system

Abstract

The system according to this embodiment aims to detect abnormalities within a house in real time and take appropriate action. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a calling unit, a notification unit, and an operation unit. The collection unit collects information from cameras and microphone devices installed in the house. The analysis unit analyzes the information collected by the collection unit to determine when, who, where, and what the residents are doing. The calling unit makes an audible call when the analysis unit detects an anomaly. The notification unit notifies the security company when the analysis unit detects an anomaly. The operation unit operates IoT devices based on the resident's voice commands during normal operation.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to detect an abnormality in a house in real time and take appropriate measures.

[0005] The system according to the embodiment aims to detect an abnormality in a house in real time and take appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a calling unit, a notification unit, and an operation unit. The data collection unit collects information from cameras and microphones installed in the house. The analysis unit analyzes the information collected by the data collection unit to determine when, who, where, and what residents are doing. The calling unit makes an audible call when the analysis unit detects an anomaly. The notification unit notifies the security company when the analysis unit detects an anomaly. The operation unit operates IoT devices based on the resident's voice commands during normal operation. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormalities within a house in real time and take appropriate action. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home security system according to an embodiment of the present invention is a system that aggregates information from cameras and microphones installed in a house and captures the actions of residents using local AI. This home security system collects information from cameras and microphones installed in a house and analyzes it with local AI to capture when, who, where, and what residents are doing. For example, the home security system collects information from cameras and microphones installed in a house. In this process, each device aggregates information via Wi-Fi. For example, cameras collect video information and microphones collect audio information. This allows for centralized management of various information within the house. Next, the home security system uses local AI to analyze the collected information. The local AI analyzes the collected video and audio information to capture when, who, where, and what residents are doing. For example, if a resident is watching television in the living room, the local AI analyzes this information and recognizes that the resident is in the living room. If an anomaly is detected, the home security system makes an audio call or notifies a security company. For example, if a person other than a resident is acting alone, the local AI detects the anomaly and makes an audio call through the microphone. It also notifies a security company as necessary. This enhances security within the home. Normally, the home security system functions as an AI assistant for the resident. For example, if a resident voice-commands "Turn off the lights," the local AI analyzes the command and turns off the lights. The local AI also makes voice-guided suggestions such as "Shall I raise the air conditioning temperature?" or "Shall I heat the bathwater?" This supports the resident's daily life. This system enhances security within the home while improving the convenience of the resident's life. For example, the local AI can detect anomalies and respond quickly to crimes such as illegal scams. Furthermore, because it requires no special installation work and can be offered at a low price, widespread adoption is expected. In summary, the home security system enhances security within the home and improves the convenience of the resident's life.

[0029] The home security system according to this embodiment comprises a data collection unit, an analysis unit, a calling unit, a notification unit, and an operation unit. The data collection unit collects information from cameras and microphone devices installed in the house. For example, the data collection unit collects video information from cameras and audio information from microphones. The data collection unit can aggregate information via Wi-Fi. The analysis unit analyzes the information collected by the data collection unit to determine when, who, where, and what residents are doing. For example, the analysis unit analyzes video information to identify the location of residents and analyzes audio information to understand the residents' actions. The analysis unit can use AI to analyze the information. The calling unit makes an audio call when an abnormality is detected by the analysis unit. For example, the calling unit issues a warning message via a microphone. The calling unit can use AI to make an audio call. The notification unit notifies a security company when an abnormality is detected by the analysis unit. For example, the notification unit notifies the security company via telephone or email. The notification unit can use AI to make notifications. The control unit operates IoT devices based on the resident's voice commands during normal operation. The control unit operates IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI. As a result, the home security system according to this embodiment can enhance security within the house and improve the convenience of the resident's life.

