to understand the intent behind the questions. This identifies what kind of crime prevention measures the user's question relates to. Next, the advice service refers to past databases and expert knowledge bases to generate optimal advice. For example, if a user asks, "What crime prevention measures should I take at home?", the advice service will suggest specific measures such as "installing security cameras, locking windows and doors, and implementing a security system." Furthermore, the advice service can learn from users' past question history and behavioral patterns to provide advice optimized for each individual user. This allows the advice service to raise users' crime prevention awareness and encourage concrete actions. Furthermore, the advice provision department can collect feedback from users and continuously improve the accuracy and usefulness of its advice. For example, by providing feedback such as "it was helpful" or "I would like more specific information" regarding the advice provided, the advice provision department can review the content and adjust it to generate more effective advice. In this way, the advice provision department can provide flexible and effective crime prevention advice that meets the user's needs and ensures the user's safety.

[0066] The Risk Identification Unit identifies crime risks by analyzing past crime data. For example, if crime is frequent at night in a particular area, the Risk Identification Unit will notify users of this information and warn them to be careful. The Risk Identification Unit uses machine learning techniques to analyze past data and predict crime risks. Specifically, the Risk Identification Unit collects past crime data and stores it in a database. This data includes detailed information such as the location, time, type, and extent of damage of crimes. Next, the Risk Identification Unit analyzes this data and applies machine learning algorithms such as clustering and regression analysis to identify crime patterns and trends. For example, if crime is frequent in a particular area during a particular time period, the Unit performs a risk assessment for that area and time period and notifies users. Furthermore, the Risk Identification Unit can continuously monitor fluctuations in crime risk based on real-time updated data and provide the latest risk information. In addition, the Risk Identification Unit can perform individual risk assessments considering the user's location information and behavioral patterns. For example, if a user frequently visits a particular area, the Unit will prioritize providing risk information for that area and issue specific warnings to the user. This allows the risk identification unit to provide appropriate risk information to ensure user safety and raise user awareness of crime prevention. Furthermore, the risk identification unit can collaborate with other security systems and devices to achieve comprehensive crime prevention measures. For example, the risk identification unit can collaborate with security cameras and security systems to detect anomalies in real time and respond quickly. In this way, the risk identification unit can comprehensively support user safety and minimize the risk of crime.

[0067] The anomaly detection unit analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit analyzes the footage, detects the anomaly, and issues a warning. The anomaly detection unit utilizes computer vision technology to analyze the footage. Specifically, the anomaly detection unit receives video data transmitted from security cameras in real time and applies object detection algorithms and motion detection algorithms to detect movement and objects in the footage. For example, the anomaly detection unit uses background subtraction or optical flow methods to detect movement in the footage and identify suspicious movements or abnormal behavior. The anomaly detection unit also uses deep learning technology to identify people and objects in the footage and identify suspicious persons or dangerous objects. For example, the anomaly detection unit uses facial recognition technology to identify the faces of people in the footage and detect suspicious persons by comparing them with a list of previously registered suspicious persons. Furthermore, the anomaly detection unit can integrate and analyze footage from multiple cameras to improve the accuracy of anomaly detection. For example, the anomaly detection unit integrates video from multiple cameras and detects anomalies across a wide field of view, thereby achieving more accurate anomaly detection. Furthermore, the anomaly detection unit can immediately issue warnings and notify users and security personnel of detected anomalies. For instance, when an anomaly is detected, the unit can sound an alarm or send a notification to a smartphone to encourage a quick response. This allows the anomaly detection unit to detect anomalies in real time and support a rapid and appropriate response. Additionally, the anomaly detection unit can accumulate data on detected anomalies and utilize it for later analysis and improvement. For example, the anomaly detection unit can learn from past anomaly detection data to improve the accuracy of its anomaly detection algorithm. This allows the anomaly detection unit to continuously improve the accuracy of anomaly detection, enhancing the overall reliability and security of the system.

