system

Abstract

The system according to this embodiment aims to detect abnormal behavior and intruders and provide effective countermeasures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a dialogue unit. The collection unit collects data from surveillance cameras and sensors. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormal behavior or intruders based on the data analyzed by the analysis unit. The notification unit notifies the owner of the abnormal behavior or intruder detected by the detection unit. The dialogue unit provides countermeasures in a dialogue format based on the information notified by the notification unit.

Description

Technical Field

[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 is room for improvement in not only detecting abnormal behavior and intruders in a home security system but also providing effective countermeasures.

[0005] The system according to the embodiment aims to detect abnormal behavior and intruders and provide effective countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, a notification unit, and a dialogue unit. The data collection unit collects data from surveillance cameras and sensors. The analysis unit analyzes the data collected by the data collection unit. The detection unit detects abnormal behavior or intruders based on the data analyzed by the analysis unit. The notification unit notifies the owner of the abnormal behavior or intruder detected by the detection unit. The dialogue unit provides countermeasures in a dialogue format based on the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormal behavior and intruders and provide effective countermeasures. [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 controls communication between multiple computers. Examples of communication standards applicable 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 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home security system according to an embodiment of the present invention is a system that enhances home security by leveraging the advanced understanding capabilities of LLM and multimodal AI and their collaboration with surveillance cameras and sensors. This home security system collects data from surveillance cameras and sensors in real time and recognizes the environment using LLM and multimodal AI. If abnormal behavior or an intruder is detected, the system immediately notifies the owner and provides interactive support for effective countermeasures based on environmental analysis. This ensures the safety of the home. For example, the home security system collects data from surveillance cameras and sensors in real time. Cameras capture images of the home, and sensors detect the opening and closing of doors and windows. This data is transmitted to the home security system. Next, the home security system uses LLM and multimodal AI to analyze the collected data and recognize the environment. It detects human movement from camera footage and confirms the opening and closing of doors and windows from sensor data. This allows the system to understand the situation inside the home. If abnormal behavior or an intruder is detected, the home security system immediately notifies the owner. For example, if suspicious activity is detected late at night, a notification is sent to the owner's smartphone. This allows homeowners to instantly become aware of any anomalies. Furthermore, the home security system provides interactive support for effective countermeasures based on environmental analysis. For example, if a homeowner asks, "What should I do?", the home security system will provide specific advice such as, "Contact the police" or "Evacuate to a safe place." This enables homeowners to take appropriate action. This mechanism helps protect the safety of the home. For example, it can be expected to reduce crime rates and shorten emergency response times. It also improves users' sense of security, allowing them to live with peace of mind. In this way, the home security system can protect the safety of the home.

[0029] The home security system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, a notification unit, and a dialogue unit. The data collection unit collects data from surveillance cameras and sensors. For example, the data collection unit can collect data from surveillance cameras that capture images of the home and sensors that detect the opening and closing of doors and windows. The data collection unit can collect this data in real time. For example, the data collection unit collects video data from cameras and detection data from sensors. The data collection unit may include AI processing. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using LLM or multimodal AI. For example, the analysis unit detects human movement from camera footage and confirms the opening and closing of doors and windows from sensor data. The analysis unit includes AI processing. The detection unit detects abnormal behavior and intruders based on the data analyzed by the analysis unit. For example, the detection unit detects suspicious movements from the analyzed data. The detection unit includes AI processing. The notification unit notifies the owner of the abnormal behavior or intruder detected by the detection unit. The notification unit sends a notification to the owner's smartphone, for example. The notification unit may include AI processing. The dialogue unit provides a response in a dialogue format based on the information notified by the notification unit. For example, if the owner asks "What should I do?", the dialogue unit provides specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit includes AI processing. In this way, the home security system according to the embodiment can protect the safety of the home.

[0030] The data collection unit collects data from surveillance cameras and sensors. Specifically, surveillance cameras continuously film the interior of the home 24 hours a day, collecting video data in real time. The cameras are high-resolution and equipped with night vision, providing clear images day and night. Furthermore, the cameras use wide-angle lenses, covering a wide field of view. Sensors are installed to detect the opening and closing of doors and windows, using magnetic and vibration sensors. These sensors emit signals the moment a door or window is opened or closed, and transmit this data to the data collection unit. The data collection unit centrally manages this data and stores it in a central database in real time. The data collection unit can include AI processing, such as pre-processing video data and filtering sensor data. This allows the data collection unit to provide high-quality data with less noise. In addition, the data collection unit can adjust the frequency and sensitivity of data collection, allowing for flexible responses to specific situations and conditions. For example, by increasing the sensitivity of sensors at night or when away from home, and decreasing the sensitivity during the day or when at home, false detections can be reduced. This allows the data collection unit to efficiently and effectively collect data and enhance home security.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses LLM and multimodal AI to analyze the collected data from multiple perspectives. Specifically, it analyzes camera video data and uses image recognition technology to detect human movement. For example, it analyzes the posture and movement of people in the video to identify suspicious movements that differ from normal movements. It also analyzes sensor data to check the opening and closing of doors and windows. By analyzing the changes in sensor data over time, it can detect abnormal opening and closing patterns. Because the analysis unit includes AI processing, it can analyze data quickly and understand the situation in real time. Furthermore, the analysis unit can learn patterns of abnormal behavior by utilizing past data and statistical information, and predict future risks. For example, based on data from past intrusion incidents, it can evaluate the risk at specific times and locations and propose preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to quickly and accurately analyze collected data, strengthening home security.

[0032] The detection unit detects abnormal behavior and intruders based on data analyzed by the analysis unit. Specifically, it uses an AI-based anomaly detection algorithm to detect suspicious movements from the analyzed data. For example, if the movement of a person detected from camera footage differs from the normal pattern, the detection unit will determine that movement is abnormal. It also detects suspicious opening and closing of doors and windows from sensor data. For example, if a door is opened or closed in the middle of the night, or if a window that is not normally used is opened, the detection unit will determine that it is abnormal. The detection unit can detect these anomalies in real time and respond immediately. Furthermore, the detection unit can learn patterns of abnormal behavior based on past data and statistical information and predict future risks. For example, it can analyze the frequency of abnormal behavior occurring at specific times and locations to identify high-risk times and locations. This allows the detection unit to enhance home security and detect abnormal behavior and intruders at an early stage.

[0033] The notification unit notifies the owner of abnormal behavior or intruders detected by the detection unit. Specifically, it uses communication methods such as push notifications, SMS, and email to send notifications to the owner's smartphone. For example, if abnormal behavior is detected while the owner is away from home, a notification will be sent to their smartphone in real time. The notification will include details of the abnormal behavior, video data, and sensor data, allowing the owner to immediately check the situation. Furthermore, because the notification unit incorporates AI processing, it can automatically generate notification content and send it at the appropriate time. For example, it can send a notification immediately after abnormal behavior is detected, and then send a follow-up notification after the owner has confirmed the initial notification. The notification unit can also collect feedback from the owner and continuously improve the accuracy and effectiveness of the notification content. For example, by providing feedback on the notification content, the notification unit can adjust the notification content based on that feedback to provide more effective notifications. In this way, the notification unit can provide owners with quick and accurate information and enhance home security.

