system

Abstract

The system according to this embodiment aims to detect surrounding movements in real time and notify of any abnormalities. [Solution] The system according to the embodiment comprises a detection unit, an analysis unit, a notification unit, and a prevention unit. The detection unit detects movement in the surroundings. The analysis unit analyzes the movement detected by the detection unit. The notification unit notifies residents if an abnormality is detected by the analysis unit. The prevention unit prevents data tampering.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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, a system for detecting surrounding movements in real time and notifying abnormalities is not sufficiently developed, and there is room for improvement.

[0005] The system according to the embodiment aims to detect surrounding movements in real time and notify abnormalities.

Means for Solving the Problems

[0006] The system according to the embodiment includes a detection unit, an analysis unit, a notification unit, and a prevention unit. The detection unit detects surrounding movements. The analysis unit analyzes the movements detected by the detection unit. The notification unit notifies residents when an abnormality is detected by the analysis unit. The prevention unit prevents data tampering.

Effects of the Invention

[0007] The system according to this embodiment can detect surrounding movements in real time and notify of abnormalities. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 predictive home security system according to an embodiment of the present invention is a system that utilizes a house's nameplate as a sensor device and is equipped with an AI agent. In the predictive home security system, cameras and sensors embedded in the nameplate detect movement in the surroundings, and the AI ​​analyzes any anomalies in real time and provides notification. Furthermore, blockchain technology is used to prevent data tampering. This system is expected to enhance proactive prevention capabilities and improve the safety of residents. For example, in the predictive home security system, cameras and sensors embedded in the nameplate detect movement in the surroundings. In this case, the camera can monitor a wide area, and the sensor can detect even minute movements. For example, it can detect the movement of people or cars passing in front of the nameplate. Next, the AI ​​in the predictive home security system analyzes the detected movement in real time. The AI ​​analyzes the movement patterns and detects abnormal movements. For example, it can detect people moving differently from normal passersby or cars that remain in the same place for a long time. If an anomaly is detected, the predictive home security system notifies the resident. The notification is sent to a device such as a smartphone or personal computer. For example, if abnormal movement is detected, a warning message will be displayed on the resident's smartphone. This notification allows residents to respond quickly. Furthermore, the predictive home security system uses blockchain technology to prevent data tampering. Blockchain is a technology that ensures data reliability and can prevent data from being altered. For example, by recording detected movement data and AI analysis results on the blockchain, data tampering can be prevented. This system is expected to enhance proactive prevention capabilities and improve the safety of residents. For example, in response to the increase in widespread robberies, the system can prevent crimes by detecting anomalies in advance and notifying residents. In addition, by using blockchain technology, data reliability can be ensured and residents' sense of security can be increased. In this way, the predictive home security system can improve the safety of residents.

[0029] The predictive home security system according to this embodiment comprises a detection unit, an analysis unit, a notification unit, and a prevention unit. The detection unit detects movement in the surroundings. The detection unit can detect movement in the surroundings using, for example, a camera or sensor built into a nameplate. The detection unit can detect movement even at night using, for example, an infrared camera. The detection unit can also detect minute movements using a motion detection sensor. Furthermore, the detection unit can also detect surrounding sounds using a sound sensor. For example, the detection unit can detect movement at night using an infrared camera. The detection unit can detect minute movements using a motion detection sensor. The detection unit can detect surrounding sounds using a sound sensor. The analysis unit analyzes the movement detected by the detection unit in real time. The analysis unit can, for example, use AI to analyze movement patterns and detect abnormal movements. The analysis unit can, for example, detect a person moving differently from a normal passerby. The analysis unit can also detect a car that stays in the same place for a long time. Furthermore, the analysis unit can also analyze the speed and direction of movement to detect anomalies. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. The analysis unit can detect individuals moving differently from normal passersby. The analysis unit can detect cars that remain in the same place for extended periods. The notification unit notifies residents when the analysis unit detects an anomaly. The notification unit can send warning messages to smartphones and computers, for example. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can also send a warning message to the resident's computer when an anomaly is detected. Furthermore, the notification unit can send a warning message to the resident's smartwatch when an anomaly is detected. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can send a warning message to the resident's computer when an anomaly is detected. The notification unit can send a warning message to the resident's smartwatch when an anomaly is detected. The prevention unit prevents data tampering.The prevention unit can prevent data tampering, for example, by using blockchain technology. The prevention unit can prevent data tampering by recording detected motion data and AI analysis results on the blockchain. Furthermore, the prevention unit can also use blockchain technology to ensure data reliability. In addition, the prevention unit can apply multiple security protocols to prevent data tampering. For example, the prevention unit can prevent data tampering using blockchain technology. The prevention unit can prevent data tampering by recording detected motion data and AI analysis results on the blockchain. The prevention unit can use blockchain technology to ensure data reliability. As a result, the predictive home security system according to this embodiment can improve the safety of residents.

[0030] The detection unit detects movement in the surroundings. For example, the detection unit can detect movement using cameras and sensors built into the nameplate. Specifically, the camera built into the nameplate uses a wide-angle lens to capture a wide area of ​​imagery and monitor movement in detail. Infrared cameras can detect movement with high sensitivity even at night, providing clear images even in darkness. Motion detection sensors can detect even minute movements without missing anything, accurately capturing, for example, the movement of leaves swaying in the wind or small animals. Sound sensors can detect sounds in the surroundings and identify unusual or suspicious sounds. For example, they can detect sounds that are different from normal, such as the sound of glass breaking or the sound of doors opening and closing. As a result, the detection unit can detect movement in the surroundings with high accuracy, day or night, and detect anomalies early. Furthermore, the detection unit transmits the data obtained from these sensors to a central database in real time, enabling rapid response in cooperation with other departments.

[0031] The analysis unit analyzes the movements detected by the detection unit in real time. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. Specifically, the AI ​​uses machine learning algorithms to build models that distinguish between normal and abnormal movements. For example, it can compare the movements of normal passersby with the suspicious movements of intruders to detect abnormal movements. The AI ​​can also detect cars that remain in the same place for a long time, enabling early detection of parking violations and the presence of suspicious vehicles. Furthermore, the AI ​​can analyze the speed and direction of movement to detect movements that are different from normal. For example, it can detect people who suddenly start running or people who move at a speed different from normal walking speed. As a result, the analysis unit can analyze movement patterns in real time and quickly detect abnormal movements. In addition, the analysis unit can utilize past data to learn patterns of abnormal movements and predict future anomalies. As a result, the analysis unit can not only perform real-time anomaly detection but also handle long-term risk assessment and prediction.

[0032] The notification unit notifies residents when an anomaly is detected by the analysis unit. The notification unit can, for example, send warning messages to smartphones and computers. Specifically, when the notification unit detects abnormal activity, it displays a warning message in real time on the resident's smartphone, providing detailed information about the nature and location of the anomaly. The notification unit can also send a warning message to the resident's computer when an anomaly is detected, providing detailed information about the anomaly. Furthermore, when an anomaly is detected, the notification unit can send a warning message to the resident's smartwatch, drawing attention through vibration and voice notifications. This allows the notification unit to quickly understand the anomaly and take appropriate action. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify residents of anomalies and ensure their safety.