[0068] The emergency response unit analyzes voice input from users in emergencies and directs appropriate responses. For example, if a user shouts "Help!", the emergency response unit recognizes the voice and notifies the police and emergency contacts. The emergency response unit uses voice recognition technology to analyze the user's voice and understand the situation. Specifically, the emergency response unit receives voice input from users in real time and applies a voice recognition algorithm to convert the voice data into text. Next, the emergency response unit analyzes the converted text data and uses natural language processing technology to determine the nature and urgency of the emergency. For example, the emergency response unit extracts keywords such as "Help," "Fire," and "Robbery" from the voice input to identify the type of emergency. The emergency response unit can also analyze the tone and emotion of the voice to assess the user's urgency. This allows the emergency response unit to accurately grasp the user's situation and direct appropriate responses. Furthermore, the emergency response unit can acquire the user's location information to expedite the emergency response. For example, the emergency response unit can use the GPS function of the user's smartphone to determine the user's current location and notify the nearest police station or emergency contact. The emergency response unit can also refer to the user's past activity history and emergency contact information to provide a swift and appropriate response. This allows the emergency response unit to ensure the user's safety and support a rapid response. Furthermore, the emergency response unit can provide follow-up advice and support to the user after the emergency. For example, after an emergency, the emergency response unit can advise the user to "evacuate to a safe place" or "wait until the police arrive," ensuring the user's safety. The emergency response unit can also accumulate emergency data and use it for future analysis and improvement. This allows the emergency response unit to continuously improve the accuracy and effectiveness of emergency responses, enhancing the overall reliability and security of the system.

[0069] The security system includes a coordinating unit that works in conjunction with security cameras and sensors. The coordinating unit improves the accuracy of anomaly detection by working with security cameras and sensors. For example, the coordinating unit analyzes video data from security cameras in real time to detect anomalies. It can also analyze sensor data in real time to detect anomalies. Furthermore, it can integrate and analyze data from security cameras and sensors to improve the accuracy of anomaly detection. This improves the accuracy of anomaly detection through collaboration with security cameras and sensors. Some or all of the above-described processes in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input video data from security cameras into a generating AI and have the generating AI perform anomaly detection.

[0070] The security system includes a voice analysis unit that analyzes the user's voice input. By analyzing the user's voice input, the voice analysis unit enables a quick and appropriate response in emergencies. For example, if a user shouts "Help!", the voice analysis unit recognizes the voice and notifies the police or emergency contacts. The voice analysis unit uses voice recognition technology to analyze the user's voice and understand the situation. For example, the voice analysis unit analyzes the user's voice data in real time and gives instructions for emergency response. The voice analysis unit can also analyze the voice data and estimate the user's emotions. This enables a quick and appropriate response in emergencies by analyzing the user's voice input. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the user's voice data into a generating AI and have the generating AI perform the voice analysis.

[0071] The advice-providing unit can estimate the user's emotions and adjust the content and tone of the advice based on the estimated emotions. For example, if the user is feeling anxious, the advice-providing unit can provide advice in a gentle tone that provides reassurance. If the user is excited, the advice-providing unit can also provide advice in a calm tone that encourages composure. If the user is relaxed, the advice-providing unit can also provide advice in a friendly tone. This allows for more appropriate security measures by providing advice tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0072] The advice-providing unit can analyze a user's past question history and provide individually customized advice. For example, the advice-providing unit can provide new, relevant advice based on the content of questions the user has asked in the past. The advice-providing unit can also evaluate the effectiveness of advice the user has received in the past and provide advice that reflects areas for improvement. The advice-providing unit can also identify specific patterns from the user's past question history and provide advice based on those patterns. This makes it possible to implement more effective security measures by providing advice based on the user's past question history. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the user's question history data into a generating AI and have the generating AI generate customized advice.

[0073] The advice-providing unit can provide region-specific crime prevention advice, taking into account the user's current location. For example, if the user is in a specific area, the advice-providing unit can provide advice based on crime trends in that area. If the user is traveling, the advice-providing unit can also provide crime prevention measures for the area they are visiting. If the user is at home, the advice-providing unit can also provide advice based on crime prevention information around their home. This allows for addressing region-specific risks by providing crime prevention advice based on the user's current location. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not. For example, the advice-providing unit can input the user's location data into a generating AI and have the generating AI generate region-specific crime prevention advice.

