system

Abstract

The system according to this embodiment aims to analyze surveillance camera footage and immediately detect and respond to criminal activity. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, a warning unit, and a learning unit. The collection unit collects video from surveillance cameras. The analysis unit analyzes the video collected by the collection unit and detects suspicious behavior or criminal activity. The notification unit notifies the police based on the information detected by the analysis unit. The warning unit issues warnings and evacuation notices to relevant parties based on the information detected by the analysis unit. The learning unit learns from the information obtained by the analysis unit and makes predictions about future crimes and assesses the degree of risk.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to analyze the video of a surveillance camera in real time and immediately detect and respond to criminal acts.

[0005] The system according to the embodiment aims to analyze the video of a surveillance camera and immediately detect and respond to criminal acts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a warning unit, and a learning unit. The collection unit collects video footage from surveillance cameras. The analysis unit analyzes the video footage collected by the collection unit and detects suspicious behavior or criminal activity. The notification unit notifies the police based on the information detected by the analysis unit. The warning unit issues warnings and evacuation notices to relevant parties based on the information detected by the analysis unit. The learning unit learns from the information obtained by the analysis unit and makes predictions about future crimes and assesses the level of risk. [Effects of the Invention]

[0007] The system according to this embodiment can analyze surveillance camera footage and immediately detect and respond to criminal activity. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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), etc.

[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 security system according to an embodiment of the present invention is a system for protecting families from criminals by utilizing a generating AI agent. This security system monitors video in real time using surveillance cameras, and the generating AI agent analyzes the surveillance footage to detect suspicious behavior or criminal acts and notifies the police. The security system also issues warnings and evacuation notices to relevant parties. This system enables constant monitoring to protect families and can prevent criminal acts before they occur. For example, the security system monitors video in real time using surveillance cameras. In this case, the surveillance cameras are installed around and inside the house and monitor 24 hours a day. For example, cameras are installed in the entrance, garden, living room, etc., and footage is constantly recorded. Next, the generating AI agent analyzes the surveillance footage in the security system. The generating AI agent checks the people, number of people, time of day, actions, belongings, clothing, etc. in the footage and detects suspicious behavior or criminal acts. For example, it can detect if a suspicious person is loitering around the house or trying to break a window. If detected, the generating AI agent in the security system notifies the police. The generating AI agent quickly notifies the police based on detected information and makes crime predictions and risk assessments. The security system also issues warnings and evacuation notices to relevant parties to encourage a swift response. For example, it can issue evacuation orders to family members or warn neighbors. Furthermore, the security system's generating AI agent collects and learns from crime information. This improves the accuracy of future crime predictions and risk assessments. For example, it can predict crimes at specific times and locations based on past crime data. This system enables constant monitoring to protect families and prevent criminal activity before it occurs. For example, if a suspicious person is loitering around the house at night, the security system's generating AI agent can immediately notify the police and issue evacuation orders to family members, preventing harm. In this way, the security system ensures the safety of families and prevents criminal activity.

[0029] The security system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a warning unit, and a learning unit. The collection unit collects video from surveillance cameras. The collection unit collects video from surveillance cameras installed around or inside a house, for example. The collection unit can collect video from cameras installed in the entrance, garden, living room, etc. Furthermore, the collection unit can collect video 24 hours a day and monitor continuously. For example, the collection unit can collect video using an infrared camera even at night. The analysis unit analyzes the video collected by the collection unit and detects suspicious behavior or criminal activity. The analysis unit checks, for example, the people, number of people, time of day, actions, belongings, clothing, etc. in the video and detects suspicious behavior or criminal activity. The analysis unit can detect, for example, when a suspicious person is loitering around the house or trying to break a window. The analysis unit uses a generation AI to analyze the information in the video and can detect suspicious behavior or criminal activity with high accuracy. The reporting unit notifies the police based on information detected by the analysis unit. The reporting unit can, for example, quickly notify the police based on the detected information. The reporting unit can, for example, predict crimes and assess the level of danger and then notify the police. The reporting unit can use AI to optimize the timing and content of reports. The warning unit issues warnings and evacuation notices to relevant parties based on information detected by the analysis unit. The warning unit can, for example, issue evacuation orders to family members or warn neighbors. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. The learning unit learns from the information obtained by the analysis unit and makes future crime predictions and assesses the level of danger. The learning unit can, for example, predict crimes at specific times and locations based on past crime data. The learning unit can use AI to improve the accuracy of its learning. As a result, the crime prevention system according to this embodiment can ensure the safety of families and prevent criminal acts.

[0030] The collection unit collects video footage from surveillance cameras. For example, it can collect footage from cameras installed around or inside a house. Specifically, it can collect footage from cameras installed in areas such as the entrance, garden, and living room. These cameras provide high-resolution video, capturing clear images day and night. The collection unit can collect footage 24 hours a day, ensuring constant monitoring. For instance, the collection unit can use infrared cameras to collect footage even at night. Infrared cameras provide clear images even in darkness, capturing the movements of suspicious individuals. The collection unit also features motion detection, automatically starting recording when motion is detected. This reduces unnecessary recording and allows for efficient data management. Furthermore, the collection unit can store video data using cloud storage, retaining data for extended periods. This allows for reviewing past footage and utilizing it as evidence. The collection unit can integrate footage from multiple cameras to understand the overall situation. For example, positioning cameras to cover the entire perimeter of a house, eliminating blind spots, can enhance security. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the video footage collected by the collection unit to detect suspicious behavior and criminal activity. For example, the analysis unit checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior and criminal activity. Specifically, it analyzes the movements, facial expressions, and behavioral patterns of people in the video to identify suspicious behavior. For example, it can detect if a suspicious person is loitering around a house or attempting to break a window. The analysis unit uses generative AI to analyze the information in the video and detect suspicious behavior and criminal activity with high accuracy. The generative AI analyzes the movements and actions of people in the video in real time and detects abnormal behavior. For example, if it detects movement that differs from normal behavioral patterns, it can immediately issue a warning. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past crime data, it can predict fluctuations in risk at specific times and locations and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The reporting unit notifies the police based on information detected by the analysis unit. For example, the reporting unit can quickly notify the police based on the detected information. Specifically, if the analysis unit detects suspicious behavior or criminal activity, the reporting unit automatically notifies the police and provides the necessary information. The reporting unit can predict crimes and assess the level of danger before notifying the police. For example, if it is determined that there is a high probability of a crime, the reporting unit immediately contacts the police and conveys the situation at the scene. The reporting unit can use AI to optimize the timing and content of reports. The AI ​​generates the most appropriate report content according to the situation and conveys the information to the police quickly and accurately. For example, by conveying details such as the progress of the crime, the characteristics of the perpetrator, and the situation at the scene, it supports the police's rapid response. In addition, the reporting unit manages the history of reports and can refer to past report content. This makes it possible to plan countermeasures based on past cases and improve the content of reports. Furthermore, the reporting unit can reliably transmit information using multiple communication methods. For example, it can use a combination of telephone, email, and SMS to ensure that important information is delivered reliably. This allows the reporting department to report incidents to the police quickly and accurately, supporting the early detection and response to criminal activity.