[0033] The prevention unit prevents data tampering. For example, it can prevent data tampering using blockchain technology. Specifically, the prevention unit records detected motion data and AI analysis results on the blockchain to prevent data tampering. Blockchain is a decentralized database technology, making data tampering extremely difficult and thus ensuring high reliability. Furthermore, the prevention unit can apply multiple security protocols to ensure data reliability. For example, data encryption and digital signatures can be used to guarantee data authenticity. In addition, the prevention unit monitors data integrity in real time to prevent data tampering and can immediately issue warnings if an anomaly is detected. This allows the prevention unit to prevent data tampering and improve the reliability and security of the entire system. Moreover, the prevention unit not only prevents data tampering but also has data backup and recovery functions, allowing for a rapid response in the event of data loss. This ensures the security of data throughout the entire system and provides peace of mind to residents.

[0034] The detection unit can detect surrounding movement using cameras and sensors built into the nameplate. For example, the detection unit can detect surrounding movement using a camera built into the nameplate. The detection unit can also detect surrounding movement using sensors built into the nameplate. For example, the detection unit can detect movement even at night using an infrared camera built into the nameplate. This allows for effective detection of surrounding movement using cameras and sensors built into the nameplate. Cameras and sensors include, but are not limited to, infrared cameras, motion detection sensors, and sound sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from cameras and sensors built into the nameplate into a generating AI and have the generating AI perform the detection of surrounding movement.

[0035] The analysis unit can analyze detected movements in real time and detect abnormal movements. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. For example, the analysis unit can detect a person moving differently from a normal passerby. For example, the analysis unit can also detect a car that remains in the same place for a long time. For example, the analysis unit can analyze the speed and direction of movement to detect anomalies. This allows for rapid detection of abnormal movements by analyzing movements in real time. Real time includes, but is not limited to, seconds or milliseconds. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from the detection unit into a generating AI and have the generating AI perform analysis of movement patterns.

[0036] The notification unit can send warning messages to residents' smartphones or computers if an anomaly is detected. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can also send a warning message to the resident's computer if an anomaly is detected. The notification unit can also send a warning message to the resident's smartwatch if an anomaly is detected. This allows residents to respond quickly by sending a warning message when an anomaly is detected. Warning messages include, but are not limited to, text messages and voice messages. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can generate a warning message using a generation AI when an anomaly is detected and send it to the resident's device.

[0037] The prevention unit can prevent data tampering using blockchain technology. For example, the prevention unit prevents data tampering by recording detected motion data or AI analysis results on the blockchain. The prevention unit can also use blockchain technology to ensure data reliability. For example, the prevention unit can apply multiple security protocols to prevent data tampering. This allows for data tampering prevention and reliability assurance through the use of blockchain technology. Blockchain technology includes, but is not limited to, public blockchains and private blockchains. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can manage blockchain records using generative AI to prevent data tampering.

[0038] The detection unit can simultaneously detect ambient sounds and identify abnormal sound patterns. For example, the detection unit can continuously monitor ambient sounds and detect abnormal sounds (e.g., the sound of breaking glass or shouting). For example, the detection unit can learn sound patterns and identify sounds that differ from normal ambient sounds. For example, if an abnormal sound is detected, the detection unit can record the sound for later review. This allows for the identification of abnormal sound patterns and the detection of anomalies by detecting ambient sounds. Abnormal sound patterns include, but are not limited to, sounds that differ from normal sounds or sounds of a specific frequency. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient sound data into a generating AI and have the generating AI perform the identification of abnormal sound patterns.

[0039] The detection unit can also detect changes in temperature and humidity, and identify abnormal environmental changes. For example, the detection unit can use a temperature sensor to detect rapid temperature changes. For example, the detection unit can use a humidity sensor to detect abnormal humidity changes. For example, if the detection unit determines that a change in temperature or humidity is abnormal, it can record that information for later review. In this way, by detecting changes in temperature and humidity, abnormal environmental changes can be identified and abnormalities can be detected. Abnormal environmental changes include, but are not limited to, changes that differ from normal temperature and humidity, or changes that exceed a certain range. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature and humidity data into a generating AI and have the generating AI perform the identification of abnormal environmental changes.

[0040] The detection unit can detect changes in ambient light and identify abnormal light patterns. The detection unit can detect rapid changes in light, for example, using a light sensor. The detection unit can learn light patterns and identify changes in light that differ from a normal light environment. The detection unit can also record information when an abnormal light change is detected, so that it can be reviewed later. This allows for the identification of abnormal light patterns and the detection of anomalies by detecting changes in ambient light. Abnormal light patterns include, but are not limited to, patterns that differ from normal light or changes in specific brightness. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient light data into a generating AI and have the generating AI perform the identification of abnormal light patterns.

[0041] The detection unit can detect abnormal vibrations using a vibration sensor. For example, the detection unit can use a vibration sensor to detect abnormal vibrations (e.g., strong door shaking or window breakage). The detection unit can learn vibration patterns and identify vibrations that differ from normal vibrations. The detection unit can also record information when abnormal vibrations are detected so that it can be reviewed later. This allows for the detection of abnormal vibrations and the detection of anomalies using a vibration sensor. Abnormal vibrations include, but are not limited to, vibrations that differ from normal vibrations or vibrations of a specific frequency. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input vibration data into a generating AI and have the generating AI identify abnormal vibrations.

[0042] The analysis unit can improve accuracy by learning anomaly patterns by referring to past anomaly data. For example, the analysis unit can analyze past anomaly data and learn anomaly patterns. For example, the analysis unit can improve the accuracy of anomaly detection based on the learned patterns. For example, if new anomaly data occurs, the analysis unit can add that data to the learning to further improve accuracy. In this way, the accuracy of anomaly detection is improved by learning from past anomaly data. Anomaly patterns include, but are not limited to, examples such as analysis of past data and the use of machine learning algorithms. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past anomaly data into a generating AI and have the generating AI perform learning of anomaly patterns.

[0043] The analysis unit can analyze the frequency of anomalies and evaluate their severity. For example, the analysis unit can record the frequency of anomalies and prioritize evaluating anomalies with high frequency. For example, the analysis unit can evaluate the severity of anomalies based on their frequency. For example, if an anomaly occurs frequently, the analysis unit can prioritize considering countermeasures for that anomaly. In this way, by analyzing the frequency of anomalies, the severity of the anomalies can be evaluated and appropriate responses can be taken. The severity of an anomaly includes, but is not limited to, the scope of impact and frequency of occurrence. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly frequency data into a generating AI and have the generating AI perform an evaluation of the severity of the anomalies.