[0074] The advice-providing unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is nervous, the advice-providing unit will prioritize providing the most important advice. If the user is relaxed, the advice-providing unit can also provide detailed advice sequentially. If the user is in a hurry, the advice-providing unit can also prioritize providing concise and to-the-point advice. This allows for more effective security measures by prioritizing advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using AI, or not using AI. For example, the advice-providing unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0075] The advice service can analyze a user's social media activity and provide relevant crime prevention advice. For example, the advice service can provide privacy protection advice based on information a user has made public on social media. If a user has shared on social media their plans to attend a specific event, the advice service can also provide crime prevention advice related to that event. The advice service can also predict specific risks from a user's social media activity and provide advice based on those predictions. This enables privacy protection and risk avoidance by providing crime prevention advice based on a user's social media activity. Some or all of the above processes in the advice service may be performed using AI, for example, or not using AI. For example, the advice service can input a user's social media data into a generating AI and have the generating AI generate relevant crime prevention advice.

[0076] The advice-providing unit can provide targeted advice based on the user's family structure and lifestyle. For example, if the user has children, the advice-providing unit can provide advice on child safety. If the user lives alone, the advice-providing unit can also provide crime prevention advice tailored to single-person households. If the user is elderly, the advice-providing unit can also provide crime prevention advice tailored to the elderly. By providing crime prevention advice based on the user's family structure and lifestyle, more effective crime prevention measures become possible. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the user's family structure data and lifestyle data into a generating AI and have the generating AI generate targeted advice.

[0077] The risk identification unit can estimate the user's emotions and adjust the method of risk notification based on the estimated emotions. For example, if the user is feeling anxious, the risk identification unit can deliver risk notifications in a gentle tone. If the user is relaxed, the risk identification unit can also provide detailed risk information. If the user is in a hurry, the risk identification unit can deliver concise and to-the-point risk notifications. This allows for more effective security measures by providing risk notifications tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk identification unit may be performed using AI or not. For example, the risk identification unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0078] The risk identification unit can provide up-to-date risk information by analyzing real-time news and social media information in addition to historical crime data. For example, the risk identification unit can analyze real-time news information to identify the latest crime risks. The risk identification unit can also analyze social media posts to predict crime risks in specific areas. The risk identification unit can also provide more accurate risk information by combining historical crime data with real-time information. This allows the system to provide up-to-date risk information by analyzing real-time information. Some or all of the above-described processes in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input news feeds and social media data into a generating AI and have the generating AI generate the latest risk information.

[0079] The risk identification unit can learn the user's behavior patterns and perform individually customized risk predictions. For example, the risk identification unit can predict risks at specific times and locations based on the user's past behavior patterns. The risk identification unit can also analyze the user's travel history and predict risks along specific routes. The risk identification unit can also perform individually customized risk predictions considering the user's lifestyle. This enables more effective crime prevention measures by performing risk predictions based on the user's behavior patterns. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input user behavior pattern data into a generating AI and have the generating AI perform customized risk predictions.

[0080] The risk identification unit can estimate the user's emotions and adjust the timing of risk notifications based on the estimated emotions. For example, if the user is tense, the risk identification unit will issue a risk notification quickly. If the user is relaxed, the risk identification unit can also adjust the timing of providing detailed risk information. If the user is in a hurry, the risk identification unit can also adjust the timing of providing a concise and to-the-point risk notification. By adjusting the timing of risk notifications according to the user's emotions, more effective security measures can be implemented. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or not using AI. For example, the risk identification unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0081] The risk identification unit can provide region-specific risk information, taking into account the user's geographical location. For example, if the user is in a specific region, the risk identification unit can provide risk information based on crime trends in that region. If the user is traveling, the risk identification unit can also provide risk information for the region they are visiting. If the user is at home, the risk identification unit can also provide notifications based on risk information around their home. This allows for addressing region-specific risks by providing risk information based on the user's geographical location. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input the user's geographical location data into a generating AI and have the generating AI generate region-specific risk information.