[0034] The dialogue unit provides solutions in a conversational format based on information notified by the notification unit. Specifically, when the owner asks a question in a conversational format via a smartphone or other device, the dialogue unit uses AI to provide an appropriate answer. For example, if the owner asks "What should I do?", the dialogue unit will provide specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit uses natural language processing technology to understand the owner's question and generate an appropriate answer. Furthermore, the dialogue unit can learn from past conversation history and the owner's behavior patterns to provide more personalized advice. For example, if the owner has previously selected a particular solution, the dialogue unit will suggest the same solution in a similar situation based on that history. The dialogue unit also allows for flexible responses by presenting multiple solutions and allowing the owner to choose. This enables the dialogue unit to provide owners with quick and appropriate solutions, thereby enhancing home security.

[0035] The data collection unit can collect data from surveillance cameras and sensors in real time. For example, the data collection unit can collect data from surveillance cameras that film the inside of a home and from sensors that detect the opening and closing of doors and windows. The data collection unit collects this data in real time. For example, the data collection unit collects video data from cameras and detection data from sensors in real time. This allows the data collection unit to immediately detect anomalies. Specific definitions and criteria for "real time" include, for example, the delay time of data collection and the update frequency. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can collect data from surveillance cameras and sensors in real time and input that data into AI for analysis.

[0036] The analysis unit can analyze the collected data using LLM or multimodal AI. For example, the analysis unit can use LLM to analyze camera video data and detect human movement. The analysis unit can also use multimodal AI to integrate and analyze camera video data and sensor detection data. This allows the analysis unit to improve the accuracy of data analysis. Specific types and methods of implementing multimodal AI include, for example, integrated analysis of images and text. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can then analyze the data and detect anomalies.

[0037] The detection unit can detect abnormal behavior and intruders based on the analyzed data. For example, the detection unit can detect suspicious movements from the analyzed data. The detection unit includes AI processing. Specific definitions and criteria for abnormal behavior include, for example, suspicious movements or prolonged stays. Specific definitions and criteria for intruders include, for example, unauthorized persons or animals. This allows the detection unit to accurately detect abnormal behavior and intruders based on the analyzed data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into the AI, which can then detect abnormal behavior or intruders.

[0038] The notification unit can immediately notify the owner of abnormal behavior or intruders detected by the detection unit. The notification unit can, for example, send a notification to the owner's smartphone. The notification unit may include AI processing. The specific definition and criteria of "immediately" include, for example, the delay time before notification. This allows the notification unit to immediately notify the owner of abnormal behavior or intruders. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input information about detected abnormal behavior or intruders into the AI, and the AI ​​can send a notification to the owner.

[0039] The dialogue unit can provide effective countermeasures in a conversational format based on information notified by the notification unit. For example, if the owner asks "What should I do?", the dialogue unit will provide specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit includes AI processing. Specific content and criteria for effective countermeasures include, for example, reporting to the police or sounding an alarm. This allows the dialogue unit to enable the owner to take appropriate action. Some or all of the processing described above in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the notified information into the AI, which can then provide effective countermeasures in a conversational format.

[0040] The data collection unit can focus on collecting data from specific areas within the home. For example, it can focus on collecting data from entry points such as front doors and windows. It can also focus on collecting data from important areas such as children's rooms and bedrooms. It can also focus on collecting data from external areas such as garages and gardens. This allows the data collection unit to enhance monitoring of important areas by focusing data collection on specific areas. The specific scope and criteria for a particular area could be, for example, the front door or living room. Some or all of the processing described above in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data from a specific area within the home into an AI, which can then enhance monitoring of that area.

[0041] The data collection unit can collect data by combining different types of sensors. For example, it can combine a temperature sensor and a sound sensor to detect abnormal temperature changes or sounds. It can also combine a humidity sensor and a motion sensor to detect abnormal humidity changes or motion. It can also combine a light sensor and a vibration sensor to detect abnormal light changes or vibrations. In this way, the data collection unit can improve the accuracy of anomaly detection by collecting data by combining different types of sensors. Specific types of sensors and implementation methods include, for example, temperature sensors and motion sensors. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data from different types of sensors into an AI, which can then analyze the data to detect anomalies.

[0042] The data collection unit can collect data while taking into account the movements of pets in the home. For example, the data collection unit can detect pet movements and filter the data to prevent false positives. The data collection unit can also adjust the frequency of data collection, taking into account the pet's activity times. The data collection unit can also determine the pet's location and collect data to detect abnormal movements. In this way, the data collection unit can prevent false positives by collecting data while taking pet movements into account. Specific methods and criteria for considering pet movements include, for example, movement patterns and specific behaviors. 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 pet movement data into AI, which can analyze the data to prevent false positives.

[0043] The data collection unit can collect electricity consumption data within a household and detect abnormal consumption patterns. For example, the data collection unit can collect electricity consumption data within a household in real time and detect abnormal consumption patterns. The data collection unit can also analyze the electricity consumption data and notify the owner of abnormal consumption patterns. Based on the electricity consumption data, the data collection unit can also identify the cause of abnormal consumption patterns. In this way, by collecting electricity consumption data and detecting abnormal consumption patterns, the data collection unit can detect anomalies early. Specific definitions and criteria for abnormal consumption patterns include, for example, comparisons with normal consumption or sudden 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 electricity consumption data into AI, which can then detect abnormal consumption patterns.

[0044] The analysis unit can detect anomalies by comparing them with past data. For example, the analysis unit can detect abnormal movements or sounds by comparing them with past data. The analysis unit can also detect abnormal changes in temperature or humidity by comparing them with past data. The analysis unit can also detect abnormal power consumption patterns by comparing them with past data. This allows the analysis unit to detect anomalies early by detecting them by comparing them with past data. The specific scope and criteria for past data may be, for example, data from the past month or data from the past year. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input past data into AI, and the AI ​​can detect anomalies by comparing them with the past data.

[0045] The analysis unit can detect anomalies by considering the temporal changes in the data. For example, the analysis unit can detect abnormal movements or sounds by considering the temporal changes in the data. The analysis unit can also detect abnormal temperature or humidity changes by considering the temporal changes in the data. The analysis unit can also detect abnormal power consumption patterns by considering the temporal changes in the data. In this way, the analysis unit can improve the accuracy of anomaly detection by considering the temporal changes in the data. Specific methods and criteria for considering temporal changes include, for example, changes by time of day or changes by season. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. For example, the analysis unit can input the temporal changes in the data into AI, and the AI ​​can detect anomalies by considering the temporal changes.

[0046] The analysis unit can analyze household energy consumption data and detect abnormal consumption patterns. For example, the analysis unit can analyze household energy consumption data and detect abnormal consumption patterns. Based on the energy consumption data, the analysis unit can also identify the cause of the abnormal consumption pattern. The analysis unit can also analyze the energy consumption data and notify the owner of any abnormal consumption patterns. This allows the analysis unit to detect abnormalities early by analyzing the energy consumption data and detecting abnormal consumption patterns. Specific definitions and criteria for abnormal consumption patterns include, for example, comparisons with normal consumption levels or sudden changes. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input energy consumption data into AI, which can then detect abnormal consumption patterns.