[0044] The analysis unit can identify the location of an anomaly and map the area where the anomaly occurred. For example, the analysis unit can identify the location of the anomaly and map that location on a map. For example, the analysis unit can identify the area where the anomaly occurred and focus its monitoring on that area. For example, if there are multiple locations where the anomaly occurred, the analysis unit can map each location and analyze the trend of the anomaly occurrence. This allows for focused monitoring of the area where the anomaly occurred by identifying the location of the anomaly. The location of the anomaly includes, but is not limited to, GPS data and sensor location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly location data into a generating AI and have the generating AI perform the mapping of the area where the anomaly occurred.

[0045] The analysis unit can analyze the time periods in which anomalies occur and predict the trends in anomaly occurrence. For example, the analysis unit can record the time periods in which anomalies occur and analyze whether anomalies are more likely to occur during specific time periods. For example, the analysis unit can predict the trends in anomaly occurrence based on the time periods in which anomalies occur. For example, if the time periods in which anomalies occur have a specific pattern, the analysis unit can also take preventive measures based on that pattern. In this way, by analyzing the time periods in which anomalies occur, it is possible to predict the trends in anomaly occurrence and take preventive measures. The time periods in which anomalies occur include, but are not limited to, the frequency of occurrence for each time period and anomalies in specific time periods. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly occurrence time period data into a generating AI and have the generating AI perform a prediction of anomaly occurrence trends.

[0046] The notification unit can select different notification methods depending on the type of anomaly. For example, in the case of a minor anomaly, the notification unit may notify via email or app notification. For example, in the case of a serious anomaly, the notification unit may notify via phone or SMS. The notification unit can also automatically select a notification method depending on the type of anomaly and notify in the most appropriate way. This allows for more appropriate notifications by selecting a notification method according to the type of anomaly. Different notification methods include, but are not limited to, email notifications, app notifications, and voice notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly type data into a generating AI and have the generating AI select the notification method.

[0047] The notification unit can notify detailed information, including the location of the anomaly. For example, the notification unit can identify the location of the anomaly and notify detailed information about that location. For example, the notification unit can attach photos or videos of the location of the anomaly to the notification. For example, the notification unit can attach a map of the location of the anomaly to the notification to encourage a quick response. This enables a quick response by notifying detailed information, including the location of the anomaly. Detailed information includes, but is not limited to, the location, time, and type of anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly location data into a generating AI and have the generating AI execute the notification of detailed information.

[0048] The notification unit can automatically contact the police or security companies when an anomaly occurs. For example, the notification unit will automatically contact the police if a serious anomaly occurs. The notification unit can also contact the appropriate security company depending on the type of anomaly. The notification unit can also automatically notify pre-configured contacts when an anomaly occurs. This enables a rapid response by automatically contacting the police or security companies when an anomaly occurs. The police and security companies include, but are not limited to, local police stations and contracted security companies. Some or all of the above processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can automate contacting the police or security companies using generated AI when an anomaly occurs.

[0049] The notification unit can also send notifications to nearby residents when an anomaly occurs. For example, the notification unit can notify nearby residents via email or app notification when an anomaly occurs. For example, the notification unit can notify nearby residents via phone or SMS when a serious anomaly occurs. The notification unit can also quickly notify nearby residents and request their cooperation when an anomaly occurs. This enables a rapid response by sending notifications to nearby residents when an anomaly occurs. Nearby residents include, but are not limited to, neighbors in adjacent houses or residents of the same apartment building. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can automate the notification to nearby residents using generation AI when an anomaly occurs.

[0050] The prevention unit can automatically create backups when it detects data tampering. For example, if data tampering is detected, the prevention unit can automatically create the latest backup to facilitate data restoration. For example, the prevention unit can isolate the tampered data and compare it with backup data to identify the extent of the tampering. The prevention unit can also, for example, create backups periodically so that it can be quickly restored if tampering is detected. This makes data restoration easier by automatically creating backups when data tampering is detected. Backups include, but are not limited to, cloud storage and external hard disks. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can automate the creation of backups using generative AI when data tampering is detected.

[0051] The prevention unit can apply multiple layers of security protocols to prevent data tampering. For example, the prevention unit can apply a combination of multiple security protocols to prevent data tampering. For example, the prevention unit can broaden the scope of application of security protocols to minimize the risk of tampering. The prevention unit can also, for example, periodically update security protocols to respond to the latest threats. This minimizes the risk of data tampering by applying multiple layers of security protocols. Multiple layers of security protocols include, but are not limited to, firewalls, encryption, and authentication protocols. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can use generative AI to manage the application of security protocols to prevent data tampering.

[0052] The prevention unit can combine different blockchain technologies to prevent data tampering. For example, the prevention unit can combine different blockchain technologies to prevent data tampering. For example, the prevention unit can leverage the characteristics of blockchain technologies to minimize the risk of tampering. The prevention unit can also, for example, regularly update blockchain technologies to respond to the latest threats. This minimizes the risk of data tampering by combining different blockchain technologies. Different blockchain technologies include, but are not limited to, a combination of public and private blockchains. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can use generative AI to manage combinations of different blockchain technologies to prevent data tampering.

[0053] The prevention unit can check data integrity in real time to prevent data tampering. For example, the prevention unit checks data integrity in real time to prevent data tampering. For example, if data integrity is confirmed, the prevention unit can minimize the risk of tampering. The prevention unit can also periodically check data integrity to detect the risk of tampering early. This allows for early detection of the risk of data tampering by checking data integrity in real time. Data integrity includes, but is not limited to, hash value comparison and data consistency checks. Real time includes, but is not limited to, seconds and milliseconds. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can have a generating AI perform the data integrity check.

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

[0055] The detection unit can not only detect ambient movement but also changes in ambient temperature and humidity. For example, it can use a temperature sensor to detect rapid temperature changes, allowing it to detect abnormal temperature increases in the initial stages of a fire. It can also use a humidity sensor to detect abnormal humidity changes, enabling early detection of abnormalities such as water leaks and condensation. Furthermore, the detection unit can analyze temperature and humidity data in real time and notify residents if an abnormality is detected, allowing them to respond quickly.

[0056] The analysis unit can analyze not only detected movement patterns but also abnormal sound patterns. For example, the analysis unit can constantly monitor ambient sounds and detect abnormal sounds (e.g., the sound of breaking glass or screaming). This allows for the detection of abnormal sounds even when residents are absent, enabling a rapid response. Furthermore, the analysis unit can learn abnormal sound patterns and distinguish sounds that differ from normal ambient sounds. In addition, if an abnormal sound is detected, it can record the sound for later review. This improves the safety of residents by detecting abnormal sounds.

[0057] The notification unit not only notifies residents when an anomaly is detected, but can also select different notification methods depending on the type of anomaly. For example, in the case of a minor anomaly, notifications can be sent via email or app. This allows residents to check for anomalies without disrupting their daily lives. In the case of a serious anomaly, notifications can be sent via phone or SMS. This allows residents to respond to the anomaly quickly. Furthermore, the notification unit can automatically select the notification method according to the type of anomaly and notify in the most appropriate way. This enables appropriate notifications depending on the type of anomaly.