[0030] The data collection unit gathers information from cameras and microphones installed within the house. Specifically, cameras capture high-resolution video in real time, monitoring key points such as rooms, entrances, and windows. The cameras are equipped with night vision and motion detection capabilities, providing clear images even at night or in low-light environments. Microphones collect audio from within the house with high sensitivity, detecting unusual or suspicious sounds. For example, they can detect sounds such as breaking glass, doors opening and closing, and footsteps, and transmit this information to the data collection unit. The data collection unit aggregates this video and audio information into a central database via Wi-Fi and provides it to the analysis unit in real time. Furthermore, the data collection unit can also collect data from various sensors within the house. For example, it collects data from open / close sensors, motion detection sensors, temperature sensors, and humidity sensors installed on doors and windows, providing a detailed understanding of the environment and conditions within the house. As a result, the data collection unit can gather a wide range of data from diverse sources, providing a foundation for enhancing home security.

[0031] The analysis unit analyzes the information collected by the collection unit to capture when, who, where, and what residents are doing. Specifically, it analyzes video information to identify residents' locations and analyzes audio information to understand residents' behavior. The analysis unit can use AI to analyze information. For example, video analysis uses facial recognition technology and motion detection technology to identify residents and suspicious individuals. Facial recognition technology can identify specific individuals by comparing them with pre-registered facial data of residents. Motion detection technology detects movement in the video and identifies abnormal or suspicious movements. Audio analysis uses speech recognition technology and natural language processing technology to analyze the content and patterns of residents' speech. Speech recognition technology converts residents' speech into text data, and natural language processing technology is used to understand the content of the speech. As a result, the analysis unit can grasp the behavior and situation of residents in detail and respond quickly if an anomaly occurs. Furthermore, the analysis unit can also use past data and statistical information to analyze patterns and trends of anomalies and predict future risks. This allows the analysis unit to not only grasp the situation in real time but also handle long-term risk management, thereby improving the reliability and safety of the entire system.

[0032] The calling unit issues an audible alert when an anomaly is detected by the analysis unit. Specifically, it emits a warning message through a microphone. The calling unit can use AI to issue voice alerts. For example, if an anomaly is detected, the calling unit will emit a warning message such as "An intruder has been detected. Please leave immediately" through the speakers in the house. The voice alert is also made to residents to inform them that an anomaly has occurred. For example, it may emit a message such as "There is an intruder at the front door. We will contact the security company," prompting residents to take a quick action. The calling unit can generate warning messages in a natural voice using speech synthesis technology. This allows the calling unit to issue warnings quickly and effectively when an anomaly occurs, ensuring the safety of residents. Furthermore, the calling unit can also emit specific messages based on voice instructions from residents. For example, if a resident instructs "Emit a warning message," the calling unit will emit the specified message. This allows the calling unit to respond flexibly and provide security measures tailored to the needs of residents.

[0033] The reporting unit notifies the security company when an anomaly is detected by the analysis unit. Specifically, it notifies the security company via telephone or email. The reporting unit can use AI to make notifications. For example, if an anomaly is detected, the reporting unit will automatically call the security company and deliver a message such as, "An anomaly has been detected inside the residence. Please take immediate action." It can also send detailed information about the anomaly and video data to the security company via email. This allows the security company to respond quickly. The reporting unit can pre-configure the content of notifications and the recipients, and can make appropriate notifications depending on the type and situation of the anomaly. For example, in the event of a fire, it will notify the fire department, and if an intruder enters, it will notify the police. This allows the reporting unit to support a quick and appropriate response when an anomaly occurs, ensuring the safety of residents. Furthermore, the reporting unit records the notification history and can review it later. This allows for an understanding of the content of notifications and the response status, and enables the implementation of improvement measures as needed.