[0033] The warning unit issues warnings and evacuation notices to relevant parties based on information detected by the analysis unit. For example, the warning unit can issue evacuation orders to family members or warnings to neighbors. Specifically, it can issue evacuation orders to family members via smartphone notifications or voice alarms to encourage prompt evacuation. It can also issue warnings to neighbors via email or SMS to draw their attention. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. The AI ​​generates the most appropriate warning content according to the situation and conveys the information to relevant parties quickly and accurately. For example, if a suspicious person is loitering around a house, it can immediately issue evacuation orders to family members and urge neighbors to be vigilant. The warning unit also manages the history of warnings and can refer to past warning content. This allows for the development of countermeasures based on past cases and the improvement of warning content. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, email, and social media. This allows the warning unit to quickly and reliably issue warnings and evacuation notices to those involved, minimizing damage.

[0034] The learning unit learns from information obtained by the analysis unit and makes future crime predictions and risk assessments. For example, the learning unit can predict crimes at specific times and locations based on past crime data. Specifically, it analyzes past crime data to understand crime patterns and trends. This allows it to predict risks at specific times and locations and take countermeasures in advance. The learning unit can improve the accuracy of its learning using AI. AI analyzes large amounts of data to understand crime patterns and trends with high accuracy. For example, it analyzes the frequency of suspicious person appearances at specific times and the crime rate at specific locations to identify high-risk times and locations. In addition, the learning unit can continuously update its learning model based on the latest data obtained from the analysis unit to improve accuracy. This allows the learning unit to always make highly accurate crime predictions and risk assessments based on the latest information. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the learning unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0035] The collection unit can collect video footage from surveillance cameras installed around and inside the house. For example, the collection unit can collect video footage from cameras installed in the entrance, garden, living room, etc. For example, the collection unit can collect video footage from cameras installed around and inside the house 24 hours a day. For example, the collection unit can collect video footage even at night using infrared cameras. This expands the surveillance area by collecting video footage from surveillance cameras installed around and inside the house. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from surveillance cameras into a generating AI and have the generating AI perform the video data collection.

[0036] The analysis unit can check the people, number of people, time of day, actions, belongings, clothing, etc., in the video and detect suspicious behavior or criminal activity. For example, the analysis unit can check the people in the video and detect if a suspicious person is loitering around a house. For example, the analysis unit can check the number of people in the video and detect if an unusually large number of people are around a house. For example, the analysis unit can check the time of day in the video and detect if suspicious behavior is occurring at a specific time. For example, the analysis unit can check the actions in the video and detect if someone is trying to break a window. For example, the analysis unit can check the belongings in the video and detect if someone is carrying a dangerous object. For example, the analysis unit can check the clothing in the video and detect if someone is wearing unusual clothing. By checking detailed information in the video, suspicious behavior and criminal activity can be detected with high accuracy. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the analysis of the information in the video.

[0037] The reporting unit can quickly report to the police based on the detected information. For example, the reporting unit can report to the police based on the detected information. For example, the reporting unit can predict crimes and assess the level of danger and report to the police. The reporting unit can use AI to optimize the timing and content of reports. This enables a swift response by quickly reporting to the police based on the detected information. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input detected information into a generating AI and have the generating AI optimize the timing and content of the report.

[0038] The warning unit can issue warnings and evacuation notices to relevant parties. For example, the warning unit can issue evacuation orders to family members. For example, the warning unit can issue warnings to neighbors. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. This allows for a quicker response by issuing warnings and evacuation notices to relevant parties. Some or all of the above-described processes in the warning unit may be performed using AI or not. For example, the warning unit can input the content of warnings and evacuation notices into a generating AI and have the generating AI optimize the content and timing.

[0039] The learning unit can predict crimes at specific times and locations based on past crime data. For example, the learning unit can predict crimes at specific times based on past crime data. For example, the learning unit can predict crimes at specific locations based on past crime data. The learning unit can use AI to improve the accuracy of its learning. This allows for improved accuracy of future crime predictions by making crime predictions based on past crime data. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input past crime data into a generating AI and have the generating AI perform improvements to the accuracy of crime predictions.

[0040] The collection unit can collect video footage focusing on specific areas around or inside a house. For example, it can collect video footage focusing on entry points such as front doors and windows. For example, it can collect video footage focusing on external areas such as gardens and parking lots. For example, it can collect video footage focusing on internal areas such as living rooms and bedrooms. This allows for enhanced monitoring of important areas by focusing video footage on specific areas. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from a specific area into a generating AI and have the generating AI perform the video collection.

[0041] The collection unit can change the method of collecting video at different times of day. For example, the collection unit can collect wide-area video during the day and focus on collecting video in a specific area at night. For example, the collection unit can collect video regularly on weekdays and frequently on weekends. For example, if there is a specific event or activity, the collection unit can change the method of collecting video to suit that time of day. By changing the method of collecting video at different times of day, appropriate monitoring according to the time of day becomes possible. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from different time periods into a generating AI and have the generating AI execute the change in the method of collecting video.

[0042] The data collection unit can simultaneously collect environmental data such as temperature and humidity around and inside the house. For example, the data collection unit can collect temperature and humidity from the entrance and windows to check for any abnormalities. For example, the data collection unit can collect temperature and humidity from the garden and parking lot to monitor environmental changes. For example, the data collection unit can collect temperature and humidity from the living room and bedroom to maintain a comfortable environment. In this way, by simultaneously collecting environmental data such as temperature and humidity, it is possible to monitor environmental changes. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental data into a generating AI and have the generating AI perform monitoring of environmental changes.

[0043] The collection unit can simultaneously collect audio data from around and inside the house. For example, the collection unit can collect audio data from the entrance and windows to check for any unusual sounds. For example, the collection unit can collect audio data from the garden and parking lot to monitor for any suspicious sounds. For example, the collection unit can collect audio data from the living room and bedroom to check for any abnormalities. By simultaneously collecting audio data, it is possible to detect abnormal sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input audio data into a generating AI and have the generating AI perform the detection of abnormal sounds.

[0044] The analysis unit can analyze in detail the movement patterns of people in the video. For example, the analysis unit can analyze the walking patterns of people in the video and detect abnormal movements. For example, the analysis unit can analyze the hand movements of people in the video and detect suspicious behavior. For example, the analysis unit can analyze the eye movements of people in the video and detect suspicious behavior. In this way, abnormal movements can be detected by analyzing the movement patterns of people in detail. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the analysis of the movement patterns of people.