[0058] The prevention unit can not only use blockchain technology to prevent data tampering, but can also combine different blockchain technologies. For example, it can combine public and private blockchains to prevent data tampering. This further enhances data reliability. In addition, the prevention unit can leverage the characteristics of blockchain technology to minimize the risk of tampering. Furthermore, the prevention unit can regularly update blockchain technology to respond to the latest threats. This further minimizes the risk of data tampering.

[0059] The prevention unit can apply multiple layers of security protocols to prevent data tampering. For example, by combining firewalls, encryption, and authentication protocols, the risk of data tampering can be minimized. Furthermore, the prevention unit can expand the scope of security protocol application to minimize the risk of tampering. In addition, the prevention unit can regularly update security protocols to respond to the latest threats. This allows for the application of multiple layers of security protocols, thereby minimizing the risk of data tampering.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The detection unit detects movement in the surroundings. For example, using cameras and sensors built into the nameplate, infrared cameras, motion detection sensors, and sound sensors, it can detect even minute movements and sounds day or night. Step 2: The analysis unit analyzes the movements detected by the detection unit in real time. For example, it can use AI to analyze movement patterns and detect people moving differently from normal pedestrians, cars staying in the same place for a long time, or abnormalities in the speed or direction of movement. Step 3: The notification unit notifies residents if an anomaly is detected by the analysis unit. For example, it can send warning messages to smartphones, computers, and smartwatches. Step 4: The prevention unit prevents data tampering. For example, it uses blockchain technology to record detected movement data and AI analysis results to ensure data reliability. It can also apply multiple security protocols.

[0062] (Example of form 2) The predictive home security system according to an embodiment of the present invention is a system that utilizes a house's nameplate as a sensor device and is equipped with an AI agent. In the predictive home security system, cameras and sensors embedded in the nameplate detect movement in the surroundings, and the AI ​​analyzes any anomalies in real time and provides notification. Furthermore, blockchain technology is used to prevent data tampering. This system is expected to enhance proactive prevention capabilities and improve the safety of residents. For example, in the predictive home security system, cameras and sensors embedded in the nameplate detect movement in the surroundings. In this case, the camera can monitor a wide area, and the sensor can detect even minute movements. For example, it can detect the movement of people or cars passing in front of the nameplate. Next, the AI ​​in the predictive home security system analyzes the detected movement in real time. The AI ​​analyzes the movement patterns and detects abnormal movements. For example, it can detect people moving differently from normal passersby or cars that remain in the same place for a long time. If an anomaly is detected, the predictive home security system notifies the resident. The notification is sent to a device such as a smartphone or personal computer. For example, if abnormal movement is detected, a warning message will be displayed on the resident's smartphone. This notification allows residents to respond quickly. Furthermore, the predictive home security system uses blockchain technology to prevent data tampering. Blockchain is a technology that ensures data reliability and can prevent data from being altered. For example, by recording detected movement data and AI analysis results on the blockchain, data tampering can be prevented. This system is expected to enhance proactive prevention capabilities and improve the safety of residents. For example, in response to the increase in widespread robberies, the system can prevent crimes by detecting anomalies in advance and notifying residents. In addition, by using blockchain technology, data reliability can be ensured and residents' sense of security can be increased. In this way, the predictive home security system can improve the safety of residents.

[0063] The predictive home security system according to this embodiment comprises a detection unit, an analysis unit, a notification unit, and a prevention unit. The detection unit detects movement in the surroundings. The detection unit can detect movement in the surroundings using, for example, a camera or sensor built into a nameplate. The detection unit can detect movement even at night using, for example, an infrared camera. The detection unit can also detect minute movements using a motion detection sensor. Furthermore, the detection unit can also detect surrounding sounds using a sound sensor. For example, the detection unit can detect movement at night using an infrared camera. The detection unit can detect minute movements using a motion detection sensor. The detection unit can detect surrounding sounds using a sound sensor. The analysis unit analyzes the movement detected by the detection unit in real time. The analysis unit can, for example, use AI to analyze movement patterns and detect abnormal movements. The analysis unit can, for example, detect a person moving differently from a normal passerby. The analysis unit can also detect a car that stays in the same place for a long time. Furthermore, the analysis unit can also analyze the speed and direction of movement to detect anomalies. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. The analysis unit can detect individuals moving differently from normal passersby. The analysis unit can detect cars that remain in the same place for extended periods. The notification unit notifies residents when the analysis unit detects an anomaly. The notification unit can send warning messages to smartphones and computers, for example. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can also send a warning message to the resident's computer when an anomaly is detected. Furthermore, the notification unit can send a warning message to the resident's smartwatch when an anomaly is detected. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can send a warning message to the resident's computer when an anomaly is detected. The notification unit can send a warning message to the resident's smartwatch when an anomaly is detected. The prevention unit prevents data tampering.The prevention unit can prevent data tampering, for example, by using blockchain technology. The prevention unit can prevent data tampering by recording detected motion data and AI analysis results on the blockchain. Furthermore, the prevention unit can also use blockchain technology to ensure data reliability. In addition, the prevention unit can apply multiple security protocols to prevent data tampering. For example, the prevention unit can prevent data tampering using blockchain technology. The prevention unit can prevent data tampering by recording detected motion data and AI analysis results on the blockchain. The prevention unit can use blockchain technology to ensure data reliability. As a result, the predictive home security system according to this embodiment can improve the safety of residents.

[0064] The detection unit detects movement in the surroundings. For example, the detection unit can detect movement using cameras and sensors built into the nameplate. Specifically, the camera built into the nameplate uses a wide-angle lens to capture a wide area of ​​imagery and monitor movement in detail. Infrared cameras can detect movement with high sensitivity even at night, providing clear images even in darkness. Motion detection sensors can detect even minute movements without missing anything, accurately capturing, for example, the movement of leaves swaying in the wind or small animals. Sound sensors can detect sounds in the surroundings and identify unusual or suspicious sounds. For example, they can detect sounds that are different from normal, such as the sound of glass breaking or the sound of doors opening and closing. As a result, the detection unit can detect movement in the surroundings with high accuracy, day or night, and detect anomalies early. Furthermore, the detection unit transmits the data obtained from these sensors to a central database in real time, enabling rapid response in cooperation with other departments.

[0065] The analysis unit analyzes the movements detected by the detection unit in real time. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. Specifically, the AI ​​uses machine learning algorithms to build models that distinguish between normal and abnormal movements. For example, it can compare the movements of normal passersby with the suspicious movements of intruders to detect abnormal movements. The AI ​​can also detect cars that remain in the same place for a long time, enabling early detection of parking violations and the presence of suspicious vehicles. Furthermore, the AI ​​can analyze the speed and direction of movement to detect movements that are different from normal. For example, it can detect people who suddenly start running or people who move at a speed different from normal walking speed. As a result, the analysis unit can analyze movement patterns in real time and quickly detect abnormal movements. In addition, the analysis unit can utilize past data to learn patterns of abnormal movements and predict future anomalies. As a result, the analysis unit can not only perform real-time anomaly detection but also handle long-term risk assessment and prediction.