[0047] The analysis unit can analyze audio data within a home and detect abnormal sounds. For example, the analysis unit can analyze audio data within a home and detect abnormal sounds. Based on the audio data, the analysis unit can also identify the cause of the abnormal sound. The analysis unit can also analyze the audio data and notify the owner of any abnormal sounds. This allows the analysis unit to detect abnormalities early by analyzing audio data and detecting abnormal sounds. Specific definitions and criteria for abnormal sounds include, for example, comparison with normal sounds or specific frequencies. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input audio data into an AI, which can then detect abnormal sounds.

[0048] The detection unit can detect anomalies by integrating data from different sensors. For example, the detection unit can detect anomalies by integrating data from a temperature sensor and a sound sensor. The detection unit can also detect anomalies by integrating data from a humidity sensor and a motion sensor. The detection unit can also detect anomalies by integrating data from a light sensor and a vibration sensor. In this way, the detection unit can improve the accuracy of anomaly detection by integrating data from different sensors. Specific types of different sensors and implementation methods include, for example, temperature sensors and motion sensors. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input data from different sensors into AI, and the AI ​​can integrate the data to detect anomalies.

[0049] The detection unit can detect anomalies by considering the movements of pets in the home. For example, the detection unit can detect pet movements and filter the data to prevent false positives. The detection unit can also detect anomalies by considering the pet's activity times. The detection unit can also grasp the pet's location information and detect abnormal movements. In this way, the detection unit can prevent false positives by detecting anomalies by considering the movements of pets in the home. Specific methods and criteria for considering pet movements include, for example, movement patterns and specific behaviors. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input pet movement data into AI, and the AI ​​can analyze the data to prevent false positives.

[0050] The detection unit can detect anomalies by considering household power consumption data. For example, the detection unit can analyze household power consumption data in real time and detect anomalies. The detection unit can also detect abnormal consumption patterns based on power consumption data. The detection unit can analyze power consumption data and notify the owner of abnormal consumption patterns. In this way, the detection unit can improve the accuracy of anomaly detection by considering household power consumption data. Specific methods and criteria for collecting power consumption data include, for example, methods for measuring consumption and methods for recording data. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input power consumption data into AI, and the AI ​​can detect abnormal consumption patterns.

[0051] The notification unit can send notifications by combining different notification methods. For example, it can send notifications by combining email and SMS. It can also send notifications by combining app notifications and voice notifications. It can also send notifications by combining email, SMS, and app notifications. In this way, the notification unit can reliably send notifications by combining different notification methods. Specific types and methods of implementation of different notification methods include, for example, email, SMS, and app notifications. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input different notification methods into AI, and the AI ​​can send notifications.

[0052] The notification unit can customize notification content and provide it to the user. For example, the notification unit can customize notification content according to the user's preferences. The notification unit can also customize notification content by referring to the user's past notification history. The notification unit can also customize notification content according to the user's current situation. In this way, the notification unit can provide the user with the most relevant information by customizing notification content. Specific methods and criteria for customizing notification content include, for example, notification priority and level of detail. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input notification content into AI, and the AI ​​can customize the notification content.

[0053] The notification unit can coordinate with other devices in the home to provide notifications. For example, the notification unit can use a smart speaker to provide voice notifications. The notification unit can also use smart lights to provide visual notifications. The notification unit can coordinate smart speakers and smart lights to provide notifications. This allows the notification unit to provide effective notifications by coordinating with other devices in the home. Specific types of other devices and methods of implementation include, for example, smart speakers and smart lights. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input other devices into the AI, which can then coordinate the devices to provide notifications.

[0054] The notification unit can select the optimal notification method by considering the user's current location information. For example, if the user is at home, the notification unit can send a notification using a smart speaker. If the user is out, the notification unit can also send a notification to a smartphone. If the user is in a car, the notification unit can also send a notification using the in-car system. This allows the notification unit to select the optimal notification method based on the user's location information. Specific methods and criteria for acquiring the current location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's location information into the AI, which can then select the optimal notification method.

[0055] The dialogue unit can provide optimal advice by referring to past dialogue history. For example, the dialogue unit can refer to the user's past dialogue history and provide optimal advice. The dialogue unit can also provide advice tailored to the user's preferences based on past dialogue history. The dialogue unit can also analyze past dialogue history and provide advice tailored to the user's situation. In this way, the dialogue unit can provide the best possible advice for the user by referring to past dialogue history. Specific methods for saving and referencing past dialogue history include, for example, the retention period and search method for dialogue logs. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input past dialogue history into AI, and the AI ​​can provide optimal advice.

[0056] The dialogue unit can customize the content of the conversation according to the user's current situation. For example, if the user is at home, the dialogue unit will provide content that is appropriate for the situation at home. If the user is out, the dialogue unit can also provide content that is appropriate for the situation at their destination. If the user is in a car, the dialogue unit can also provide content that is appropriate for the situation inside the car. In this way, the dialogue unit can customize the content of the conversation according to the user's current situation. Specific methods and criteria for understanding the current situation include, for example, the user's current activities and the surrounding environment. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's current situation data into the AI, which can then customize the content of the conversation.

[0057] The dialogue unit can interact with other devices in the home in conjunction with it. For example, the dialogue unit can use a smart speaker for voice interaction. The dialogue unit can also use a smart light for visual interaction. The dialogue unit can also interact with a smart speaker and a smart light in conjunction with each other. This allows the dialogue unit to engage in effective interaction by coordinating with other devices in the home. Specific examples of other devices and methods of implementation include smart speakers and smart lights. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input other devices into the AI, which can then coordinate the devices to engage in interaction.

[0058] The dialogue unit can provide optimal advice by taking into account the user's current location. For example, if the user is at home, the dialogue unit can provide advice tailored to the situation at home. If the user is out, the dialogue unit can also provide advice tailored to the situation at their destination. If the user is in a car, the dialogue unit can also provide advice tailored to the situation inside the car. This allows the dialogue unit to provide optimal advice based on the user's location. Specific methods and criteria for acquiring the current location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's location information into the AI, which can then provide optimal advice.

[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] Home security systems can also be equipped with a voice recognition unit. This unit can analyze sounds within the home in real time and detect unusual noises. For example, if it detects unusual sounds such as breaking glass or screaming, it can immediately notify the homeowner. Furthermore, the voice recognition unit can learn the normal sound environment within the home to improve the accuracy of its abnormal sound detection. In addition, the voice recognition unit can recognize the homeowner's voice commands, allowing the system to be operated by voice. This enables voice-based anomaly detection and operation, providing a higher level of security.

[0061] Home security systems can also be equipped with a temperature monitoring unit. This unit can monitor the temperature inside the home in real time and detect abnormal temperature changes. For example, it can detect an abnormal temperature rise in the early stages of a fire and immediately notify the homeowner. Furthermore, the temperature monitoring unit can learn the normal temperature environment inside the home, improving the accuracy of abnormal temperature detection. In addition, the temperature monitoring unit can analyze the temperature data inside the home and suggest improvements to energy efficiency. This allows home security systems to utilize temperature-based anomaly detection and energy efficiency improvements, providing greater security and comfort.