[0045] The analysis unit can identify the type and location of objects in the video. For example, the analysis unit can identify the type of object in the video and detect abnormal objects. For example, the analysis unit can identify the location of objects in the video and detect objects in suspicious locations. For example, the analysis unit can analyze the movement of objects in the video and detect suspicious movements. By doing so, abnormal objects and locations can be detected by identifying the type and location of objects. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the identification of the type and location of objects.

[0046] The analysis unit can analyze audio data within a video and detect suspicious sounds. For example, the analysis unit can analyze audio data within a video and detect abnormal sounds. For example, the analysis unit can analyze audio data within a video and detect suspicious conversations. For example, the analysis unit can analyze audio data within a video and detect abnormal sound patterns. In this way, abnormal sounds can be detected by analyzing audio data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI perform the analysis of the audio data.

[0047] The analysis unit can analyze changes in temperature and humidity within the video and detect anomalies. For example, the analysis unit can analyze temperature data within the video and detect abnormal temperature changes. For example, the analysis unit can analyze humidity data within the video and detect abnormal humidity changes. For example, the analysis unit can analyze changes in temperature and humidity within the video and detect abnormal environmental changes. In this way, abnormal environmental changes can be detected by analyzing changes in temperature and humidity. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input temperature and humidity data into a generation AI and have the generation AI perform the analysis of environmental changes.

[0048] The reporting unit can attach a portion of a video when reporting to the police. For example, the reporting unit can attach a portion of a video showing suspicious behavior when reporting to the police. For example, the reporting unit can attach a portion of a video showing a criminal act when reporting to the police. For example, the reporting unit can attach a portion of a video that serves as evidence when reporting to the police. By attaching a portion of the video, it is possible to provide the police with more specific information. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input video data into a generation AI and have the generation AI perform the process of attaching a portion of the video to the report.

[0049] The reporting unit can include detailed information in the reported content. For example, when reporting to the police, the reporting unit can include detailed information about the suspicious behavior it has detected. For example, when reporting to the police, the reporting unit can include detailed information about the criminal act it has detected. For example, when reporting to the police, the reporting unit can include detailed information about crime prediction and risk assessment. By including detailed information, it is possible to provide the police with more accurate information. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input the content of the report into a generation AI and have the generation AI perform the processing of including detailed information.

[0050] The reporting unit can also report to relevant agencies other than the police. For example, the reporting unit can report to the fire department in addition to the police. For example, the reporting unit can report to the ambulance service in addition to the police. For example, the reporting unit can report to neighbors in addition to the police. By reporting to relevant agencies other than the police, a broader response becomes possible. Some or all of the above-described processes in the reporting unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the reporting unit can input the report content into a generation AI and have the generation AI execute the reporting to the relevant agencies.

[0051] The reporting unit can transmit the content of the report by voice. For example, when reporting to the police, the reporting unit can provide details of suspicious behavior by voice. For example, when reporting to the police, the reporting unit can provide details of criminal activity by voice. For example, when reporting to the police, the reporting unit can provide details of crime predictions and risk assessments by voice. This enables rapid and accurate information transmission by transmitting the content of the report by voice. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input the content of the report into a generation AI and have the generation AI perform the voice transmission.

[0052] The warning unit can attach a portion of a video when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video showing suspicious behavior when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video showing a criminal act when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video that serves as evidence when issuing a warning to the relevant parties. By attaching a portion of the video, more specific information can be provided to the relevant parties. Some or all of the above processing in the warning unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the warning unit can input video data into a generating AI and have the generating AI perform the process of attaching a portion of the video to the warning content.

[0053] The warning unit can include detailed information in the warning content. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about the suspicious behavior it has detected. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about the criminal act it has detected. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about crime prediction and risk assessment. By including detailed information, it is possible to provide more accurate information to the person concerned. Some or all of the above processing in the warning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI perform the processing of including detailed information.

[0054] The warning unit can issue warnings to nearby residents who are not directly involved. For example, the warning unit can issue warnings to nearby residents who are not directly involved. For example, the warning unit can issue evacuation orders to nearby residents who are not directly involved. For example, the warning unit can issue warnings to nearby residents who are not directly involved. This allows for a broader response by issuing warnings to nearby residents who are not directly involved. Some or all of the above-described processes in the warning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI issue a warning to nearby residents.

[0055] The warning unit can convey warning content by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of suspicious behavior by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of criminal acts by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of crime predictions and risk assessments by voice. This enables rapid and accurate information transmission by conveying warning content by voice. Some or all of the above processing in the warning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI perform the voice transmission.

[0056] The learning unit can improve the accuracy of future crime predictions and risk assessments based on past warning and report data. For example, the learning unit can improve the accuracy of future crime predictions based on past warning data. For example, the learning unit can improve the accuracy of risk assessments based on past report data. For example, the learning unit can improve the accuracy of crime predictions and risk assessments based on past warning and report data. In this way, by learning from past warning and report data, the accuracy of future crime predictions and risk assessments can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input past warning and report data into a generative AI and have the generative AI perform the task of improving the accuracy of crime predictions and risk assessments.

[0057] The learning unit can learn not only past crime data but also crime data from the surrounding area. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of crime prediction. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of risk assessment. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of future crime prediction and risk assessment. In this way, by learning crime data from the surrounding area as well, the accuracy of crime prediction and risk assessment can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input crime data from the surrounding area into a generative AI and have the generative AI perform the learning.

[0058] The learning unit can learn not only past crime data but also weather and seasonal data. For example, the learning unit can learn past crime data and weather data to improve the accuracy of crime prediction. For example, the learning unit can learn past crime data and seasonal data to improve the accuracy of risk assessment. For example, the learning unit can learn past crime data and weather and seasonal data to improve the accuracy of future crime prediction and risk assessment. In this way, by learning weather and seasonal data as well, the accuracy of crime prediction and risk assessment can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input weather and seasonal data into a generative AI and have the generative AI perform the learning.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The security system can identify the type and location of objects in surveillance camera footage when analyzing the footage. For example, it can identify the type of object in the footage and detect abnormal objects. It can identify the location of objects in the footage and detect objects in suspicious positions. It can also analyze the movement of objects in the footage and detect suspicious movements. In this way, by identifying the type and location of objects, abnormal objects and locations can be detected. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the identification of the type and location of objects.

[0061] The security system can analyze audio data within surveillance camera footage to detect suspicious sounds. For example, it can analyze audio data within the video to detect unusual sounds. It can analyze audio data within the video to detect suspicious conversations. It can also analyze audio data within the video to detect unusual sound patterns. In this way, by analyzing audio data, it is possible to detect unusual sounds. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI perform the analysis of the audio data.

[0062] The security system can analyze changes in temperature and humidity within surveillance camera footage to detect anomalies. For example, it can analyze temperature data within the footage to detect abnormal temperature changes. It can also analyze humidity data within the footage to detect abnormal humidity changes. Furthermore, it can analyze changes in temperature and humidity within the footage to detect abnormal environmental changes. In this way, abnormal environmental changes can be detected by analyzing changes in temperature and humidity. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input temperature and humidity data into a generation AI and have the generation AI perform the analysis of environmental changes.