[0066] The notification unit notifies residents when an anomaly is detected by the analysis unit. The notification unit can, for example, send warning messages to smartphones and computers. Specifically, when the notification unit detects abnormal activity, it displays a warning message in real time on the resident's smartphone, providing detailed information about the nature and location of the anomaly. The notification unit can also send a warning message to the resident's computer when an anomaly is detected, providing detailed information about the anomaly. Furthermore, when an anomaly is detected, the notification unit can send a warning message to the resident's smartwatch, drawing attention through vibration and voice notifications. This allows the notification unit to quickly understand the anomaly and take appropriate action. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify residents of anomalies and ensure their safety.

[0067] The prevention unit prevents data tampering. For example, it can prevent data tampering using blockchain technology. Specifically, the prevention unit records detected motion data and AI analysis results on the blockchain to prevent data tampering. Blockchain is a decentralized database technology, making data tampering extremely difficult and thus ensuring high reliability. Furthermore, the prevention unit can apply multiple security protocols to ensure data reliability. For example, data encryption and digital signatures can be used to guarantee data authenticity. In addition, the prevention unit monitors data integrity in real time to prevent data tampering and can immediately issue warnings if an anomaly is detected. This allows the prevention unit to prevent data tampering and improve the reliability and security of the entire system. Moreover, the prevention unit not only prevents data tampering but also has data backup and recovery functions, allowing for a rapid response in the event of data loss. This ensures the security of data throughout the entire system and provides peace of mind to residents.

[0068] The detection unit can detect surrounding movement using cameras and sensors built into the nameplate. For example, the detection unit can detect surrounding movement using a camera built into the nameplate. The detection unit can also detect surrounding movement using sensors built into the nameplate. For example, the detection unit can detect movement even at night using an infrared camera built into the nameplate. This allows for effective detection of surrounding movement using cameras and sensors built into the nameplate. Cameras and sensors include, but are not limited to, infrared cameras, motion detection sensors, and sound sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from cameras and sensors built into the nameplate into a generating AI and have the generating AI perform the detection of surrounding movement.

[0069] The analysis unit can analyze detected movements in real time and detect abnormal movements. For example, the analysis unit can use AI to analyze movement patterns and detect abnormal movements. For example, the analysis unit can detect a person moving differently from a normal passerby. For example, the analysis unit can also detect a car that remains in the same place for a long time. For example, the analysis unit can analyze the speed and direction of movement to detect anomalies. This allows for rapid detection of abnormal movements by analyzing movements in real time. Real time includes, but is not limited to, seconds or milliseconds. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from the detection unit into a generating AI and have the generating AI perform analysis of movement patterns.

[0070] The notification unit can send warning messages to residents' smartphones or computers if an anomaly is detected. For example, if the notification unit detects abnormal movement, it can display a warning message on the resident's smartphone. The notification unit can also send a warning message to the resident's computer if an anomaly is detected. The notification unit can also send a warning message to the resident's smartwatch if an anomaly is detected. This allows residents to respond quickly by sending a warning message when an anomaly is detected. Warning messages include, but are not limited to, text messages and voice messages. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can generate a warning message using a generation AI when an anomaly is detected and send it to the resident's device.

[0071] The prevention unit can prevent data tampering using blockchain technology. For example, the prevention unit prevents data tampering by recording detected motion data or AI analysis results on the blockchain. The prevention unit can also use blockchain technology to ensure data reliability. For example, the prevention unit can apply multiple security protocols to prevent data tampering. This allows for data tampering prevention and reliability assurance through the use of blockchain technology. Blockchain technology includes, but is not limited to, public blockchains and private blockchains. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can manage blockchain records using generative AI to prevent data tampering.

[0072] The detection unit can estimate the user's emotions and adjust the detection sensitivity based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can increase the detection sensitivity to detect even subtle movements. For example, if the user is relaxed, the detection unit can decrease the detection sensitivity to ignore normal movements. For example, if the user is in a hurry, the detection unit can adjust the sensitivity to detect only important movements. This allows for more appropriate detection by adjusting the detection sensitivity according to the user's emotions. The user's emotions are estimated using methods such as facial recognition and voice analysis. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and detection sensitivity adjustment.

[0073] The detection unit can simultaneously detect ambient sounds and identify abnormal sound patterns. For example, the detection unit can continuously monitor ambient sounds and detect abnormal sounds (e.g., the sound of breaking glass or shouting). For example, the detection unit can learn sound patterns and identify sounds that differ from normal ambient sounds. For example, if an abnormal sound is detected, the detection unit can record the sound for later review. This allows for the identification of abnormal sound patterns and the detection of anomalies by detecting ambient sounds. Abnormal sound patterns include, but are not limited to, sounds that differ from normal sounds or sounds of a specific frequency. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient sound data into a generating AI and have the generating AI perform the identification of abnormal sound patterns.

[0074] The detection unit can also detect changes in temperature and humidity, and identify abnormal environmental changes. For example, the detection unit can use a temperature sensor to detect rapid temperature changes. For example, the detection unit can use a humidity sensor to detect abnormal humidity changes. For example, if the detection unit determines that a change in temperature or humidity is abnormal, it can record that information for later review. In this way, by detecting changes in temperature and humidity, abnormal environmental changes can be identified and abnormalities can be detected. Abnormal environmental changes include, but are not limited to, changes that differ from normal temperature and humidity, or changes that exceed a certain range. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature and humidity data into a generating AI and have the generating AI perform the identification of abnormal environmental changes.

[0075] The detection unit can estimate the user's emotions and dynamically change the detection area based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can expand the detection area to monitor the entire surroundings. For example, if the user is relaxed, the detection unit can narrow the detection area to monitor only a specific area. For example, if the user is in a hurry, the detection unit can prioritize monitoring only important areas. This allows for more appropriate monitoring by dynamically changing the detection area according to the user's emotions. The detection area includes, but is not limited to, an entire room or a specific zone. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform the dynamic change of the detection area.

[0076] The detection unit can detect changes in ambient light and identify abnormal light patterns. The detection unit can detect rapid changes in light, for example, using a light sensor. The detection unit can learn light patterns and identify changes in light that differ from a normal light environment. The detection unit can also record information when an abnormal light change is detected, so that it can be reviewed later. This allows for the identification of abnormal light patterns and the detection of anomalies by detecting changes in ambient light. Abnormal light patterns include, but are not limited to, patterns that differ from normal light or changes in specific brightness. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient light data into a generating AI and have the generating AI perform the identification of abnormal light patterns.