[0062] Home security systems can also be equipped with a vibration detection unit. This unit can monitor vibrations within the home in real time and detect abnormal vibrations. For example, it can detect abnormal vibrations such as broken windows or forced doors and immediately notify the homeowner. Furthermore, the vibration detection unit can learn the normal vibration environment within the home, improving the accuracy of abnormal vibration detection. In addition, the vibration detection unit can detect the initial vibrations of natural disasters such as earthquakes and issue warnings to the homeowner. This allows home security systems to utilize vibration-based anomaly detection and natural disaster warnings, providing a higher level of security and safety.

[0063] The home security system can also be equipped with an air quality monitoring unit. This unit can monitor the air quality within the home in real time and detect abnormal changes in air quality. For example, it can detect increases in the concentration of carbon monoxide or harmful gases and immediately notify the homeowner. Furthermore, the air quality monitoring unit can learn the normal air quality environment within the home, improving the accuracy of abnormal air quality detection. In addition, the air quality monitoring unit can analyze the air quality data within the home and suggest ventilation and air purification measures. This allows the home security system to utilize air quality for anomaly detection and health management, providing greater security and comfort.

[0064] Home security systems can also include a lighting control unit. This unit can control the home's lighting in real time and adjust it according to abnormal situations. For example, if an intruder enters, the lights can be turned on to deter them. Furthermore, the lighting control unit can automatically adjust the lighting according to the family's daily routine, providing a comfortable environment. In addition, the lighting control unit can automatically turn off unnecessary lights, taking energy efficiency into consideration. This allows home security systems to utilize lighting for anomaly detection and provide a comfortable environment, offering higher levels of security and comfort.

[0065] The home security system can also be equipped with a smart appliance integration unit. This unit can connect with smart appliances in the home and control them in response to abnormal situations. For example, if a fire is detected, the unit can automatically activate a ventilation fan to expel smoke. Furthermore, the unit can automatically control appliances according to the family's daily routine, providing a comfortable environment. In addition, the unit can automatically turn off unnecessary appliances to improve energy efficiency. This allows the home security system to utilize smart appliances for anomaly detection and provide a comfortable environment, offering higher security and comfort.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data from surveillance cameras and sensors. For example, surveillance cameras may film the inside of a home, and sensors may detect the opening and closing of doors and windows. The data collection unit can collect this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses LLM or multimodal AI to analyze the collected data. For example, it can detect human movement from camera footage and confirm the opening and closing of doors and windows from sensor data. Step 3: The detection unit detects abnormal behavior or intruders based on the data analyzed by the analysis unit. For example, it detects suspicious movements from the analyzed data. Step 4: The notification unit notifies the owner of any abnormal behavior or intruder detected by the detection unit. For example, it sends a notification to the owner's smartphone. Step 5: The Dialogue Unit provides solutions in a dialogue format based on the information provided by the Notification Unit. For example, if the owner asks, "What should I do?", it will provide specific advice such as, "Contact the police" or "Evacuate to a safe place."

[0068] (Example of form 2) The home security system according to an embodiment of the present invention is a system that enhances home security by leveraging the advanced understanding capabilities of LLM and multimodal AI and their collaboration with surveillance cameras and sensors. This home security system collects data from surveillance cameras and sensors in real time and recognizes the environment using LLM and multimodal AI. If abnormal behavior or an intruder is detected, the system immediately notifies the owner and provides interactive support for effective countermeasures based on environmental analysis. This ensures the safety of the home. For example, the home security system collects data from surveillance cameras and sensors in real time. Cameras capture images of the home, and sensors detect the opening and closing of doors and windows. This data is transmitted to the home security system. Next, the home security system uses LLM and multimodal AI to analyze the collected data and recognize the environment. It detects human movement from camera footage and confirms the opening and closing of doors and windows from sensor data. This allows the system to understand the situation inside the home. If abnormal behavior or an intruder is detected, the home security system immediately notifies the owner. For example, if suspicious activity is detected late at night, a notification is sent to the owner's smartphone. This allows homeowners to instantly become aware of any anomalies. Furthermore, the home security system provides interactive support for effective countermeasures based on environmental analysis. For example, if a homeowner asks, "What should I do?", the home security system will provide specific advice such as, "Contact the police" or "Evacuate to a safe place." This enables homeowners to take appropriate action. This mechanism helps protect the safety of the home. For example, it can be expected to reduce crime rates and shorten emergency response times. It also improves users' sense of security, allowing them to live with peace of mind. In this way, the home security system can protect the safety of the home.

[0069] The home security system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, a notification unit, and a dialogue unit. The data collection unit collects data from surveillance cameras and sensors. For example, the data collection unit can collect data from surveillance cameras that capture images of the home and sensors that detect the opening and closing of doors and windows. The data collection unit can collect this data in real time. For example, the data collection unit collects video data from cameras and detection data from sensors. The data collection unit may include AI processing. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using LLM or multimodal AI. For example, the analysis unit detects human movement from camera footage and confirms the opening and closing of doors and windows from sensor data. The analysis unit includes AI processing. The detection unit detects abnormal behavior and intruders based on the data analyzed by the analysis unit. For example, the detection unit detects suspicious movements from the analyzed data. The detection unit includes AI processing. The notification unit notifies the owner of the abnormal behavior or intruder detected by the detection unit. The notification unit sends a notification to the owner's smartphone, for example. The notification unit may include AI processing. The dialogue unit provides a response in a dialogue format based on the information notified by the notification unit. For example, if the owner asks "What should I do?", the dialogue unit provides specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit includes AI processing. In this way, the home security system according to the embodiment can protect the safety of the home.

[0070] The data collection unit collects data from surveillance cameras and sensors. Specifically, surveillance cameras continuously film the interior of the home 24 hours a day, collecting video data in real time. The cameras are high-resolution and equipped with night vision, providing clear images day and night. Furthermore, the cameras use wide-angle lenses, covering a wide field of view. Sensors are installed to detect the opening and closing of doors and windows, using magnetic and vibration sensors. These sensors emit signals the moment a door or window is opened or closed, and transmit this data to the data collection unit. The data collection unit centrally manages this data and stores it in a central database in real time. The data collection unit can include AI processing, such as pre-processing video data and filtering sensor data. This allows the data collection unit to provide high-quality data with less noise. In addition, the data collection unit can adjust the frequency and sensitivity of data collection, allowing for flexible responses to specific situations and conditions. For example, by increasing the sensitivity of sensors at night or when away from home, and decreasing the sensitivity during the day or when at home, false detections can be reduced. This allows the data collection unit to efficiently and effectively collect data and enhance home security.

[0071] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses LLM and multimodal AI to analyze the collected data from multiple perspectives. Specifically, it analyzes camera video data and uses image recognition technology to detect human movement. For example, it analyzes the posture and movement of people in the video to identify suspicious movements that differ from normal movements. It also analyzes sensor data to check the opening and closing of doors and windows. By analyzing the changes in sensor data over time, it can detect abnormal opening and closing patterns. Because the analysis unit includes AI processing, it can analyze data quickly and understand the situation in real time. Furthermore, the analysis unit can learn patterns of abnormal behavior by utilizing past data and statistical information, and predict future risks. For example, based on data from past intrusion incidents, it can evaluate the risk at specific times and locations and propose preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to quickly and accurately analyze collected data, strengthening home security.