[0063] The crime prevention system can attach a portion of the video footage when reporting to the police. For example, it can attach a portion of the video showing suspicious behavior when reporting to the police. It can also attach a portion of the video showing a criminal act when reporting to the police. Furthermore, it can attach a portion of the video footage that serves as evidence when reporting to the police. This allows for the provision of more specific information to the police by attaching a portion of the video footage. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input video data into a generation AI and have the generation AI perform the process of attaching a portion of the video footage to the report.

[0064] The security system can also notify relevant agencies other than the police. For example, it can notify the fire department in addition to the police. It can also notify emergency services in addition to the police. Furthermore, it can notify neighbors in addition to the police. This allows for a broader response by notifying relevant agencies other than the police. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the notification content into a generation AI and have the generation AI execute the notification to the relevant agencies.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects video footage from surveillance cameras. For example, it collects footage from surveillance cameras installed around and inside the house. The collection unit can collect footage from cameras installed in the entrance, garden, living room, etc. Furthermore, the collection unit can collect footage 24 hours a day, providing continuous monitoring. Footage can also be collected at night using infrared cameras. Step 2: The analysis unit analyzes the video collected by the collection unit to detect suspicious behavior and criminal activity. For example, it checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior and criminal activity. It can detect situations such as a suspicious person loitering around a house or attempting to break a window. Using generated AI, it can analyze the information in the video and detect suspicious behavior and criminal activity with high accuracy. Step 3: The reporting unit notifies the police based on the information detected by the analysis unit. For example, it can quickly notify the police based on the detected information. It can predict crimes and assess the level of danger before notifying the police. It can use AI to optimize the timing and content of the report. Step 4: The warning unit issues warnings and evacuation notices to relevant parties based on the information detected by the analysis unit. For example, it can issue evacuation orders to family members or warn neighbors. AI can be used to optimize the content and timing of warnings and evacuation notices. Step 5: The learning unit learns from the information obtained by the analysis unit and makes predictions about future crimes and assesses the level of risk. For example, it can predict crimes at specific times and locations based on past crime data. AI can be used to improve the accuracy of the learning process.

[0067] (Example of form 2) The security system according to an embodiment of the present invention is a system for protecting families from criminals by utilizing a generating AI agent. This security system monitors video in real time using surveillance cameras, and the generating AI agent analyzes the surveillance footage to detect suspicious behavior or criminal acts and notifies the police. The security system also issues warnings and evacuation notices to relevant parties. This system enables constant monitoring to protect families and can prevent criminal acts before they occur. For example, the security system monitors video in real time using surveillance cameras. In this case, the surveillance cameras are installed around and inside the house and monitor 24 hours a day. For example, cameras are installed in the entrance, garden, living room, etc., and footage is constantly recorded. Next, the generating AI agent analyzes the surveillance footage in the security system. The generating AI agent checks the people, number of people, time of day, actions, belongings, clothing, etc. in the footage and detects suspicious behavior or criminal acts. For example, it can detect if a suspicious person is loitering around the house or trying to break a window. If detected, the generating AI agent in the security system notifies the police. The generating AI agent quickly notifies the police based on detected information and makes crime predictions and risk assessments. The security system also issues warnings and evacuation notices to relevant parties to encourage a swift response. For example, it can issue evacuation orders to family members or warn neighbors. Furthermore, the security system's generating AI agent collects and learns from crime information. This improves the accuracy of future crime predictions and risk assessments. For example, it can predict crimes at specific times and locations based on past crime data. This system enables constant monitoring to protect families and prevent criminal activity before it occurs. For example, if a suspicious person is loitering around the house at night, the security system's generating AI agent can immediately notify the police and issue evacuation orders to family members, preventing harm. In this way, the security system ensures the safety of families and prevents criminal activity.

[0068] The security system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a warning unit, and a learning unit. The collection unit collects video from surveillance cameras. The collection unit collects video from surveillance cameras installed around or inside a house, for example. The collection unit can collect video from cameras installed in the entrance, garden, living room, etc. Furthermore, the collection unit can collect video 24 hours a day and monitor continuously. For example, the collection unit can collect video using an infrared camera even at night. The analysis unit analyzes the video collected by the collection unit and detects suspicious behavior or criminal activity. The analysis unit checks, for example, the people, number of people, time of day, actions, belongings, clothing, etc. in the video and detects suspicious behavior or criminal activity. The analysis unit can detect, for example, when a suspicious person is loitering around the house or trying to break a window. The analysis unit uses a generation AI to analyze the information in the video and can detect suspicious behavior or criminal activity with high accuracy. The reporting unit notifies the police based on information detected by the analysis unit. The reporting unit can, for example, quickly notify the police based on the detected information. The reporting unit can, for example, predict crimes and assess the level of danger and then notify the police. The reporting unit can use AI to optimize the timing and content of reports. The warning unit issues warnings and evacuation notices to relevant parties based on information detected by the analysis unit. The warning unit can, for example, issue evacuation orders to family members or warn neighbors. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. The learning unit learns from the information obtained by the analysis unit and makes future crime predictions and assesses the level of danger. The learning unit can, for example, predict crimes at specific times and locations based on past crime data. The learning unit can use AI to improve the accuracy of its learning. As a result, the crime prevention system according to this embodiment can ensure the safety of families and prevent criminal acts.

[0069] The collection unit collects video footage from surveillance cameras. For example, it can collect footage from cameras installed around or inside a house. Specifically, it can collect footage from cameras installed in areas such as the entrance, garden, and living room. These cameras provide high-resolution video, capturing clear images day and night. The collection unit can collect footage 24 hours a day, ensuring constant monitoring. For instance, the collection unit can use infrared cameras to collect footage even at night. Infrared cameras provide clear images even in darkness, capturing the movements of suspicious individuals. The collection unit also features motion detection, automatically starting recording when motion is detected. This reduces unnecessary recording and allows for efficient data management. Furthermore, the collection unit can store video data using cloud storage, retaining data for extended periods. This allows for reviewing past footage and utilizing it as evidence. The collection unit can integrate footage from multiple cameras to understand the overall situation. For example, positioning cameras to cover the entire perimeter of a house, eliminating blind spots, can enhance security. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0070] The analysis unit analyzes the video footage collected by the collection unit to detect suspicious behavior and criminal activity. For example, the analysis unit checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior and criminal activity. Specifically, it analyzes the movements, facial expressions, and behavioral patterns of people in the video to identify suspicious behavior. For example, it can detect if a suspicious person is loitering around a house or attempting to break a window. The analysis unit uses generative AI to analyze the information in the video and detect suspicious behavior and criminal activity with high accuracy. The generative AI analyzes the movements and actions of people in the video in real time and detects abnormal behavior. For example, if it detects movement that differs from normal behavioral patterns, it can immediately issue a warning. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past crime data, it can predict fluctuations in risk at specific times and locations and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0071] The reporting unit notifies the police based on information detected by the analysis unit. For example, the reporting unit can quickly notify the police based on the detected information. Specifically, if the analysis unit detects suspicious behavior or criminal activity, the reporting unit automatically notifies the police and provides the necessary information. The reporting unit can predict crimes and assess the level of danger before notifying the police. For example, if it is determined that there is a high probability of a crime, the reporting unit immediately contacts the police and conveys the situation at the scene. The reporting unit can use AI to optimize the timing and content of reports. The AI ​​generates the most appropriate report content according to the situation and conveys the information to the police quickly and accurately. For example, by conveying details such as the progress of the crime, the characteristics of the perpetrator, and the situation at the scene, it supports the police's rapid response. In addition, the reporting unit manages the history of reports and can refer to past report content. This makes it possible to plan countermeasures based on past cases and improve the content of reports. Furthermore, the reporting unit can reliably transmit information using multiple communication methods. For example, it can use a combination of telephone, email, and SMS to ensure that important information is delivered reliably. This allows the reporting department to report incidents to the police quickly and accurately, supporting the early detection and response to criminal activity.