[0082] The risk identification unit can analyze the user's past security measures history and propose effective risk avoidance measures. For example, the risk identification unit can evaluate the effectiveness of security measures previously implemented by the user and propose areas for improvement. The risk identification unit can also propose effective avoidance measures for specific risks based on the user's past security measures history. The risk identification unit can also propose individually customized risk avoidance measures based on the user's security measures history. This enables more effective security measures by proposing risk avoidance measures based on the user's past security measures history. Some or all of the above processing in the risk identification unit may be performed using AI, for example, or without AI. For example, the risk identification unit can input the user's security measures history data into a generating AI and have the generating AI generate effective risk avoidance measures.

[0083] The anomaly detection unit can estimate the user's emotions and adjust the sensitivity of anomaly detection based on the estimated emotions. For example, if the user is feeling anxious, the anomaly detection unit can increase the sensitivity of anomaly detection. If the user is relaxed, the anomaly detection unit can also set the sensitivity of anomaly detection to normal. If the user is in a hurry, the anomaly detection unit can also adjust the sensitivity of anomaly detection to reduce false positives. This allows for more effective security measures by adjusting the sensitivity of anomaly detection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0084] The anomaly detection unit can improve the accuracy of anomaly detection by analyzing not only security camera footage but also audio and temperature sensor data. For example, the anomaly detection unit can detect anomalies by combining security camera footage and audio data. The anomaly detection unit can also detect anomalies by combining security camera footage and temperature sensor data. The anomaly detection unit can also improve the accuracy of anomaly detection by integrating security camera footage, audio data, and temperature sensor data. As a result, the accuracy of anomaly detection is improved by analyzing audio and temperature sensor data in addition to security camera footage. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input security camera footage, audio data, and temperature sensor data into a generating AI and have the generating AI perform anomaly detection.

[0085] The anomaly detection unit can take a quicker and more accurate response when an anomaly is detected by referring to past anomaly detection history. For example, the anomaly detection unit can identify anomaly patterns based on past anomaly detection history and take a rapid response. The anomaly detection unit can also analyze the anomaly detection history and take measures to reduce false positives. The anomaly detection unit can also refer to the anomaly detection history to provide the optimal countermeasure for a specific anomaly. This makes a quicker and more accurate response possible by referring to past anomaly detection history. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input past anomaly detection history data into a generating AI and have the generating AI perform the generation of a quick and accurate countermeasure.

[0086] The anomaly detection unit can detect anomalies in real time and notify the user. For example, the anomaly detection unit can analyze security camera footage in real time and detect anomalies. When the anomaly detection unit detects an anomaly, it can immediately notify the user. By detecting anomalies in real time and notifying the user, the anomaly detection unit enables a rapid response. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input security camera footage into a generating AI and have the generating AI perform real-time anomaly detection.

[0087] The emergency response unit can estimate the user's emotions and adjust the emergency response instructions based on the estimated emotions. For example, if the user is in a state of panic, the emergency response unit will prioritize providing instructions to calm the user. If the user is calm, the emergency response unit can also provide detailed emergency response measures. If the user is confused, the emergency response unit can also provide concise and clear instructions. This allows for a more appropriate response by adjusting the emergency response instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not using AI. For example, the emergency response unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0088] The emergency response unit can provide the optimal response in an emergency by referring to the user's past emergency response history. For example, the emergency response unit can provide the optimal response based on the user's history of past emergencies. The emergency response unit can also propose effective response measures based on the user's past emergency response history. The emergency response unit can also analyze the user's emergency response history and provide response measures that reflect areas for improvement. This enables a quick and appropriate response by providing the optimal response measures based on the user's past emergency response history. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's emergency response history data into a generating AI and have the generating AI generate the optimal response measures.

[0089] The emergency response unit can, in an emergency, take into account the user's current location and notify the nearest police station or emergency contact. For example, if the user is at home, the emergency response unit will notify the nearest police station. If the user is out, the emergency response unit can also notify the nearest police station based on their current location. If the user is traveling, the emergency response unit can also notify the nearest police station in the area they are visiting. This enables a rapid response by notifying the nearest police station or emergency contact based on the user's current location. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or not using AI. For example, the emergency response unit can input the user's location data into a generating AI and have the generating AI execute the notification to the nearest police station or emergency contact.