[0072] The detection unit detects abnormal behavior and intruders based on data analyzed by the analysis unit. Specifically, it uses an AI-based anomaly detection algorithm to detect suspicious movements from the analyzed data. For example, if the movement of a person detected from camera footage differs from the normal pattern, the detection unit will determine that movement is abnormal. It also detects suspicious opening and closing of doors and windows from sensor data. For example, if a door is opened or closed in the middle of the night, or if a window that is not normally used is opened, the detection unit will determine that it is abnormal. The detection unit can detect these anomalies in real time and respond immediately. Furthermore, the detection unit can learn patterns of abnormal behavior based on past data and statistical information and predict future risks. For example, it can analyze the frequency of abnormal behavior occurring at specific times and locations to identify high-risk times and locations. This allows the detection unit to enhance home security and detect abnormal behavior and intruders at an early stage.

[0073] The notification unit notifies the owner of abnormal behavior or intruders detected by the detection unit. Specifically, it uses communication methods such as push notifications, SMS, and email to send notifications to the owner's smartphone. For example, if abnormal behavior is detected while the owner is away from home, a notification will be sent to their smartphone in real time. The notification will include details of the abnormal behavior, video data, and sensor data, allowing the owner to immediately check the situation. Furthermore, because the notification unit incorporates AI processing, it can automatically generate notification content and send it at the appropriate time. For example, it can send a notification immediately after abnormal behavior is detected, and then send a follow-up notification after the owner has confirmed the initial notification. The notification unit can also collect feedback from the owner and continuously improve the accuracy and effectiveness of the notification content. For example, by providing feedback on the notification content, the notification unit can adjust the notification content based on that feedback to provide more effective notifications. In this way, the notification unit can provide owners with quick and accurate information and enhance home security.

[0074] The dialogue unit provides solutions in a conversational format based on information notified by the notification unit. Specifically, when the owner asks a question in a conversational format via a smartphone or other device, the dialogue unit uses AI to provide an appropriate answer. For example, if the owner asks "What should I do?", the dialogue unit will provide specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit uses natural language processing technology to understand the owner's question and generate an appropriate answer. Furthermore, the dialogue unit can learn from past conversation history and the owner's behavior patterns to provide more personalized advice. For example, if the owner has previously selected a particular solution, the dialogue unit will suggest the same solution in a similar situation based on that history. The dialogue unit also allows for flexible responses by presenting multiple solutions and allowing the owner to choose. This enables the dialogue unit to provide owners with quick and appropriate solutions, thereby enhancing home security.

[0075] The data collection unit can collect data from surveillance cameras and sensors in real time. For example, the data collection unit can collect data from surveillance cameras that film the inside of a home and from sensors that detect the opening and closing of doors and windows. The data collection unit collects this data in real time. For example, the data collection unit collects video data from cameras and detection data from sensors in real time. This allows the data collection unit to immediately detect anomalies. Specific definitions and criteria for "real time" include, for example, the delay time of data collection and the update frequency. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can collect data from surveillance cameras and sensors in real time and input that data into AI for analysis.

[0076] The analysis unit can analyze the collected data using LLM or multimodal AI. For example, the analysis unit can use LLM to analyze camera video data and detect human movement. The analysis unit can also use multimodal AI to integrate and analyze camera video data and sensor detection data. This allows the analysis unit to improve the accuracy of data analysis. Specific types and methods of implementing multimodal AI include, for example, integrated analysis of images and text. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can then analyze the data and detect anomalies.

[0077] The detection unit can detect abnormal behavior and intruders based on the analyzed data. For example, the detection unit can detect suspicious movements from the analyzed data. The detection unit includes AI processing. Specific definitions and criteria for abnormal behavior include, for example, suspicious movements or prolonged stays. Specific definitions and criteria for intruders include, for example, unauthorized persons or animals. This allows the detection unit to accurately detect abnormal behavior and intruders based on the analyzed data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into the AI, which can then detect abnormal behavior or intruders.

[0078] The notification unit can immediately notify the owner of abnormal behavior or intruders detected by the detection unit. The notification unit can, for example, send a notification to the owner's smartphone. The notification unit may include AI processing. The specific definition and criteria of "immediately" include, for example, the delay time before notification. This allows the notification unit to immediately notify the owner of abnormal behavior or intruders. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input information about detected abnormal behavior or intruders into the AI, and the AI ​​can send a notification to the owner.

[0079] The dialogue unit can provide effective countermeasures in a conversational format based on information notified by the notification unit. For example, if the owner asks "What should I do?", the dialogue unit will provide specific advice such as "Contact the police" or "Evacuate to a safe place." The dialogue unit includes AI processing. Specific content and criteria for effective countermeasures include, for example, reporting to the police or sounding an alarm. This allows the dialogue unit to enable the owner to take appropriate action. Some or all of the processing described above in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the notified information into the AI, which can then provide effective countermeasures in a conversational format.

[0080] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of data collection to enhance real-time monitoring. If the user is relaxed, the data collection unit can also decrease the frequency of data collection to reduce the system load. If the user is away from home, the data collection unit can increase the frequency of data collection to detect anomalies earlier. In this way, the data collection unit can adjust the frequency of data collection according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the frequency of data collection.

[0081] The data collection unit can focus on collecting data from specific areas within the home. For example, it can focus on collecting data from entry points such as front doors and windows. It can also focus on collecting data from important areas such as children's rooms and bedrooms. It can also focus on collecting data from external areas such as garages and gardens. This allows the data collection unit to enhance monitoring of important areas by focusing data collection on specific areas. The specific scope and criteria for a particular area could be, for example, the front door or living room. Some or all of the processing described above in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data from a specific area within the home into an AI, which can then enhance monitoring of that area.

[0082] The data collection unit can collect data by combining different types of sensors. For example, it can combine a temperature sensor and a sound sensor to detect abnormal temperature changes or sounds. It can also combine a humidity sensor and a motion sensor to detect abnormal humidity changes or motion. It can also combine a light sensor and a vibration sensor to detect abnormal light changes or vibrations. In this way, the data collection unit can improve the accuracy of anomaly detection by collecting data by combining different types of sensors. Specific types of sensors and implementation methods include, for example, temperature sensors and motion sensors. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data from different types of sensors into an AI, which can then analyze the data to detect anomalies.

[0083] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting data that detects anomalies such as sounds or movements. If the user is relaxed, the data collection unit may also prioritize collecting data that detects changes in the environment. If the user is away from home, the data collection unit may also prioritize collecting data related to intruder detection. This allows the data collection unit to select the types of data to collect according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and select the types of data that the generative AI will collect.