[0072] The warning unit issues warnings and evacuation notices to relevant parties based on information detected by the analysis unit. For example, the warning unit can issue evacuation orders to family members or warnings to neighbors. Specifically, it can issue evacuation orders to family members via smartphone notifications or voice alarms to encourage prompt evacuation. It can also issue warnings to neighbors via email or SMS to draw their attention. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. The AI ​​generates the most appropriate warning content according to the situation and conveys the information to relevant parties quickly and accurately. For example, if a suspicious person is loitering around a house, it can immediately issue evacuation orders to family members and urge neighbors to be vigilant. The warning unit also manages the history of warnings and can refer to past warning content. This allows for the development of countermeasures based on past cases and the improvement of warning content. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, email, and social media. This allows the warning unit to quickly and reliably issue warnings and evacuation notices to those involved, minimizing damage.

[0073] The learning unit learns from information obtained by the analysis unit and makes future crime predictions and risk assessments. For example, the learning unit can predict crimes at specific times and locations based on past crime data. Specifically, it analyzes past crime data to understand crime patterns and trends. This allows it to predict risks at specific times and locations and take countermeasures in advance. The learning unit can improve the accuracy of its learning using AI. AI analyzes large amounts of data to understand crime patterns and trends with high accuracy. For example, it analyzes the frequency of suspicious person appearances at specific times and the crime rate at specific locations to identify high-risk times and locations. In addition, the learning unit can continuously update its learning model based on the latest data obtained from the analysis unit to improve accuracy. This allows the learning unit to always make highly accurate crime predictions and risk assessments based on the latest information. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the learning unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0074] The collection unit can collect video footage from surveillance cameras installed around and inside the house. For example, the collection unit can collect video footage from cameras installed in the entrance, garden, living room, etc. For example, the collection unit can collect video footage from cameras installed around and inside the house 24 hours a day. For example, the collection unit can collect video footage even at night using infrared cameras. This expands the surveillance area by collecting video footage from surveillance cameras installed around and inside the house. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from surveillance cameras into a generating AI and have the generating AI perform the video data collection.

[0075] The analysis unit can check the people, number of people, time of day, actions, belongings, clothing, etc., in the video and detect suspicious behavior or criminal activity. For example, the analysis unit can check the people in the video and detect if a suspicious person is loitering around a house. For example, the analysis unit can check the number of people in the video and detect if an unusually large number of people are around a house. For example, the analysis unit can check the time of day in the video and detect if suspicious behavior is occurring at a specific time. For example, the analysis unit can check the actions in the video and detect if someone is trying to break a window. For example, the analysis unit can check the belongings in the video and detect if someone is carrying a dangerous object. For example, the analysis unit can check the clothing in the video and detect if someone is wearing unusual clothing. By checking detailed information in the video, suspicious behavior and criminal activity can be detected with high accuracy. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the analysis of the information in the video.

[0076] The reporting unit can quickly report to the police based on the detected information. For example, the reporting unit can report to the police based on the detected information. For example, the reporting unit can predict crimes and assess the level of danger and report to the police. The reporting unit can use AI to optimize the timing and content of reports. This enables a swift response by quickly reporting to the police based on the detected information. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input detected information into a generating AI and have the generating AI optimize the timing and content of the report.

[0077] The warning unit can issue warnings and evacuation notices to relevant parties. For example, the warning unit can issue evacuation orders to family members. For example, the warning unit can issue warnings to neighbors. The warning unit can use AI to optimize the content and timing of warnings and evacuation notices. This allows for a quicker response by issuing warnings and evacuation notices to relevant parties. Some or all of the above-described processes in the warning unit may be performed using AI or not. For example, the warning unit can input the content of warnings and evacuation notices into a generating AI and have the generating AI optimize the content and timing.

[0078] The learning unit can predict crimes at specific times and locations based on past crime data. For example, the learning unit can predict crimes at specific times based on past crime data. For example, the learning unit can predict crimes at specific locations based on past crime data. The learning unit can use AI to improve the accuracy of its learning. This allows for improved accuracy of future crime predictions by making crime predictions based on past crime data. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input past crime data into a generating AI and have the generating AI perform improvements to the accuracy of crime predictions.

[0079] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is feeling anxious, the collection unit can collect video more frequently and enhance real-time monitoring. For example, if the user is relaxed, the collection unit can collect video periodically and perform normal monitoring. For example, if the user is out, the collection unit can adjust the timing of video collection according to the situation at the user's location. This allows for more appropriate monitoring by adjusting the timing of video collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of video collection.

[0080] The collection unit can collect video footage focusing on specific areas around or inside a house. For example, it can collect video footage focusing on entry points such as front doors and windows. For example, it can collect video footage focusing on external areas such as gardens and parking lots. For example, it can collect video footage focusing on internal areas such as living rooms and bedrooms. This allows for enhanced monitoring of important areas by focusing video footage on specific areas. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from a specific area into a generating AI and have the generating AI perform the video collection.

[0081] The collection unit can change the method of collecting video at different times of day. For example, the collection unit can collect wide-area video during the day and focus on collecting video in a specific area at night. For example, the collection unit can collect video regularly on weekdays and frequently on weekends. For example, if there is a specific event or activity, the collection unit can change the method of collecting video to suit that time of day. By changing the method of collecting video at different times of day, appropriate monitoring according to the time of day becomes possible. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input video data from different time periods into a generating AI and have the generating AI execute the change in the method of collecting video.

[0082] The collection unit can estimate the user's emotions and determine the priority of the video footage to collect based on the estimated emotions. For example, if the user is feeling anxious, the collection unit may prioritize collecting video footage of entry points such as front doors and windows. For example, if the user is relaxed, the collection unit may prioritize collecting video footage of external areas such as gardens and parking lots. For example, if the user is out, the collection unit may prioritize collecting video footage of the interior of the house. This allows for the priority collection of important footage by determining the priority of the video footage to collect 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 collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the video footage.