[0034] The control unit operates IoT devices based on the resident's voice commands during normal operation. Specifically, it controls IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI. For example, if a resident commands "Turn on the lights," the control unit will automatically turn on the lights. If a resident commands "Turn on the air conditioner," the control unit will automatically start the air conditioner and adjust it to the set temperature. Furthermore, if a resident commands "Fill the bath," the control unit will automatically heat the bath water and maintain it at the appropriate temperature. The control unit can accurately understand the resident's commands using voice recognition technology and perform appropriate operations. This allows the control unit to improve the convenience of the resident's life. In addition, the control unit can learn the resident's lifestyle patterns and preferences to provide a more comfortable environment. For example, if a resident has a habit of turning on the lights at a specific time every day, the control unit will automatically turn on the lights at that time. Also, if a resident prefers a specific temperature setting, the control unit will remember that setting and automatically adjust the air conditioner temperature. This allows the control unit to respond flexibly to the needs of residents, providing a comfortable living environment.

[0035] The data collection unit can aggregate information via Wi-Fi. For example, the data collection unit aggregates video information from cameras and audio information from microphones via Wi-Fi. By using Wi-Fi, the data collection unit can efficiently aggregate information from various devices. This allows the data collection unit to centrally manage various types of information within the home. Some or all of the processing described above in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can collect information from cameras and microphones via Wi-Fi and input that information into AI for analysis.

[0036] The analysis unit can analyze collected video and audio information to capture when, who, where, and what residents are doing. For example, the analysis unit can analyze video information to identify residents' locations and analyze audio information to understand residents' actions. The analysis unit can use AI to analyze the information. This allows the analysis unit to accurately capture residents' actions. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected video and audio information into the AI, which can then analyze that information to capture residents' actions.

[0037] The calling unit can make an audio call through the microphone when an anomaly is detected. For example, the calling unit can issue a warning message through the microphone. The calling unit can use AI to make the audio call. This allows the calling unit to respond quickly in the event of an anomaly. Some or all of the above-described processes in the calling unit may be performed using AI or not. For example, when the calling unit makes an audio call through the microphone when an anomaly is detected, the AI ​​can generate a warning message and broadcast that message through the microphone.

[0038] The reporting unit can notify the security company when an anomaly is detected. The reporting unit can notify the security company, for example, by telephone or email. The reporting unit can use AI to make notifications. This allows the reporting unit to quickly notify the security company in the event of an anomaly. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, when the reporting unit detects an anomaly and notifies the security company, the AI ​​can generate the content of the notification and send it to the security company by telephone or email.

[0039] The control unit can operate IoT devices such as lighting, air conditioning, baths, and electrical appliances based on the resident's voice commands. For example, if the resident says "turn off the lights" by voice, the control unit will turn off the lights. The control unit can operate IoT devices using AI. This allows the control unit to improve the convenience of the resident's life. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input the resident's voice commands into the AI, which can then analyze the commands and operate the IoT devices.

[0040] The data collection unit can dynamically change the types of information it collects based on the resident's behavior patterns. For example, if the resident is in the living room, the data collection unit prioritizes collecting video information from the camera. If the resident is in the bedroom, the data collection unit prioritizes collecting audio information from the microphone. If the resident is out, the data collection unit temporarily suspends all information collection to reduce energy consumption. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's behavior patterns, and the AI ​​can analyze that information and dynamically change the types of information it collects.

[0041] The data collection unit can adjust the accuracy of the information it collects according to the resident's current activities. For example, if the resident is watching television, the data collection unit will lower the accuracy of audio information and increase the accuracy of video information. If the resident is on the phone, the data collection unit will increase the accuracy of audio information and decrease the accuracy of video information. If the resident is cooking, the data collection unit will increase the accuracy of both audio and video information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's current activities, and the AI ​​can analyze that information to adjust the accuracy of the information it collects.

[0042] The data collection unit can filter the information it collects based on the resident's geographical location. For example, if the resident is in the living room, the data collection unit prioritizes collecting information from the living room camera and microphone. If the resident is in the bedroom, the data collection unit prioritizes collecting information from the bedroom camera and microphone. If the resident is out, the data collection unit pauses all information collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location, and the AI ​​can analyze that information and filter the information to be collected.