[0077] The detection unit can detect abnormal vibrations using a vibration sensor. For example, the detection unit can use a vibration sensor to detect abnormal vibrations (e.g., strong door shaking or window breakage). The detection unit can learn vibration patterns and identify vibrations that differ from normal vibrations. The detection unit can also record information when abnormal vibrations are detected so that it can be reviewed later. This allows for the detection of abnormal vibrations and the detection of anomalies using a vibration sensor. Abnormal vibrations include, but are not limited to, vibrations that differ from normal vibrations or vibrations of a specific frequency. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input vibration data into a generating AI and have the generating AI identify abnormal vibrations.

[0078] The analysis unit can estimate the user's emotions and adjust the abnormality detection criteria based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can tighten the abnormality detection criteria and detect even minor abnormalities. For example, if the user is relaxed, the analysis unit can loosen the abnormality detection criteria and not judge normal movements as abnormal. For example, if the user is in a hurry, the analysis unit can also prioritize detecting only important abnormalities. This allows for more appropriate abnormality detection by adjusting the abnormality detection criteria according to the user's emotions. Abnormality detection criteria include, but are not limited to, threshold setting and pattern matching. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the abnormality detection criteria.

[0079] The analysis unit can improve accuracy by learning anomaly patterns by referring to past anomaly data. For example, the analysis unit can analyze past anomaly data and learn anomaly patterns. For example, the analysis unit can improve the accuracy of anomaly detection based on the learned patterns. For example, if new anomaly data occurs, the analysis unit can add that data to the learning to further improve accuracy. In this way, the accuracy of anomaly detection is improved by learning from past anomaly data. Anomaly patterns include, but are not limited to, examples such as analysis of past data and the use of machine learning algorithms. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past anomaly data into a generating AI and have the generating AI perform learning of anomaly patterns.

[0080] The analysis unit can analyze the frequency of anomalies and evaluate their severity. For example, the analysis unit can record the frequency of anomalies and prioritize evaluating anomalies with high frequency. For example, the analysis unit can evaluate the severity of anomalies based on their frequency. For example, if an anomaly occurs frequently, the analysis unit can prioritize considering countermeasures for that anomaly. In this way, by analyzing the frequency of anomalies, the severity of the anomalies can be evaluated and appropriate responses can be taken. The severity of an anomaly includes, but is not limited to, the scope of impact and frequency of occurrence. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly frequency data into a generating AI and have the generating AI perform an evaluation of the severity of the anomalies.

[0081] The analysis unit can estimate the user's emotions and determine the priority of anomalies based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will set the anomaly priority high and respond quickly. For example, if the user is relaxed, the analysis unit will set the anomaly priority low and respond normally. For example, if the user is in a hurry, the analysis unit can also prioritize and respond only to important anomalies. This enables a quick response by determining the priority of anomalies according to the user's emotions. Anomaly priorities include, but are not limited to, factors such as impact and urgency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of anomalies.

[0082] The analysis unit can identify the location of an anomaly and map the area where the anomaly occurred. For example, the analysis unit can identify the location of the anomaly and map that location on a map. For example, the analysis unit can identify the area where the anomaly occurred and focus its monitoring on that area. For example, if there are multiple locations where the anomaly occurred, the analysis unit can map each location and analyze the trend of the anomaly occurrence. This allows for focused monitoring of the area where the anomaly occurred by identifying the location of the anomaly. The location of the anomaly includes, but is not limited to, GPS data and sensor location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly location data into a generating AI and have the generating AI perform the mapping of the area where the anomaly occurred.

[0083] The analysis unit can analyze the time periods in which anomalies occur and predict the trends in anomaly occurrence. For example, the analysis unit can record the time periods in which anomalies occur and analyze whether anomalies are more likely to occur during specific time periods. For example, the analysis unit can predict the trends in anomaly occurrence based on the time periods in which anomalies occur. For example, if the time periods in which anomalies occur have a specific pattern, the analysis unit can also take preventive measures based on that pattern. In this way, by analyzing the time periods in which anomalies occur, it is possible to predict the trends in anomaly occurrence and take preventive measures. The time periods in which anomalies occur include, but are not limited to, the frequency of occurrence for each time period and anomalies in specific time periods. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input anomaly occurrence time period data into a generating AI and have the generating AI perform a prediction of anomaly occurrence trends.

[0084] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. For example, if the user is feeling anxious, the notification unit can increase the urgency of the notification and send a quick notification. For example, if the user is relaxed, the notification unit can lower the urgency of the notification and send a normal notification. For example, if the user is in a hurry, the notification unit can prioritize sending only important notifications. This allows for more appropriate notifications by adjusting the urgency of notifications according to the user's emotions. The urgency of a notification includes, but is not limited to, the severity of the anomaly and the user's situation. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the urgency of notifications.

[0085] The notification unit can select different notification methods depending on the type of anomaly. For example, in the case of a minor anomaly, the notification unit may notify via email or app notification. For example, in the case of a serious anomaly, the notification unit may notify via phone or SMS. The notification unit can also automatically select a notification method depending on the type of anomaly and notify in the most appropriate way. This allows for more appropriate notifications by selecting a notification method according to the type of anomaly. Different notification methods include, but are not limited to, email notifications, app notifications, and voice notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly type data into a generating AI and have the generating AI select the notification method.

[0086] The notification unit can notify detailed information, including the location of the anomaly. For example, the notification unit can identify the location of the anomaly and notify detailed information about that location. For example, the notification unit can attach photos or videos of the location of the anomaly to the notification. For example, the notification unit can attach a map of the location of the anomaly to the notification to encourage a quick response. This enables a quick response by notifying detailed information, including the location of the anomaly. Detailed information includes, but is not limited to, the location, time, and type of anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly location data into a generating AI and have the generating AI execute the notification of detailed information.

[0087] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, if the user is feeling anxious, the notification unit can provide a notification with detailed information. If the user is relaxed, the notification unit can provide a concise notification. If the user is in a hurry, the notification unit can provide a notification containing only essential information. By customizing the content of notifications according to the user's emotions, more appropriate notifications can be provided. The content of notifications may include, but is not limited to, the user's preferences and situational content. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI customize the notification content.

[0088] The notification unit can automatically contact the police or security companies when an anomaly occurs. For example, the notification unit will automatically contact the police if a serious anomaly occurs. The notification unit can also contact the appropriate security company depending on the type of anomaly. The notification unit can also automatically notify pre-configured contacts when an anomaly occurs. This enables a rapid response by automatically contacting the police or security companies when an anomaly occurs. The police and security companies include, but are not limited to, local police stations and contracted security companies. Some or all of the above processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can automate contacting the police or security companies using generated AI when an anomaly occurs.