[0083] The data collection unit can simultaneously collect environmental data such as temperature and humidity around and inside the house. For example, the data collection unit can collect temperature and humidity from the entrance and windows to check for any abnormalities. For example, the data collection unit can collect temperature and humidity from the garden and parking lot to monitor environmental changes. For example, the data collection unit can collect temperature and humidity from the living room and bedroom to maintain a comfortable environment. In this way, by simultaneously collecting environmental data such as temperature and humidity, it is possible to monitor environmental changes. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental data into a generating AI and have the generating AI perform monitoring of environmental changes.

[0084] The collection unit can simultaneously collect audio data from around and inside the house. For example, the collection unit can collect audio data from the entrance and windows to check for any unusual sounds. For example, the collection unit can collect audio data from the garden and parking lot to monitor for any suspicious sounds. For example, the collection unit can collect audio data from the living room and bedroom to check for any abnormalities. By simultaneously collecting audio data, it is possible to detect abnormal sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input audio data into a generating AI and have the generating AI perform the detection of abnormal sounds.

[0085] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can analyze the video with high accuracy and detect suspicious behavior. For example, if the user is relaxed, the analysis unit can analyze the video with normal accuracy and detect suspicious behavior. For example, if the user is out, the analysis unit can adjust the accuracy of the analysis according to the situation at the user's location. This allows for more appropriate analysis by adjusting the accuracy of the 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-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the accuracy of the analysis.

[0086] The analysis unit can analyze in detail the movement patterns of people in the video. For example, the analysis unit can analyze the walking patterns of people in the video and detect abnormal movements. For example, the analysis unit can analyze the hand movements of people in the video and detect suspicious behavior. For example, the analysis unit can analyze the eye movements of people in the video and detect suspicious behavior. In this way, abnormal movements can be detected by analyzing the movement patterns of people in detail. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the analysis of the movement patterns of people.

[0087] The analysis unit can identify the type and location of objects in the video. For example, the analysis unit can identify the type of object in the video and detect abnormal objects. For example, the analysis unit can identify the location of objects in the video and detect objects in suspicious locations. For example, the analysis unit can analyze the movement of objects in the video and detect suspicious movements. By doing so, abnormal objects and locations can be detected by identifying the type and location of objects. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the identification of the type and location of objects.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can display the analysis results in detail to provide reassurance. For example, if the user is relaxed, the analysis unit can display the analysis results concisely and provide only the necessary information. For example, if the user is out, the analysis unit can display the analysis results in real time so that they can check them while away from home. This allows for the provision of more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0089] The analysis unit can analyze audio data within a video and detect suspicious sounds. For example, the analysis unit can analyze audio data within a video and detect abnormal sounds. For example, the analysis unit can analyze audio data within a video and detect suspicious conversations. For example, the analysis unit can analyze audio data within a video and detect abnormal sound patterns. In this way, abnormal sounds can be detected by analyzing audio data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI perform the analysis of the audio data.

[0090] The analysis unit can analyze changes in temperature and humidity within the video and detect anomalies. For example, the analysis unit can analyze temperature data within the video and detect abnormal temperature changes. For example, the analysis unit can analyze humidity data within the video and detect abnormal humidity changes. For example, the analysis unit can analyze changes in temperature and humidity within the video and detect abnormal environmental changes. In this way, abnormal environmental changes can be detected by analyzing changes in temperature and humidity. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input temperature and humidity data into a generation AI and have the generation AI perform the analysis of environmental changes.

[0091] The reporting unit can estimate the user's emotions and adjust the timing of the report based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can quickly report to the police. For example, if the user is relaxed, the reporting unit can report to the police at the usual time. For example, if the user is out, the reporting unit can adjust the timing of the report depending on the situation at their location. By adjusting the timing of the report according to the user's emotions, it becomes possible to report at a more appropriate time. 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 reporting unit may be performed using or without a generative AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the timing of the report.

[0092] The reporting unit can attach a portion of a video when reporting to the police. For example, the reporting unit can attach a portion of a video showing suspicious behavior when reporting to the police. For example, the reporting unit can attach a portion of a video showing a criminal act when reporting to the police. For example, the reporting unit can attach a portion of a video that serves as evidence when reporting to the police. By attaching a portion of the video, it is possible to provide the police with more specific information. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input video data into a generation AI and have the generation AI perform the process of attaching a portion of the video to the report.

[0093] The reporting unit can include detailed information in the reported content. For example, when reporting to the police, the reporting unit can include detailed information about the suspicious behavior it has detected. For example, when reporting to the police, the reporting unit can include detailed information about the criminal act it has detected. For example, when reporting to the police, the reporting unit can include detailed information about crime prediction and risk assessment. By including detailed information, it is possible to provide the police with more accurate information. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input the content of the report into a generation AI and have the generation AI perform the processing of including detailed information.

[0094] The notification unit can estimate the user's emotions and determine the priority of the notification content based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit will prioritize reporting important information. For example, if the user is relaxed, the notification unit can report information with normal priority. For example, if the user is out, the notification unit can determine the priority of the notification content according to the situation at the user's location. This allows for prioritizing important information by determining the priority of the notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using or without a generative AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of the notification content.

[0095] The reporting unit can also report to relevant agencies other than the police. For example, the reporting unit can report to the fire department in addition to the police. For example, the reporting unit can report to the ambulance service in addition to the police. For example, the reporting unit can report to neighbors in addition to the police. By reporting to relevant agencies other than the police, a broader response becomes possible. Some or all of the above-described processes in the reporting unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the reporting unit can input the report content into a generation AI and have the generation AI execute the reporting to the relevant agencies.

[0096] The reporting unit can transmit the content of the report by voice. For example, when reporting to the police, the reporting unit can provide details of suspicious behavior by voice. For example, when reporting to the police, the reporting unit can provide details of criminal activity by voice. For example, when reporting to the police, the reporting unit can provide details of crime predictions and risk assessments by voice. This enables rapid and accurate information transmission by transmitting the content of the report by voice. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input the content of the report into a generation AI and have the generation AI perform the voice transmission.

[0097] The warning unit can estimate the user's emotions and adjust the warning method based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can issue a warning quickly. For example, if the user is relaxed, the warning unit can issue a warning at a normal time. For example, if the user is out, the warning unit can adjust the warning method according to the situation at the user's location. This allows for more appropriate warnings by adjusting the warning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 warning unit may be performed using the generative AI or not. For example, the warning unit can input user emotion data into the generative AI and have the generative AI adjust the warning method.

[0098] The warning unit can attach a portion of a video when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video showing suspicious behavior when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video showing a criminal act when issuing a warning to the relevant parties. For example, the warning unit can attach a portion of a video that serves as evidence when issuing a warning to the relevant parties. By attaching a portion of the video, more specific information can be provided to the relevant parties. Some or all of the above processing in the warning unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the warning unit can input video data into a generating AI and have the generating AI perform the process of attaching a portion of the video to the warning content.