[0043] The data collection unit can supplement the information it collects based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the data collection unit will reduce the frequency of information collection. If the resident posts on social media that they are stressed, the data collection unit will increase the frequency of information collection. If the resident posts on social media that they are out, the data collection unit will temporarily suspend information collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the data collection unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the information it collects.

[0044] The analysis unit can dynamically change the level of detail of the information being analyzed based on the resident's behavior patterns. For example, if the resident is in the living room, the analysis unit increases the level of detail of the camera video information. If the resident is in the bedroom, the analysis unit increases the level of detail of the microphone audio information. If the resident is out, the analysis unit decreases the level of detail of all information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's behavior patterns, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and dynamically change the level of detail of the information being analyzed.

[0045] The analysis unit can adjust the type of information to be analyzed according to the resident's current activities. For example, if the resident is watching television, the analysis unit prioritizes the analysis of audio information. If the resident is on the phone, the analysis unit prioritizes the analysis of video information. If the resident is cooking, the analysis unit analyzes both audio and video information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's current activities, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and adjust the type of information to be analyzed.

[0046] The analysis unit can filter the information to be analyzed based on the resident's geographical location. For example, if the resident is in the living room, the analysis unit prioritizes analyzing the information from the living room camera and microphone. If the resident is in the bedroom, the analysis unit prioritizes analyzing the information from the bedroom camera and microphone. If the resident is out, the analysis unit temporarily suspends the analysis of all information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's geographical location, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and filter the information to be analyzed.

[0047] The analysis unit can supplement the information it analyzes based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the analysis unit will reduce the frequency of analysis. If the resident posts on social media that they are stressed, the analysis unit will increase the frequency of analysis. If the resident posts on social media that they are out, the analysis unit will pause the analysis. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the analysis unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the information it analyzes.

[0048] The calling unit can dynamically change the timing of voice calls depending on the type of anomaly. For example, if the anomaly is minor, the calling unit will delay the timing of the call. If the anomaly is serious, the calling unit will make a call immediately. If the anomaly persists, the calling unit will make calls periodically. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from the camera and microphone into the AI ​​to analyze the type of anomaly, and the AI ​​can analyze that information to dynamically change the timing of voice calls.

[0049] The calling unit can adjust the volume of the voice call according to the severity of the anomaly. For example, if the anomaly is minor, the calling unit will set the volume low. If the anomaly is serious, the calling unit will set the volume high. If the anomaly persists, the calling unit will gradually increase the volume. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from the camera and microphone into the AI ​​to analyze the severity of the anomaly, and the AI ​​can analyze that information to adjust the volume of the voice call.

[0050] The calling unit can adjust the content of voice calls based on the resident's geographical location information. For example, if the resident is in the living room, the calling unit will make a call related to the living room. If the resident is in the bedroom, the calling unit will make a call related to the bedroom. If the resident is out, the calling unit will pause the call. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location information, and the AI ​​can analyze that information to adjust the content of the voice call.

[0051] The calling unit can supplement the content of the voice call based on the resident's social media activity. For example, if the calling unit posts on social media that the resident is relaxed, it will make a call in a gentle tone. If the calling unit posts on social media that the resident is stressed, it will make a quick and clear call. If the calling unit posts on social media that the resident is out, it will pause the call. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input the content of social media posts into an AI to analyze the resident's social media activity, and the AI ​​can analyze that information to supplement the content of the voice call.

[0052] The notification unit can dynamically change the timing of notifications depending on the type of anomaly. For example, if the anomaly is minor, the notification unit will delay the timing of the notification. If the anomaly is serious, the notification unit will make an immediate notification. If the anomaly persists, the notification unit will make periodic notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information from cameras and microphones into the AI ​​to analyze the type of anomaly, and the AI ​​can analyze that information to dynamically change the timing of notifications.