[0089] The notification unit can also send notifications to nearby residents when an anomaly occurs. For example, the notification unit can notify nearby residents via email or app notification when an anomaly occurs. For example, the notification unit can notify nearby residents via phone or SMS when a serious anomaly occurs. The notification unit can also quickly notify nearby residents and request their cooperation when an anomaly occurs. This enables a rapid response by sending notifications to nearby residents when an anomaly occurs. Nearby residents include, but are not limited to, neighbors in adjacent houses or residents of the same apartment building. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can automate the notification to nearby residents using generation AI when an anomaly occurs.

[0090] The prevention unit can estimate the user's emotions and adjust the data protection level based on the estimated emotions. For example, if the user is feeling anxious, the prevention unit can increase the data protection level to enhance security. For example, if the user is relaxed, the prevention unit can set the data protection level to normal. For example, if the user is in a hurry, the prevention unit can prioritize protecting only important data. This allows for more appropriate data protection by adjusting the data protection level according to the user's emotions. Data protection levels include, but are not limited to, encryption levels and access restrictions. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can input user emotion data into a generating AI and have the generating AI adjust the data protection level.

[0091] The prevention unit can automatically create backups when it detects data tampering. For example, if data tampering is detected, the prevention unit can automatically create the latest backup to facilitate data restoration. For example, the prevention unit can isolate the tampered data and compare it with backup data to identify the extent of the tampering. The prevention unit can also, for example, create backups periodically so that it can be quickly restored if tampering is detected. This makes data restoration easier by automatically creating backups when data tampering is detected. Backups include, but are not limited to, cloud storage and external hard disks. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can automate the creation of backups using generative AI when data tampering is detected.

[0092] The prevention unit can apply multiple layers of security protocols to prevent data tampering. For example, the prevention unit can apply a combination of multiple security protocols to prevent data tampering. For example, the prevention unit can broaden the scope of application of security protocols to minimize the risk of tampering. The prevention unit can also, for example, periodically update security protocols to respond to the latest threats. This minimizes the risk of data tampering by applying multiple layers of security protocols. Multiple layers of security protocols include, but are not limited to, firewalls, encryption, and authentication protocols. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can use generative AI to manage the application of security protocols to prevent data tampering.

[0093] The prevention unit can estimate the user's emotions and customize data protection methods based on the estimated emotions. For example, if the user is feeling anxious, the prevention unit can strengthen data protection methods to enhance security. For example, if the user is relaxed, the prevention unit can apply standard data protection methods. For example, if the user is in a hurry, the prevention unit can also apply a method that prioritizes the protection of only important data. This allows for more appropriate data protection by customizing data protection methods according to the user's emotions. Data protection methods include, but are not limited to, protection methods that respond to user requests and protection methods that depend on the situation. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input user emotion data into a generating AI and have the generating AI perform the customization of data protection methods.

[0094] The prevention unit can combine different blockchain technologies to prevent data tampering. For example, the prevention unit can combine different blockchain technologies to prevent data tampering. For example, the prevention unit can leverage the characteristics of blockchain technologies to minimize the risk of tampering. The prevention unit can also, for example, regularly update blockchain technologies to respond to the latest threats. This minimizes the risk of data tampering by combining different blockchain technologies. Different blockchain technologies include, but are not limited to, a combination of public and private blockchains. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can use generative AI to manage combinations of different blockchain technologies to prevent data tampering.

[0095] The prevention unit can check data integrity in real time to prevent data tampering. For example, the prevention unit checks data integrity in real time to prevent data tampering. For example, if data integrity is confirmed, the prevention unit can minimize the risk of tampering. The prevention unit can also periodically check data integrity to detect the risk of tampering early. This allows for early detection of the risk of data tampering by checking data integrity in real time. Data integrity includes, but is not limited to, hash value comparison and data consistency checks. Real time includes, but is not limited to, seconds and milliseconds. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can have a generating AI perform the data integrity check.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The detection unit can not only detect ambient movement but also changes in ambient temperature and humidity. For example, it can use a temperature sensor to detect rapid temperature changes, allowing it to detect abnormal temperature increases in the initial stages of a fire. It can also use a humidity sensor to detect abnormal humidity changes, enabling early detection of abnormalities such as water leaks and condensation. Furthermore, the detection unit can analyze temperature and humidity data in real time and notify residents if an abnormality is detected, allowing them to respond quickly.

[0098] The analysis unit can analyze not only detected movement patterns but also abnormal sound patterns. For example, the analysis unit can constantly monitor ambient sounds and detect abnormal sounds (e.g., the sound of breaking glass or screaming). This allows for the detection of abnormal sounds even when residents are absent, enabling a rapid response. Furthermore, the analysis unit can learn abnormal sound patterns and distinguish sounds that differ from normal ambient sounds. In addition, if an abnormal sound is detected, it can record the sound for later review. This improves the safety of residents by detecting abnormal sounds.

[0099] The notification unit not only notifies residents when an anomaly is detected, but can also select different notification methods depending on the type of anomaly. For example, in the case of a minor anomaly, notifications can be sent via email or app. This allows residents to check for anomalies without disrupting their daily lives. In the case of a serious anomaly, notifications can be sent via phone or SMS. This allows residents to respond to the anomaly quickly. Furthermore, the notification unit can automatically select the notification method according to the type of anomaly and notify in the most appropriate way. This enables appropriate notifications depending on the type of anomaly.

[0100] The prevention unit can not only use blockchain technology to prevent data tampering, but can also combine different blockchain technologies. For example, it can combine public and private blockchains to prevent data tampering. This further enhances data reliability. In addition, the prevention unit can leverage the characteristics of blockchain technology to minimize the risk of tampering. Furthermore, the prevention unit can regularly update blockchain technology to respond to the latest threats. This further minimizes the risk of data tampering.

[0101] The prevention unit can apply multiple layers of security protocols to prevent data tampering. For example, by combining firewalls, encryption, and authentication protocols, the risk of data tampering can be minimized. Furthermore, the prevention unit can expand the scope of security protocol application to minimize the risk of tampering. In addition, the prevention unit can regularly update security protocols to respond to the latest threats. This allows for the application of multiple layers of security protocols, thereby minimizing the risk of data tampering.

[0102] The detection unit can estimate the user's emotions and adjust the detection sensitivity based on those emotions. For example, if the user is feeling anxious, the detection sensitivity can be increased to detect even subtle movements, thereby reducing the user's anxiety. Conversely, if the user is relaxed, the detection sensitivity can be decreased to ignore normal movements, allowing the user to maintain a relaxed state. Furthermore, if the user is in a hurry, the sensitivity can be adjusted to detect only important movements. This enables appropriate detection tailored to the user's situation.

[0103] The analysis unit can estimate the user's emotions and adjust the abnormality detection criteria based on those emotions. For example, if the user is feeling anxious, the abnormality detection criteria can be tightened to detect even minor abnormalities. This can reduce the user's anxiety. Conversely, if the user is relaxed, the abnormality detection criteria can be loosened, preventing normal movements from being judged as abnormal. This allows the user to maintain a relaxed state. Furthermore, if the user is in a hurry, only important abnormalities can be prioritized for detection. This enables appropriate abnormality detection tailored to the user's situation.