[0090] The emergency response unit can estimate the user's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the user is in a state of panic, the emergency response unit will prioritize providing the most important response measures. If the user is calm, the emergency response unit can also provide detailed response measures sequentially. If the user is confused, the emergency response unit can also prioritize providing concise and to-the-point response measures. This allows for a more effective response by determining the priority of emergency responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI, or not using AI. For example, the emergency response unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0091] The emergency response unit can simultaneously notify the user's family and emergency contacts in the event of an emergency. For example, the emergency response unit can automatically notify the user's family in the event of an emergency. The emergency response unit can also automatically notify the user's emergency contacts in the event of an emergency. The emergency response unit can also simultaneously notify the user's family and emergency contacts in the event of an emergency to encourage a swift response. This enables a swift response by simultaneously notifying the user's family and emergency contacts in the event of an emergency. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's emergency contact data into a generating AI and have the generating AI execute the notification.

[0092] The emergency response unit can provide the optimal response in an emergency, taking into account the user's device information. For example, if the user is using a smartphone, the emergency response unit can provide a response optimized for the smartphone. If the user is using a tablet, the emergency response unit can also provide a response optimized for the tablet. If the user is using a smartwatch, the emergency response unit can also provide a response optimized for the smartwatch. This enables a quick and appropriate response by providing the optimal response based on the user's device information. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's device information into a generating AI and have the generating AI generate the optimal response.

[0093] The integration unit can estimate the user's emotions and select a device to integrate with based on the estimated emotions. For example, if the user is feeling anxious, the integration unit will prioritize integrating with the most reliable device. If the user is relaxed, the integration unit can also integrate with multiple devices to provide detailed information. If the user is in a hurry, the integration unit can select a device that can integrate quickly. This allows for more effective security measures by selecting devices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI. For example, the integration unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0094] The integration unit can analyze data from security cameras and sensors in real time to improve the accuracy of anomaly detection. For example, the integration unit can analyze video data from security cameras in real time to detect anomalies. The integration unit can also analyze sensor data in real time to detect anomalies. The integration unit can also integrate and analyze data from security cameras and sensors to improve the accuracy of anomaly detection. As a result, the accuracy of anomaly detection is improved by analyzing data from security cameras and sensors in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input video data from security cameras and sensor data into a generating AI and have the generating AI perform real-time anomaly detection.

[0095] The integration unit can estimate the user's emotions and prioritize integrated devices based on those emotions. For example, if the user is feeling anxious, the integration unit will prioritize connecting with the most reliable device. If the user is relaxed, the integration unit can also connect with multiple devices to provide detailed information. If the user is in a hurry, the integration unit can also prioritize selecting a device that can connect quickly. This allows for more effective security measures by prioritizing integrated devices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0096] The integration unit can save security camera and sensor data to the cloud for later analysis. For example, the integration unit can save security camera video data to the cloud for later analysis. The integration unit can also save sensor data to the cloud for later analysis. The integration unit can save security camera and sensor data to the cloud and integrate and analyze it later. This makes it possible to analyze security camera and sensor data later by saving it to the cloud. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input security camera video data and sensor data into a generating AI and have the generating AI perform the process of saving it to the cloud.

[0097] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can increase the accuracy of the voice analysis. If the user is relaxed, the voice analysis unit can perform voice analysis with normal accuracy. If the user is in a hurry, the voice analysis unit can adjust the accuracy of the voice analysis to perform a faster analysis. This allows for more effective security measures by adjusting the accuracy of the voice analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0098] The audio analysis unit can improve the accuracy of the analysis by removing background noise and other noise during audio analysis. For example, the audio analysis unit can improve the accuracy of the analysis by removing background noise during audio analysis. The audio analysis unit can also improve the accuracy of the analysis by removing noise during audio analysis. The audio analysis unit can also improve the accuracy of the analysis by simultaneously removing background noise and other noise during audio analysis. As a result, the accuracy of the analysis is improved by removing background noise and other noise during audio analysis. Some or all of the above processing in the audio analysis unit may be performed using AI, for example, or without using AI. For example, the audio analysis unit can input audio data into a generating AI and have the generating AI perform the removal of background noise and other noise.