[0084] The data collection unit can collect data while taking into account the movements of pets in the home. For example, the data collection unit can detect pet movements and filter the data to prevent false positives. The data collection unit can also adjust the frequency of data collection, taking into account the pet's activity times. The data collection unit can also determine the pet's location and collect data to detect abnormal movements. In this way, the data collection unit can prevent false positives by collecting data while taking pet movements into account. Specific methods and criteria for considering pet movements include, for example, movement patterns and specific behaviors. 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 pet movement data into AI, which can analyze the data to prevent false positives.

[0085] The data collection unit can collect electricity consumption data within a household and detect abnormal consumption patterns. For example, the data collection unit can collect electricity consumption data within a household in real time and detect abnormal consumption patterns. The data collection unit can also analyze the electricity consumption data and notify the owner of abnormal consumption patterns. Based on the electricity consumption data, the data collection unit can also identify the cause of abnormal consumption patterns. In this way, by collecting electricity consumption data and detecting abnormal consumption patterns, the data collection unit can detect anomalies early. Specific definitions and criteria for abnormal consumption patterns include, for example, comparisons with normal consumption or sudden 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 electricity consumption data into AI, which can then detect abnormal consumption patterns.

[0086] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit may prioritize the analysis of abnormal behavior. If the user is relaxed, the analysis unit may also prioritize the analysis of environmental changes. If the user is out, the analysis unit may also prioritize the analysis of intruders. In this way, the analysis unit can determine the priority of analysis according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then determine the priority of analysis.

[0087] The analysis unit can detect anomalies by comparing them with past data. For example, the analysis unit can detect abnormal movements or sounds by comparing them with past data. The analysis unit can also detect abnormal changes in temperature or humidity by comparing them with past data. The analysis unit can also detect abnormal power consumption patterns by comparing them with past data. This allows the analysis unit to detect anomalies early by detecting them by comparing them with past data. The specific scope and criteria for past data may be, for example, data from the past month or data from the past year. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input past data into AI, and the AI ​​can detect anomalies by comparing them with the past data.

[0088] The analysis unit can detect anomalies by considering the temporal changes in the data. For example, the analysis unit can detect abnormal movements or sounds by considering the temporal changes in the data. The analysis unit can also detect abnormal temperature or humidity changes by considering the temporal changes in the data. The analysis unit can also detect abnormal power consumption patterns by considering the temporal changes in the data. In this way, the analysis unit can improve the accuracy of anomaly detection by considering the temporal changes in the data. Specific methods and criteria for considering temporal changes include, for example, changes by time of day or changes by season. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. For example, the analysis unit can input the temporal changes in the data into AI, and the AI ​​can detect anomalies by considering the temporal changes.

[0089] 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 provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0090] The analysis unit can analyze household energy consumption data and detect abnormal consumption patterns. For example, the analysis unit can analyze household energy consumption data and detect abnormal consumption patterns. Based on the energy consumption data, the analysis unit can also identify the cause of the abnormal consumption pattern. The analysis unit can also analyze the energy consumption data and notify the owner of any abnormal consumption patterns. This allows the analysis unit to detect abnormalities early by analyzing the energy consumption data and detecting abnormal consumption patterns. Specific definitions and criteria for abnormal consumption patterns include, for example, comparisons with normal consumption levels or sudden changes. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input energy consumption data into AI, which can then detect abnormal consumption patterns.

[0091] The analysis unit can analyze audio data within a home and detect abnormal sounds. For example, the analysis unit can analyze audio data within a home and detect abnormal sounds. Based on the audio data, the analysis unit can also identify the cause of the abnormal sound. The analysis unit can also analyze the audio data and notify the owner of any abnormal sounds. This allows the analysis unit to detect abnormalities early by analyzing audio data and detecting abnormal sounds. Specific definitions and criteria for abnormal sounds include, for example, comparison with normal sounds or specific frequencies. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input audio data into an AI, which can then detect abnormal sounds.

[0092] The detection unit can estimate the user's emotions and adjust the detection threshold based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can set a lower detection threshold to detect abnormalities more sensitively. If the user is relaxed, the detection unit can also set a higher detection threshold to reduce false positives. If the user is out, the detection unit can also set a lower detection threshold to detect abnormalities earlier. In this way, the detection unit can adjust the detection threshold according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into the generative AI, which can then adjust the detection threshold.

[0093] The detection unit can detect anomalies by integrating data from different sensors. For example, the detection unit can detect anomalies by integrating data from a temperature sensor and a sound sensor. The detection unit can also detect anomalies by integrating data from a humidity sensor and a motion sensor. The detection unit can also detect anomalies by integrating data from a light sensor and a vibration sensor. In this way, the detection unit can improve the accuracy of anomaly detection by integrating data from different sensors. Specific types of different sensors and implementation methods include, for example, temperature sensors and motion sensors. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input data from different sensors into AI, and the AI ​​can integrate the data to detect anomalies.

[0094] The detection unit can estimate the user's emotions and adjust the notification method of the detection result based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit will immediately send a notification. If the user is relaxed, the detection unit can also send a detailed notification. If the user is out, the detection unit can also send a notification according to the urgency. In this way, the detection unit can adjust the notification method according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into the generative AI, and the generative AI can adjust the notification method.

[0095] The detection unit can detect anomalies by considering the movements of pets in the home. For example, the detection unit can detect pet movements and filter the data to prevent false positives. The detection unit can also detect anomalies by considering the pet's activity times. The detection unit can also grasp the pet's location information and detect abnormal movements. In this way, the detection unit can prevent false positives by detecting anomalies by considering the movements of pets in the home. Specific methods and criteria for considering pet movements include, for example, movement patterns and specific behaviors. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input pet movement data into AI, and the AI ​​can analyze the data to prevent false positives.

[0096] The detection unit can detect anomalies by considering household power consumption data. For example, the detection unit can analyze household power consumption data in real time and detect anomalies. The detection unit can also detect abnormal consumption patterns based on power consumption data. The detection unit can analyze power consumption data and notify the owner of abnormal consumption patterns. In this way, the detection unit can improve the accuracy of anomaly detection by considering household power consumption data. Specific methods and criteria for collecting power consumption data include, for example, methods for measuring consumption and methods for recording data. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input power consumption data into AI, and the AI ​​can detect abnormal consumption patterns.

[0097] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize high-urgency notifications. If the user is relaxed, the notification unit may also prioritize low-urgency notifications. If the user is out, the notification unit may also provide notifications according to their urgency. In this way, the notification unit can adjust the urgency of notifications according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can then adjust the urgency of notifications.

[0098] The notification unit can send notifications by combining different notification methods. For example, it can send notifications by combining email and SMS. It can also send notifications by combining app notifications and voice notifications. It can also send notifications by combining email, SMS, and app notifications. In this way, the notification unit can reliably send notifications by combining different notification methods. Specific types and methods of implementation of different notification methods include, for example, email, SMS, and app notifications. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input different notification methods into AI, and the AI ​​can send notifications.

[0099] The notification unit can customize notification content and provide it to the user. For example, the notification unit can customize notification content according to the user's preferences. The notification unit can also customize notification content by referring to the user's past notification history. The notification unit can also customize notification content according to the user's current situation. In this way, the notification unit can provide the user with the most relevant information by customizing notification content. Specific methods and criteria for customizing notification content include, for example, notification priority and level of detail. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input notification content into AI, and the AI ​​can customize the notification content.