[0104] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. For example, if the user is feeling anxious, the urgency of the notification can be increased to send a quick notification, thereby reducing the user's anxiety. Conversely, if the user is relaxed, the urgency of the notification can be lowered to send a normal notification, allowing the user to maintain a relaxed state. Furthermore, if the user is in a hurry, only important notifications can be prioritized. This enables appropriate notifications tailored to the user's situation.

[0105] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, if the user is feeling anxious, it can send a notification with detailed information, thereby reducing the user's anxiety. If the user is relaxed, it can send a concise notification, helping the user maintain a relaxed state. Furthermore, if the user is in a hurry, it can send a notification containing only essential information. This enables appropriate notifications tailored to the user's situation.

[0106] The protection unit can estimate the user's emotions and adjust the data protection level based on those emotions. For example, if the user is feeling anxious, the data protection level can be increased to enhance security, thereby reducing the user's anxiety. If the user is relaxed, the data protection level can be set to normal, allowing the user to maintain a relaxed state. Furthermore, if the user is in a hurry, only important data can be prioritized for protection. This enables appropriate data protection tailored to the user's situation.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The detection unit detects movement in the surroundings. For example, using cameras and sensors built into the nameplate, infrared cameras, motion detection sensors, and sound sensors, it can detect even minute movements and sounds day or night. Step 2: The analysis unit analyzes the movements detected by the detection unit in real time. For example, it can use AI to analyze movement patterns and detect people moving differently from normal pedestrians, cars staying in the same place for a long time, or abnormalities in the speed or direction of movement. Step 3: The notification unit notifies residents if an anomaly is detected by the analysis unit. For example, it can send warning messages to smartphones, computers, and smartwatches. Step 4: The prevention unit prevents data tampering. For example, it uses blockchain technology to record detected movement data and AI analysis results to ensure data reliability. It can also apply multiple security protocols.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the detection unit, analysis unit, notification unit, and prevention unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the smart device 14 to detect surrounding movement. The analysis unit uses the identification processing unit 290 of the data processing unit 12 to analyze the detected movement in real time and detect abnormal movement. The notification unit sends a warning message to the resident's smartphone or PC via the control unit 46A of the smart device 14 when an abnormality is detected. The prevention unit uses the identification processing unit 290 of the data processing unit 12 to prevent data tampering using blockchain technology. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the detection unit, analysis unit, notification unit, and prevention unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit detects surrounding movement using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the detected movement in real time using the identification processing unit 290 of the data processing unit 12 and detects abnormal movement. If an abnormality is detected, the notification unit sends a warning message to the resident's smartphone or PC via the control unit 46A of the smart glasses 214. The prevention unit can prevent data tampering using blockchain technology using the identification processing unit 290 of the data processing unit 12. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the detection unit, analysis unit, notification unit, and prevention unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the headset terminal 314 to detect surrounding movement. The analysis unit uses the identification processing unit 290 of the data processing unit 12 to analyze the detected movement in real time and detect abnormal movement. If an abnormality is detected, the notification unit sends a warning message to the resident's smartphone or PC via the control unit 46A of the headset terminal 314. The prevention unit uses the identification processing unit 290 of the data processing unit 12 to prevent data tampering using blockchain technology. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the detection unit, analysis unit, notification unit, and prevention unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the robot 414 to detect movement in the surroundings. The analysis unit uses the identification processing unit 290 of the data processing unit 12 to analyze the detected movement in real time and detect abnormal movement. If an abnormality is detected, the notification unit sends a warning message to the resident's smartphone or PC via the control unit 46A of the robot 414. The prevention unit uses the identification processing unit 290 of the data processing unit 12 to prevent data tampering using blockchain technology. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A detection unit that detects surrounding movements, An analysis unit analyzes the motion detected by the aforementioned detection unit, A notification unit that notifies residents when an abnormality is detected by the aforementioned analysis unit, It includes a data tampering prevention unit. A system characterized by the following features. (Note 2) The detection unit, The nameplate uses built-in cameras and sensors to detect movement in the surroundings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It analyzes detected movements in real time and detects abnormal movements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If an anomaly is detected, a warning message will be sent to residents' smartphones and computers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned prevention unit is Blockchain technology is used to prevent data tampering. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit, It estimates the user's emotions and adjusts the detection sensitivity based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit, It also detects ambient sounds simultaneously and identifies abnormal sound patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit, It also detects changes in temperature and humidity to identify abnormal environmental changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit, It estimates the user's emotions and dynamically changes the detection area based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit, It detects changes in ambient light and identifies abnormal light patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit, Abnormal vibrations are detected using vibration sensors. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the criteria for detecting abnormalities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is By referencing past anomaly data, we learn anomaly patterns and improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Analyze the frequency of anomalies and assess their severity. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and determines the priority of anomalies based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Identify the location of the anomaly and map the area where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Analyze the time periods when anomalies occur and predict the trend of anomaly occurrence. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned notification unit, Select a different notification method depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, Provide detailed information, including the location where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and customizes the content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The system automatically contacts the police and security company in the event of an anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, In the event of an anomaly, a notification will also be sent to nearby residents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned prevention unit is It estimates the user's emotions and adjusts the level of data protection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned prevention unit is Automatically creates a backup when data tampering is detected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned prevention unit is We apply multi-layered security protocols to prevent data tampering. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned prevention unit is It estimates user sentiment and customizes data protection methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned prevention unit is To prevent data tampering, different blockchain technologies are combined. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned prevention unit is To prevent data tampering, data integrity is checked in real time. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 detection unit that detects surrounding movements, An analysis unit analyzes the motion detected by the aforementioned detection unit, A notification unit that notifies residents when an abnormality is detected by the aforementioned analysis unit, It includes a data tampering prevention unit. A system characterized by the following features.

2. The detection unit, The nameplate uses built-in cameras and sensors to detect movement in the surroundings. The system according to feature 1.

3. The aforementioned analysis unit is It analyzes detected movements in real time and detects abnormal movements. The system according to feature 1.

4. The aforementioned notification unit, If an anomaly is detected, a warning message will be sent to residents' smartphones and computers. The system according to feature 1.

5. The aforementioned prevention unit is Blockchain technology is used to prevent data tampering. The system according to feature 1.

6. The detection unit, It estimates the user's emotions and adjusts the detection sensitivity based on the estimated emotions. The system according to feature 1.

7. The detection unit, It also detects ambient sounds simultaneously and identifies abnormal sound patterns. The system according to feature 1.

8. The detection unit, It also detects changes in temperature and humidity to identify abnormal environmental changes. The system according to feature 1.

9. The detection unit, It estimates the user's emotions and dynamically changes the detection area based on the estimated user emotions. The system according to feature 1.

10. The detection unit, It detects changes in ambient light and identifies abnormal light patterns. The system according to feature 1.

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