[0053] The reporting unit can adjust the level of detail in the report according to the severity of the anomaly. For example, if the anomaly is minor, the reporting unit will summarize the report concisely. If the anomaly is serious, the reporting unit will describe the report in detail. If the anomaly is ongoing, the reporting unit will gradually increase the level of detail in the report. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, in order to analyze the severity of the anomaly, the reporting unit can input information from cameras and microphones into the AI, which can then analyze that information and adjust the level of detail in the report.

[0054] The notification unit can adjust the content of its notifications based on the resident's geographical location. For example, if the resident is in the living room, the notification unit will issue a notification related to the living room. If the resident is in the bedroom, the notification unit will issue a notification related to the bedroom. If the resident is out, the notification unit will pause the notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location, and the AI ​​can analyze that information to adjust the content of the notification.

[0055] The reporting unit can supplement the content of reports based on the resident's social media activity. For example, if the reporting unit posts on social media that the resident is relaxing, it will make a brief report. If the reporting unit posts on social media that the resident is stressed, it will make a detailed report. If the reporting unit posts on social media that the resident is out, it will suspend reporting. Some or all of the above processing by the reporting unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the reporting unit can input the content of social media posts into an AI, and the AI ​​can analyze that information to supplement the content of the report.

[0056] The control unit can dynamically change the timing of IoT device operations based on the resident's behavior patterns. For example, if the resident is in the living room, the control unit prioritizes operating IoT devices in the living room. If the resident is in the bedroom, the control unit prioritizes operating IoT devices in the bedroom. If the resident is out, the control unit pauses all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's behavior patterns, and the AI ​​can analyze that information to dynamically change the timing of IoT device operations.

[0057] The control unit can adjust the level of detail in the operation of IoT devices according to the resident's current activities. For example, if the resident is watching television, the control unit will lower the level of detail in voice control. If the resident is on the phone, the control unit will increase the level of detail in voice control. If the resident is cooking, the control unit will increase the level of detail in both voice and video control. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's current activities, and the AI ​​can analyze that information to adjust the level of detail in the operation of IoT devices.

[0058] The control unit can adjust the operation of IoT devices based on the resident's geographical location information. For example, if the resident is in the living room, the control unit will prioritize the operation of IoT devices in the living room. If the resident is in the bedroom, the control unit will prioritize the operation of IoT devices in the bedroom. If the resident is out, the control unit will pause all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location information, and the AI ​​can analyze that information to adjust the operation of IoT devices.

[0059] The control unit can supplement the operation of IoT devices based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the control unit will perform gentle operations. If the resident posts on social media that they are stressed, the control unit will perform quick and clear operations. If the resident posts on social media that they are out, the control unit will pause all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the control unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the operation of IoT devices.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] Home security systems can also include a health management unit that monitors the health status of residents. This unit collects vital signs such as heart rate, body temperature, and blood pressure, and issues warnings if abnormalities are detected. For example, if a resident's heart rate increases rapidly, the health management unit analyzes the information and sends a message encouraging the resident to rest. If a resident's body temperature is high, the health management unit can automatically adjust the air conditioning to promote cooling. Furthermore, if a resident's blood pressure is abnormally high, the health management unit can notify a medical institution. In this way, home security systems can provide functions to protect the health of residents.

[0062] Home security systems can also be equipped with a learning unit that learns the residents' lifestyle patterns. The learning unit collects the residents' behavioral data over a long period and analyzes their lifestyle patterns. For example, if the system learns that a resident wakes up at 7 a.m. every morning and leaves for work at 8 a.m., the learning unit can use that information to automatically turn on the lights before the resident wakes up. Similarly, if the system learns that a resident goes to bed at 10 p.m. every night, the learning unit can use that information to play relaxing music before bedtime. Furthermore, if the system learns that a resident goes out on weekends, the learning unit can use that information to enhance security while the resident is away. This allows home security systems to respond flexibly to the resident's lifestyle.