[0100] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will send an immediate notification. If the user is relaxed, the notification unit can also send a notification at an appropriate time. If the user is out, the notification unit can send a notification at a time appropriate to the urgency. In this way, the notification unit can adjust the timing of notifications according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 AI or not. For example, the notification unit can input user emotion data into a generative AI, which can then adjust the timing of notifications.

[0101] The notification unit can coordinate with other devices in the home to provide notifications. For example, the notification unit can use a smart speaker to provide voice notifications. The notification unit can also use smart lights to provide visual notifications. The notification unit can coordinate smart speakers and smart lights to provide notifications. This allows the notification unit to provide effective notifications by coordinating with other devices in the home. Specific types of other devices and methods of implementation include, for example, smart speakers and smart lights. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input other devices into the AI, which can then coordinate the devices to provide notifications.

[0102] The notification unit can select the optimal notification method by considering the user's current location information. For example, if the user is at home, the notification unit can send a notification using a smart speaker. If the user is out, the notification unit can also send a notification to a smartphone. If the user is in a car, the notification unit can also send a notification using the in-car system. This allows the notification unit to select the optimal notification method based on the user's location information. Specific methods and criteria for acquiring the current location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's location information into the AI, which can then select the optimal notification method.

[0103] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated emotions. For example, if the user is feeling anxious, the dialogue unit will use a calm tone. If the user is relaxed, the dialogue unit can use a cheerful tone. If the user is in a hurry, the dialogue unit can use a quick and concise tone. In this way, the dialogue unit can adjust the tone and content of the dialogue according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 dialogue unit may be performed using AI or not. For example, the dialogue unit can input user emotion data into a generative AI, which can then adjust the tone and content of the dialogue.

[0104] The dialogue unit can provide optimal advice by referring to past dialogue history. For example, the dialogue unit can refer to the user's past dialogue history and provide optimal advice. The dialogue unit can also provide advice tailored to the user's preferences based on past dialogue history. The dialogue unit can also analyze past dialogue history and provide advice tailored to the user's situation. In this way, the dialogue unit can provide the best possible advice for the user by referring to past dialogue history. Specific methods for saving and referencing past dialogue history include, for example, the retention period and search method for dialogue logs. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input past dialogue history into AI, and the AI ​​can provide optimal advice.

[0105] The dialogue unit can customize the content of the conversation according to the user's current situation. For example, if the user is at home, the dialogue unit will provide content that is appropriate for the situation at home. If the user is out, the dialogue unit can also provide content that is appropriate for the situation at their destination. If the user is in a car, the dialogue unit can also provide content that is appropriate for the situation inside the car. In this way, the dialogue unit can customize the content of the conversation according to the user's current situation. Specific methods and criteria for understanding the current situation include, for example, the user's current activities and the surrounding environment. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's current situation data into the AI, which can then customize the content of the conversation.

[0106] The dialogue unit can estimate the user's emotions and adjust the frequency of dialogue based on the estimated emotions. For example, if the user is feeling anxious, the dialogue unit will engage in dialogue more frequently. If the user is relaxed, the dialogue unit can engage in dialogue at an appropriate frequency. If the user is in a hurry, the dialogue unit can engage in only the minimum necessary dialogue. In this way, the dialogue unit can adjust the frequency of dialogue according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial expression analysis and voice analysis. Emotion estimation is achieved using an emotion estimation function with 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 dialogue unit may be performed using AI or not. For example, the dialogue unit can input user emotion data into a generative AI, which can then adjust the frequency of dialogue.

[0107] The dialogue unit can interact with other devices in the home in conjunction with it. For example, the dialogue unit can use a smart speaker for voice interaction. The dialogue unit can also use a smart light for visual interaction. The dialogue unit can also interact with a smart speaker and a smart light in conjunction with each other. This allows the dialogue unit to engage in effective interaction by coordinating with other devices in the home. Specific examples of other devices and methods of implementation include smart speakers and smart lights. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input other devices into the AI, which can then coordinate the devices to engage in interaction.

[0108] The dialogue unit can provide optimal advice by taking into account the user's current location. For example, if the user is at home, the dialogue unit can provide advice tailored to the situation at home. If the user is out, the dialogue unit can also provide advice tailored to the situation at their destination. If the user is in a car, the dialogue unit can also provide advice tailored to the situation inside the car. This allows the dialogue unit to provide optimal advice based on the user's location. Specific methods and criteria for acquiring the current location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's location information into the AI, which can then provide optimal advice.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] Home security systems can also be equipped with a voice recognition unit. This unit can analyze sounds within the home in real time and detect unusual noises. For example, if it detects unusual sounds such as breaking glass or screaming, it can immediately notify the homeowner. Furthermore, the voice recognition unit can learn the normal sound environment within the home to improve the accuracy of its abnormal sound detection. In addition, the voice recognition unit can recognize the homeowner's voice commands, allowing the system to be operated by voice. This enables voice-based anomaly detection and operation, providing a higher level of security.

[0111] Home security systems can also be equipped with facial recognition. This facial recognition unit can recognize faces from surveillance camera footage and distinguish between registered family members and friends and suspicious individuals. For example, if an unregistered person appears at the front door, the homeowner can be immediately notified. The facial recognition unit can also detect the return of family members and notify the homeowner. Furthermore, it can analyze family members' facial expressions and estimate their emotions. This allows home security systems to perform anomaly detection and emotion estimation using facial recognition, providing higher security and convenience.

[0112] Home security systems can also be equipped with a temperature monitoring unit. This unit can monitor the temperature inside the home in real time and detect abnormal temperature changes. For example, it can detect an abnormal temperature rise in the early stages of a fire and immediately notify the homeowner. Furthermore, the temperature monitoring unit can learn the normal temperature environment inside the home, improving the accuracy of abnormal temperature detection. In addition, the temperature monitoring unit can analyze the temperature data inside the home and suggest improvements to energy efficiency. This allows home security systems to utilize temperature-based anomaly detection and energy efficiency improvements, providing greater security and comfort.

[0113] Home security systems can also be equipped with a vibration detection unit. This unit can monitor vibrations within the home in real time and detect abnormal vibrations. For example, it can detect abnormal vibrations such as broken windows or forced doors and immediately notify the homeowner. Furthermore, the vibration detection unit can learn the normal vibration environment within the home, improving the accuracy of abnormal vibration detection. In addition, the vibration detection unit can detect the initial vibrations of natural disasters such as earthquakes and issue warnings to the homeowner. This allows home security systems to utilize vibration-based anomaly detection and natural disaster warnings, providing a higher level of security and safety.

[0114] The home security system can also be equipped with an air quality monitoring unit. This unit can monitor the air quality within the home in real time and detect abnormal changes in air quality. For example, it can detect increases in the concentration of carbon monoxide or harmful gases and immediately notify the homeowner. Furthermore, the air quality monitoring unit can learn the normal air quality environment within the home, improving the accuracy of abnormal air quality detection. In addition, the air quality monitoring unit can analyze the air quality data within the home and suggest ventilation and air purification measures. This allows the home security system to utilize air quality for anomaly detection and health management, providing greater security and comfort.