[0099] The warning unit can include detailed information in the warning content. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about the suspicious behavior it has detected. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about the criminal act it has detected. For example, when issuing a warning to a person concerned, the warning unit can include detailed information about crime prediction and risk assessment. By including detailed information, it is possible to provide more accurate information to the person concerned. Some or all of the above processing in the warning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI perform the processing of including detailed information.

[0100] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated emotions. For example, if the user is feeling anxious, the warning unit will prioritize warnings about important information. For example, if the user is relaxed, the warning unit can warn with normal priority. For example, if the user is out, the warning unit can determine the priority of warnings based on the situation at their destination. This allows for prioritizing important information by determining the priority of warnings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using or without a generative AI. For example, the warning unit can input user emotion data into a generative AI and have the generative AI determine the priority of warnings.

[0101] The warning unit can issue warnings to nearby residents who are not directly involved. For example, the warning unit can issue warnings to nearby residents who are not directly involved. For example, the warning unit can issue evacuation orders to nearby residents who are not directly involved. For example, the warning unit can issue warnings to nearby residents who are not directly involved. This allows for a broader response by issuing warnings to nearby residents who are not directly involved. Some or all of the above-described processes in the warning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI issue a warning to nearby residents.

[0102] The warning unit can convey warning content by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of suspicious behavior by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of criminal acts by voice. For example, when issuing a warning to relevant parties, the warning unit can convey details of crime predictions and risk assessments by voice. This enables rapid and accurate information transmission by conveying warning content by voice. Some or all of the above processing in the warning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI perform the voice transmission.

[0103] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit will prioritize learning high-risk crime data. For example, if the user is relaxed, the learning unit can learn normal crime data. For example, if the user is out, the learning unit can select training data according to the situation at their location. This allows for more appropriate learning by selecting training data 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 learning unit may be performed using or without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0104] The learning unit can improve the accuracy of future crime predictions and risk assessments based on past warning and report data. For example, the learning unit can improve the accuracy of future crime predictions based on past warning data. For example, the learning unit can improve the accuracy of risk assessments based on past report data. For example, the learning unit can improve the accuracy of crime predictions and risk assessments based on past warning and report data. In this way, by learning from past warning and report data, the accuracy of future crime predictions and risk assessments can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input past warning and report data into a generative AI and have the generative AI perform the task of improving the accuracy of crime predictions and risk assessments.

[0105] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit will learn more frequently to improve accuracy. For example, if the user is relaxed, the learning unit can learn at a normal frequency. For example, if the user is out, the learning unit can adjust the learning frequency according to the situation at the user's location. This allows for more appropriate learning by adjusting the learning frequency 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 learning unit may be performed using the generative AI or not. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0106] The learning unit can learn not only past crime data but also crime data from the surrounding area. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of crime prediction. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of risk assessment. For example, the learning unit can learn past crime data and crime data from the surrounding area to improve the accuracy of future crime prediction and risk assessment. In this way, by learning crime data from the surrounding area as well, the accuracy of crime prediction and risk assessment can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input crime data from the surrounding area into a generative AI and have the generative AI perform the learning.

[0107] The learning unit can learn not only past crime data but also weather and seasonal data. For example, the learning unit can learn past crime data and weather data to improve the accuracy of crime prediction. For example, the learning unit can learn past crime data and seasonal data to improve the accuracy of risk assessment. For example, the learning unit can learn past crime data and weather and seasonal data to improve the accuracy of future crime prediction and risk assessment. In this way, by learning weather and seasonal data as well, the accuracy of crime prediction and risk assessment can be improved. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input weather and seasonal data into a generative AI and have the generative AI perform the learning.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The security system can estimate the user's emotions and automatically adjust the viewpoint of the surveillance camera based on those emotions. For example, if the user is feeling anxious, the surveillance camera can adjust its viewpoint to cover a wider area, and if the user is relaxed, it can focus on a specific area. Also, if the user is away from home, the surveillance camera's viewpoint can be adjusted according to the situation at their location. This enables flexible monitoring that responds to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. The adjustment of the surveillance camera's viewpoint may be performed using AI or not. For example, the generative AI can be used to perform the adjustment of the surveillance camera's viewpoint.

[0110] The security system can estimate the user's emotions and adjust the volume of the warning sound based on those emotions. For example, if the user is feeling anxious, the warning sound volume can be increased, and if the user is relaxed, the warning sound volume can be kept at a normal level. Furthermore, if the user is away from home, the warning sound volume can be adjusted according to the situation at their location. This enables appropriate warnings tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Warning sound volume adjustment may be performed using AI or not. For example, the generative AI can be used to adjust the warning sound volume.

[0111] The security system can estimate the user's emotions and adjust the content of warning messages based on those emotions. For example, if the user is feeling anxious, a detailed warning message can be displayed; if the user is relaxed, a concise warning message can be displayed. Furthermore, if the user is away from home, the content of the warning message can be adjusted according to the situation at their location. This enables the provision of appropriate information tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Adjustment of warning message content may be performed using AI or not. For example, the generative AI can be used to adjust the content of warning messages.

[0112] The security system can estimate the user's emotions and adjust the level of detail in the report based on those emotions. For example, if the user is feeling anxious, it can provide detailed information to the police; if the user is relaxed, it can provide concise information. Furthermore, if the user is away from home, the level of detail in the report can be adjusted according to the situation at their location. This enables appropriate reporting tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Adjusting the level of detail in the report may be done using AI or not. For example, the generative AI can be used to adjust the level of detail in the report.

[0113] The security system can estimate the user's emotions and prioritize training data based on those emotions. For example, if the user is feeling anxious, it can prioritize learning high-risk crime data; if the user is relaxed, it can prioritize learning normal crime data. Furthermore, if the user is out, the system can prioritize training data according to the situation at their location. This enables appropriate learning tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Prioritizing training data may be done using AI or not. For example, the generative AI can be used to prioritize training data.

[0114] The security system can identify the type and location of objects in surveillance camera footage when analyzing the footage. For example, it can identify the type of object in the footage and detect abnormal objects. It can identify the location of objects in the footage and detect objects in suspicious positions. It can also analyze the movement of objects in the footage and detect suspicious movements. In this way, by identifying the type and location of objects, abnormal objects and locations can be detected. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the identification of the type and location of objects.

[0115] The security system can analyze audio data within surveillance camera footage to detect suspicious sounds. For example, it can analyze audio data within the video to detect unusual sounds. It can analyze audio data within the video to detect suspicious conversations. It can also analyze audio data within the video to detect unusual sound patterns. In this way, by analyzing audio data, it is possible to detect unusual sounds. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI perform the analysis of the audio data.