[0099] The voice analysis unit can estimate the user's emotions and determine the priority of voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit will prioritize the most important voice analysis. If the user is relaxed, the voice analysis unit can also perform detailed voice analysis sequentially. If the user is in a hurry, the voice analysis unit can also prioritize concise and to-the-point voice analysis. This allows for more effective security measures by determining the priority of voice analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0100] The voice analysis unit can improve the accuracy of its analysis by referring to the user's past voice data during voice analysis. For example, the voice analysis unit can improve the accuracy of its analysis based on the user's past voice data. The voice analysis unit can also improve the accuracy of its analysis by referring to the user's past voice data to find specific patterns. The voice analysis unit can also improve the accuracy of noise reduction by analyzing the user's past voice data. As a result, the accuracy of the analysis is improved by referring to the user's past voice data. Some or all of the above processes in the voice analysis unit may be performed using AI, for example, or without using AI. For example, the voice analysis unit can input the user's past voice data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] Security systems can learn users' behavioral patterns and provide individually customized security advice. For example, if a user frequently goes out during certain times, the system can provide security advice tailored to those times. If a user frequently uses a particular route, it can also provide security advice based on that route. Furthermore, it can analyze users' behavioral patterns, predict potential risks, and issue warnings in advance. This allows for more effective security measures by providing security advice based on users' behavioral patterns.

[0103] The security system can estimate the user's emotions and automatically adjust the security camera's viewpoint based on those emotions. For example, if the user is feeling anxious, the security camera's viewpoint can be widened to monitor a broader area. If the user is relaxed, the system can focus on a specific area. Furthermore, if the user is stressed, the camera's zoom function can be used for more detailed monitoring. This allows for more effective surveillance by adjusting the security camera's viewpoint according to the user's emotions.

[0104] Security systems can monitor a user's health and provide crime prevention advice based on that health status. For example, if a user is tired, it can advise them to stay indoors. If a user is feeling unwell, it can also provide information about nearby medical facilities. Furthermore, if a user is feeling stressed, it can suggest ways to relax. In this way, by providing crime prevention advice based on the user's health status, it can support a safer life.

[0105] The security system can estimate the user's emotions and adjust the tone of the security alert based on those emotions. For example, if the user is feeling anxious, the alert will be issued in a gentle tone. If the user is relaxed, the alert can be issued in a normal tone. Furthermore, if the user is stressed, the alert can be issued in a tone that emphasizes urgency. By adjusting the tone of the security alert according to the user's emotions, more effective security measures can be achieved.

[0106] The security system can analyze a user's past security advice history and provide effective advice. For example, it can evaluate the effectiveness of past advice and provide advice that reflects areas for improvement. It can also provide new advice based on the results of security measures the user has taken in the past. Furthermore, it can identify specific patterns from a user's past security advice history and provide advice based on those patterns. In this way, by providing effective advice based on a user's past security advice history, it can support a safer life.

[0107] The security system can estimate the user's emotions and automatically switch the recording mode of the security camera based on those emotions. For example, if the user is feeling anxious, the security camera can be set to continuous recording mode. If the user is relaxed, it can be switched to motion detection mode. Furthermore, if the user is stressed, it can be switched to high-resolution recording mode. This allows for more effective surveillance by switching the security camera's recording mode according to the user's emotions.

[0108] A security system can provide targeted crime prevention advice based on the user's family structure and lifestyle. For example, if the user has children, it can provide advice on child safety. If the user lives alone, it can provide crime prevention advice tailored to single-person households. Furthermore, if the user is elderly, it can provide crime prevention advice for seniors. By providing crime prevention advice based on the user's family structure and lifestyle, more effective crime prevention measures become possible.

[0109] The security system can estimate the user's emotions and automatically adjust the security camera angle based on those emotions. For example, if the user is feeling anxious, the security camera angle can be widened to monitor a wider area. If the user is relaxed, the system can focus on monitoring a specific area. Furthermore, if the user is stressed, the system can utilize the security camera's zoom function for more detailed monitoring. This allows for more effective surveillance by adjusting the security camera angle according to the user's emotions.

[0110] A security system can analyze a user's social media activity and provide relevant security advice. For example, it can provide privacy protection advice based on information a user has made public on social media. If a user shares their plans to attend a specific event on social media, it can also provide security advice related to that event. Furthermore, it can predict specific risks from a user's social media activity and provide advice based on those risks. This enables privacy protection and risk avoidance by providing security advice based on a user's social media activity.