[0115] Home security systems can also be equipped with an emotion estimation unit. This unit can analyze audio and video data from within the home to estimate the emotions of family members. For example, if a family member is feeling stressed, it can offer suggestions for relaxation. The emotion estimation unit can also accumulate family emotion data and analyze long-term emotional changes. Furthermore, the emotion estimation unit can adjust the tone of notifications and conversations of the security system according to the emotions of the family members. This enables emotion-based anomaly detection and family mental health management, providing a higher level of security and comfort.

[0116] Home security systems can also be equipped with a health monitoring unit. This unit can analyze audio and video data from within the home to monitor the health status of family members. For example, it can detect the frequency of coughs and sneezes, enabling early detection of cold or flu symptoms. Furthermore, the health monitoring unit can analyze family movements and postures to detect the risk of falls and injuries. Additionally, it can accumulate family health data to support long-term health management. This allows home security systems to utilize health-based anomaly detection and family health management, providing greater security and comfort.

[0117] Home security systems can also include a lighting control unit. This unit can control the home's lighting in real time and adjust it according to abnormal situations. For example, if an intruder enters, the lights can be turned on to deter them. Furthermore, the lighting control unit can automatically adjust the lighting according to the family's daily routine, providing a comfortable environment. In addition, the lighting control unit can automatically turn off unnecessary lights, taking energy efficiency into consideration. This allows home security systems to utilize lighting for anomaly detection and provide a comfortable environment, offering higher levels of security and comfort.

[0118] Home security systems can also be equipped with a voice interaction unit. This unit recognizes voice commands within the home, allowing for voice control of the system. For example, if the homeowner says, "Turn on the security system," the voice interaction unit recognizes the command and activates the system. The voice interaction unit can also notify the homeowner via voice when it detects an anomaly and provide voice guidance on how to address it. Furthermore, the voice interaction unit can analyze family voice data, estimate emotions, and adjust the tone of the conversation accordingly. This enables voice-controlled operation and notifications, providing enhanced security and convenience.

[0119] The home security system can also be equipped with a smart appliance integration unit. This unit can connect with smart appliances in the home and control them in response to abnormal situations. For example, if a fire is detected, the unit can automatically activate a ventilation fan to expel smoke. Furthermore, the unit can automatically control appliances according to the family's daily routine, providing a comfortable environment. In addition, the unit can automatically turn off unnecessary appliances to improve energy efficiency. This allows the home security system to utilize smart appliances for anomaly detection and provide a comfortable environment, offering higher security and comfort.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects data from surveillance cameras and sensors. For example, surveillance cameras may film the inside of a home, and sensors may detect the opening and closing of doors and windows. The data collection unit can collect this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses LLM or multimodal AI to analyze the collected data. For example, it can detect human movement from camera footage and confirm the opening and closing of doors and windows from sensor data. Step 3: The detection unit detects abnormal behavior or intruders based on the data analyzed by the analysis unit. For example, it detects suspicious movements from the analyzed data. Step 4: The notification unit notifies the owner of any abnormal behavior or intruder detected by the detection unit. For example, it sends a notification to the owner's smartphone. Step 5: The Dialogue Unit provides solutions in a dialogue format based on the information provided by the Notification Unit. For example, if the owner asks, "What should I do?", it will provide specific advice such as, "Contact the police" or "Evacuate to a safe place."

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, notification unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart device 14 to photograph the interior of the home and detect the opening and closing of doors and windows. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to recognize the environment. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormal behavior or intruders based on the analyzed data. The notification unit is implemented in the specific processing unit 46A of the smart device 14 and notifies the owner's smartphone of the detected abnormal behavior or intruder. The dialogue unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific advice to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, notification unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart glasses 214 to photograph the interior of the home and detect the opening and closing of doors and windows. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to recognize the environment. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormal behavior or intruders based on the analyzed data. The notification unit is implemented in the specific processing unit 46A of the smart glasses 214 and notifies the owner's smartphone of the detected abnormal behavior or intruder. The dialogue unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific advice to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, notification unit, and dialogue unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the headset terminal 314 to photograph the interior of the home and detect the opening and closing of doors and windows. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to recognize the environment. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormal behavior or intruders based on the analyzed data. The notification unit is implemented in the specific processing unit 46A of the headset terminal 314 and notifies the owner's smartphone of the detected abnormal behavior or intruder. The dialogue unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific advice to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, notification unit, and dialogue unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the robot 414 to photograph the interior of the home and detect the opening and closing of doors and windows. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to recognize the environment. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which detects abnormal behavior or intruders based on the analyzed data. The notification unit is implemented, for example, by the control unit 46A of the robot 414, which notifies the owner's smartphone of the detected abnormal behavior or intruder. The dialogue unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides specific advice to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A data collection unit that collects data from surveillance cameras and sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit detects abnormal behavior or intruders based on the data analyzed by the aforementioned analysis unit, A notification unit that notifies the owner of abnormal behavior or intruders detected by the aforementioned detection unit, The system includes a dialogue unit that provides solutions in an interactive format based on information notified by the notification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from surveillance cameras and sensors in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data using LLM and multimodal AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Based on the analyzed data, abnormal behavior and intruders are detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Immediately notify the owner. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, Providing effective solutions through a dialogue format The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collect data focusing on specific areas within the home. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Collect data by combining different types of sensors. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and selects the types of data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We collect data while considering the movements of pets within the home. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Collect household electricity consumption data and detect abnormal consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of 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, Detect anomalies by comparing with past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Anomalies are detected by considering the temporal changes in the data. 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, Analyze household energy consumption data to detect abnormal consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It analyzes audio data within the home to detect abnormal sounds. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is It estimates the user's emotions and adjusts the detection threshold based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is Integrating data from different sensors to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is It estimates the user's emotions and adjusts the notification method of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It detects abnormalities by considering the movements of pets in the home. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is The system detects anomalies by considering household power consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and adjusts the urgency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, Notify using a combination of different notification methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, Customize and deliver notification content to users. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It 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 28) The aforementioned notification unit, Connect with other devices in your home to send notifications. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, The optimal notification method is selected considering the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, We provide optimal advice by referring to past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, Customize the conversation based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, Connect and interact with other devices within the home. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, Providing optimal advice while considering the user's current location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from surveillance cameras and sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit detects abnormal behavior or intruders based on the data analyzed by the aforementioned analysis unit, A notification unit that notifies the owner of abnormal behavior or intruders detected by the aforementioned detection unit, The system includes a dialogue unit that provides solutions in an interactive format based on information notified by the notification unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from surveillance cameras and sensors in real time. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the data collected using LLM and multimodal AI. The system according to feature 1.

4. The detection unit is Based on the analyzed data, abnormal behavior and intruders are detected. The system according to feature 1.

5. The aforementioned notification unit, Immediately notify the owner. The system according to feature 1.

6. The aforementioned dialogue unit, Providing effective solutions through a dialogue format The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Collect data focusing on specific areas within the home. The system according to feature 1.

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