[0116] The security system can analyze changes in temperature and humidity within surveillance camera footage to detect anomalies. For example, it can analyze temperature data within the footage to detect abnormal temperature changes. It can also analyze humidity data within the footage to detect abnormal humidity changes. Furthermore, it can analyze changes in temperature and humidity within the footage to detect abnormal environmental changes. In this way, abnormal environmental changes can be detected by analyzing changes in temperature and humidity. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input temperature and humidity data into a generation AI and have the generation AI perform the analysis of environmental changes.

[0117] The crime prevention system can attach a portion of the video footage when reporting to the police. For example, it can attach a portion of the video showing suspicious behavior when reporting to the police. It can also attach a portion of the video showing a criminal act when reporting to the police. Furthermore, it can attach a portion of the video footage that serves as evidence when reporting to the police. This allows for the provision of more specific information to the police by attaching a portion of the video footage. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input video data into a generation AI and have the generation AI perform the process of attaching a portion of the video footage to the report.

[0118] The security system can also notify relevant agencies other than the police. For example, it can notify the fire department in addition to the police. It can also notify emergency services in addition to the police. Furthermore, it can notify neighbors in addition to the police. This allows for a broader response by notifying relevant agencies other than the police. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the notification content into a generation AI and have the generation AI execute the notification to the relevant agencies.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects video footage from surveillance cameras. For example, it collects footage from surveillance cameras installed around and inside the house. The collection unit can collect footage from cameras installed in the entrance, garden, living room, etc. Furthermore, the collection unit can collect footage 24 hours a day, providing continuous monitoring. Footage can also be collected at night using infrared cameras. Step 2: The analysis unit analyzes the video collected by the collection unit to detect suspicious behavior and criminal activity. For example, it checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior and criminal activity. It can detect situations such as a suspicious person loitering around a house or attempting to break a window. Using generated AI, it can analyze the information in the video and detect suspicious behavior and criminal activity with high accuracy. Step 3: The reporting unit notifies the police based on the information detected by the analysis unit. For example, it can quickly notify the police based on the detected information. It can predict crimes and assess the level of danger before notifying the police. It can use AI to optimize the timing and content of the report. Step 4: The warning unit issues warnings and evacuation notices to relevant parties based on the information detected by the analysis unit. For example, it can issue evacuation orders to family members or warn neighbors. AI can be used to optimize the content and timing of warnings and evacuation notices. Step 5: The learning unit learns from the information obtained by the analysis unit and makes predictions about future crimes and assesses the level of risk. For example, it can predict crimes at specific times and locations based on past crime data. AI can be used to improve the accuracy of the learning process.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, warning unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to collect images of the surroundings and interior of the house. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected images to detect suspicious behavior or criminal activity. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the police based on the detected information. The warning unit is implemented in the control unit 46A of the smart device 14, for example, and issues warnings and evacuation notices to those involved. The learning unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and learns based on the information obtained by the analysis unit to predict future crimes and determine the degree of risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, warning unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to collect images of the surroundings and interior of the house. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected images to detect suspicious behavior or criminal activity. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the police based on the detected information. The warning unit is implemented in the control unit 46A of the smart glasses 214, for example, and issues warnings and evacuation notices to those involved. The learning unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and learns based on the information obtained by the analysis unit to predict future crimes and determine the degree of risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, warning unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to collect images of the surroundings and interior of the house. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected images to detect suspicious behavior or criminal activity. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies the police based on the detected information. The warning unit is implemented in the control unit 46A of the headset terminal 314, for example, and issues warnings and evacuation notices to those involved. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and learns based on the information obtained by the analysis unit to predict future crimes and determine the degree of risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, warning unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to collect images of the surroundings and interior of the house. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected images to detect suspicious behavior or criminal activity. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which notifies the police based on the detected information. The warning unit is implemented, for example, by the control unit 46A of the robot 414, which issues warnings and evacuation notices to those involved. The learning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which learns based on the information obtained by the analysis unit and makes predictions about future crimes and assesses the degree of danger. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The collection unit collects footage from surveillance cameras, An analysis unit analyzes the video footage collected by the aforementioned collection unit to detect suspicious behavior or criminal activity, A reporting unit that notifies the police based on the information detected by the aforementioned analysis unit, A warning unit that issues warnings and evacuation notices to relevant parties based on the information detected by the aforementioned analysis unit, The system includes a learning unit that learns from the information obtained by the analysis unit and performs future crime predictions and risk assessments. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect footage from surveillance cameras installed around and inside the house. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior or criminal activity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, Based on the detected information, we will promptly report it to the police. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warning unit is Issue warnings and evacuation notices to those involved. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Based on past crime data, crime predictions are made for specific time periods and locations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collect video footage focusing on the exterior and specific areas inside the house. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Change the method of collecting video footage at different time periods. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Environmental data such as temperature and humidity around and inside the house are also collected simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system also collects audio data from around and inside the house. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Analyze in detail the movement patterns of people in the video. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Identify the type and location of objects in the video. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It analyzes audio data within the video to detect suspicious sounds. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It analyzes changes in temperature and humidity within the video to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting unit, When reporting to the police, attach a portion of the video footage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, Include detailed information in your report. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes the content of reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, We will also report this to other relevant organizations besides the police. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The content of the report will be conveyed by voice. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is It estimates the user's emotions and adjusts the warning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warning unit is When issuing a warning to those involved, attach a portion of the video. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warning unit is Include detailed information in the warning message. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warning content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned warning unit is Warnings will also be issued to nearby residents who are not directly involved. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warning unit is The warning message is delivered via voice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, Based on past warnings and reports, we aim to improve the accuracy of future crime predictions and risk assessments. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It learns not only from past crime data, but also from crime data in the surrounding area. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, It learns not only from past crime data, but also from weather and seasonal data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 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. The collection unit collects footage from surveillance cameras, An analysis unit analyzes the video footage collected by the aforementioned collection unit to detect suspicious behavior or criminal activity, A reporting unit that notifies the police based on the information detected by the aforementioned analysis unit, A warning unit that issues warnings and evacuation notices to relevant parties based on the information detected by the aforementioned analysis unit, The system includes a learning unit that learns from the information obtained by the analysis unit and performs future crime predictions and risk assessments. A system characterized by the following features.

2. The aforementioned collection unit is Collect footage from surveillance cameras installed around and inside the house. The system according to feature 1.

3. The aforementioned analysis unit, The system checks the people, number of people, time of day, actions, belongings, and clothing in the video to detect suspicious behavior or criminal activity. The system according to feature 1.

4. The aforementioned reporting unit, Based on the detected information, we will promptly report it to the police. The system according to feature 1.

5. The aforementioned warning unit is Issue warnings and evacuation notices to those involved. The system according to feature 1.

6. The aforementioned learning unit, Based on past crime data, crime predictions are made for specific time periods and locations. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is Collect video footage focusing on the exterior and specific areas inside the house. The system according to feature 1.

Images