[0111] The security system can estimate the user's emotions and automatically switch the recording mode of the security camera based on those emotions. For example, if the user is feeling anxious, the security camera can be set to continuous recording mode. If the user is relaxed, it can be switched to motion detection mode. Furthermore, if the user is stressed, it can be switched to high-resolution recording mode. This allows for more effective surveillance by switching the security camera's recording mode according to the user's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The advice-providing unit provides appropriate advice to users regarding safety-related questions. For example, if a user asks, "What precautions should I take when going out at night?", the advice-providing unit will provide advice such as, "Choose well-lit areas to walk in, don't show your valuables, and pay attention to your surroundings." The advice-providing unit uses natural language processing technology to understand the user's question and generate an appropriate answer. Step 2: The risk identification unit analyzes past crime data to identify crime risks. For example, if there is a high incidence of crime at night in a particular area, the risk identification unit will notify the user of this information and warn them to be careful. The risk identification unit uses machine learning technology to analyze past data and predict crime risks. Step 3: The anomaly detection unit analyzes security camera footage in real time to detect anomalies. For example, if a suspicious person appears at a location where a security camera is installed, the anomaly detection unit analyzes the footage, detects the anomaly, and issues a warning. The anomaly detection unit uses computer vision technology to analyze the footage. Step 4: The emergency response unit analyzes the user's voice input in an emergency and directs the appropriate response. For example, if the user shouts "Help!", the emergency response unit recognizes the voice and notifies the police or emergency contacts. The emergency response unit uses voice recognition technology to analyze the user's voice and understand the situation.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the advice provision unit, risk identification unit, anomaly detection unit, emergency response unit, coordination unit, and voice analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the smart device 14 and provides appropriate advice to the user's questions. The risk identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies crime risks by analyzing past crime data. The anomaly detection unit uses the camera 42 of the smart device 14 to analyze security camera footage in real time and detect anomalies. The emergency response unit uses the microphone 38B of the smart device 14 to analyze the user's voice input and instruct appropriate responses. The coordination unit coordinates with security cameras and sensors via the communication I / F 44 of the smart device 14 to improve the accuracy of anomaly detection. The voice analysis unit uses the control unit 46A of the smart device 14 to analyze the user's voice data and instruct emergency responses. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the advice provision unit, risk identification unit, anomaly detection unit, emergency response unit, coordination unit, and voice analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the smart glasses 214 and provides appropriate advice to the user's questions. The risk identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies crime risks by analyzing past crime data. The anomaly detection unit uses the camera 42 of the smart glasses 214 to analyze security camera footage in real time and detect anomalies. The emergency response unit uses the microphone 238 of the smart glasses 214 to analyze the user's voice input and instruct appropriate responses. The coordination unit coordinates with security cameras and sensors via the communication I / F 44 of the smart glasses 214 to improve the accuracy of anomaly detection. The voice analysis unit uses the control unit 46A of the smart glasses 214 to analyze the user's voice data and instruct emergency responses. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the advice provision unit, risk identification unit, anomaly detection unit, emergency response unit, coordination unit, and voice analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the headset terminal 314 and provides appropriate advice to the user's questions. The risk identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies crime risks by analyzing past crime data. The anomaly detection unit uses the camera 42 of the headset terminal 314 to analyze security camera footage in real time and detect anomalies. The emergency response unit uses the microphone 238 of the headset terminal 314 to analyze the user's voice input and instruct appropriate responses. The coordination unit coordinates with security cameras and sensors via the communication I / F 44 of the headset terminal 314 to improve the accuracy of anomaly detection. The voice analysis unit uses the control unit 46A of the headset terminal 314 to analyze the user's voice data and instruct emergency responses. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the advice provision unit, risk identification unit, anomaly detection unit, emergency response unit, coordination unit, and voice analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the robot 414 and provides appropriate advice to the user's questions. The risk identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies crime risks by analyzing past crime data. The anomaly detection unit uses the camera 42 of the robot 414 to analyze security camera footage in real time and detect anomalies. The emergency response unit uses the microphone 238 of the robot 414 to analyze the user's voice input and instruct appropriate responses. The coordination unit coordinates with security cameras and sensors via the communication I / F 44 of the robot 414 to improve the accuracy of anomaly detection. The voice analysis unit uses the control unit 46A of the robot 414 to analyze the user's voice data and instruct emergency responses. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) An advice department that provides appropriate advice to users regarding security-related questions, The Risk Identification Department analyzes past crime data to identify crime risks, An anomaly detection unit that analyzes security camera footage in real time to detect abnormalities, It includes an emergency response unit that analyzes voice input from the user in an emergency and provides instructions for appropriate action. A system characterized by the following features. (Note 2) It is equipped with a unit that connects with security cameras and sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a voice analysis unit that analyzes the user's voice input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the content and tone of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice-providing unit, We analyze the user's past question history and provide personalized advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice-providing unit, It provides localized crime prevention advice, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned advice-providing unit, It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned advice-providing unit, Analyze users' social media activity and provide relevant crime prevention advice. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned advice-providing unit, Based on the user's family structure and lifestyle, it provides targeted advice to specific users. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned risk identification unit is The system estimates the user's emotions and adjusts the risk notification method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned risk identification unit is In addition to historical crime data, we analyze real-time news and social media information to provide the latest risk information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned risk identification unit is It learns user behavior patterns and provides individually customized risk predictions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned risk identification unit is The system estimates the user's emotions and adjusts the timing of risk notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned risk identification unit is Providing region-specific risk information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned risk identification unit is We analyze the user's past security measures and propose effective risk avoidance strategies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned abnormality detection unit, It estimates the user's emotions and adjusts the sensitivity of anomaly detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned abnormality detection unit, In addition to security camera footage, the system also analyzes audio and temperature sensor data to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned abnormality detection unit, When an anomaly is detected, past anomaly detection history is referenced to enable a faster and more accurate response. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned abnormality detection unit, Detect anomalies in real time and notify the user. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned emergency response unit, The system estimates the user's emotions and adjusts emergency response instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned emergency response unit, In emergencies, the system provides the most appropriate response by referring to the user's past emergency response history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emergency response unit, In an emergency, the system will consider the user's current location and notify the nearest police station or emergency contact. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emergency response unit, In an emergency, the user's family and emergency contacts will also be notified simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emergency response unit, In emergencies, we provide the optimal response by taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, It estimates the user's emotions and selects compatible devices based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, By analyzing data from security cameras and sensors in real time, we improve the accuracy of anomaly detection. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and prioritizes connected devices based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, Data from security cameras and sensors is stored in the cloud for later analysis. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned voice analysis unit, During audio analysis, background noise and other unwanted sounds are removed to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned voice analysis unit, It estimates the user's emotions and determines the priority of voice analysis based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned voice analysis unit, During voice analysis, the system improves the accuracy of the analysis by referencing the user's past voice data. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An advice department that provides appropriate advice to users regarding security-related questions, The Risk Identification Department analyzes past crime data to identify crime risks, An anomaly detection unit that analyzes security camera footage in real time to detect abnormalities, It includes an emergency response unit that analyzes voice input from the user in an emergency and provides instructions for appropriate action. A system characterized by the following features.

2. It is equipped with a unit that connects with security cameras and sensors. The system according to feature 1.

3. It includes a voice analysis unit that analyzes the user's voice input. The system according to feature 1.

4. The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the content and tone of advice based on those estimated emotions. The system according to feature 1.

5. The aforementioned advice-providing unit, We analyze the user's past question history and provide personalized advice. The system according to feature 1.

6. The aforementioned advice-providing unit, It provides localized crime prevention advice, taking into account the user's current location. The system according to feature 1.

7. The aforementioned advice-providing unit, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system according to feature 1.

8. The aforementioned advice-providing unit, Analyze users' social media activity and provide relevant crime prevention advice. The system according to feature 1.

9. The aforementioned advice-providing unit, Based on the user's family structure and lifestyle, it provides targeted advice to specific users. The system according to feature 1.

10. The aforementioned risk identification unit is The system estimates the user's emotions and adjusts the risk notification method based on those estimated emotions. The system according to feature 1.

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