[0063] The home security system can also include a meal management unit to manage the resident's diet. This unit records the resident's meals and analyzes their nutritional balance. For example, when a resident eats, the unit can recognize the meal content via camera and analyze its nutritional balance. Furthermore, if a resident is consuming too much of a particular nutrient, the unit can use this information to suggest nutrients that should be consumed in the next meal. Additionally, if a resident is on a diet, the unit can suggest low-calorie recipes to support calorie restriction. In this way, the home security system can support the resident's healthy eating habits.

[0064] The home security system can also include an exercise management unit to monitor the residents' exercise. This unit records the residents' activity levels and suggests appropriate exercises. For example, if a resident is habitually inactive, the unit can use this information to suggest a suitable exercise program. Furthermore, if a resident is performing a specific exercise, the unit can analyze its effectiveness and adjust its intensity and frequency. Additionally, while a resident is exercising, the unit can analyze their form in real time and provide guidance to ensure correct form. In this way, the home security system can support residents in developing healthy exercise habits.

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

[0066] Step 1: The collection unit collects information from cameras and microphones installed within the house. For example, it collects video information from cameras and audio information from microphones. The collection unit can aggregate the information via Wi-Fi. Step 2: The analysis unit analyzes the information collected by the collection unit to capture when, who, where, and what residents are doing. For example, it analyzes video information to identify residents' locations and analyzes audio information to understand residents' behavior. The analysis unit can use AI to analyze the information. Step 3: The calling unit issues a voice call when an anomaly is detected by the analysis unit. For example, it may issue a warning message through the microphone. The calling unit can use AI to issue voice calls. Step 4: The reporting unit notifies the security company if an anomaly is detected by the analysis unit. For example, it may notify the security company by phone or email. The reporting unit can use AI to make notifications. Step 5: The control unit operates IoT devices based on the resident's voice commands during normal operation. For example, it operates IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI.

[0067] (Example of form 2) The home security system according to an embodiment of the present invention is a system that aggregates information from cameras and microphones installed in a house and captures the actions of residents using local AI. This home security system collects information from cameras and microphones installed in a house and analyzes it with local AI to capture when, who, where, and what residents are doing. For example, the home security system collects information from cameras and microphones installed in a house. In this process, each device aggregates information via Wi-Fi. For example, cameras collect video information and microphones collect audio information. This allows for centralized management of various information within the house. Next, the home security system uses local AI to analyze the collected information. The local AI analyzes the collected video and audio information to capture when, who, where, and what residents are doing. For example, if a resident is watching television in the living room, the local AI analyzes this information and recognizes that the resident is in the living room. If an anomaly is detected, the home security system makes an audio call or notifies a security company. For example, if a person other than a resident is acting alone, the local AI detects the anomaly and makes an audio call through the microphone. It also notifies a security company as necessary. This enhances security within the home. Normally, the home security system functions as an AI assistant for the resident. For example, if a resident voice-commands "Turn off the lights," the local AI analyzes the command and turns off the lights. The local AI also makes voice-guided suggestions such as "Shall I raise the air conditioning temperature?" or "Shall I heat the bathwater?" This supports the resident's daily life. This system enhances security within the home while improving the convenience of the resident's life. For example, the local AI can detect anomalies and respond quickly to crimes such as illegal scams. Furthermore, because it requires no special installation work and can be offered at a low price, widespread adoption is expected. In summary, the home security system enhances security within the home and improves the convenience of the resident's life.

[0068] The home security system according to this embodiment comprises a data collection unit, an analysis unit, a calling unit, a notification unit, and an operation unit. The data collection unit collects information from cameras and microphone devices installed in the house. For example, the data collection unit collects video information from cameras and audio information from microphones. The data collection unit can aggregate information via Wi-Fi. The analysis unit analyzes the information collected by the data collection unit to determine when, who, where, and what residents are doing. For example, the analysis unit analyzes video information to identify the location of residents and analyzes audio information to understand the residents' actions. The analysis unit can use AI to analyze the information. The calling unit makes an audio call when an abnormality is detected by the analysis unit. For example, the calling unit issues a warning message via a microphone. The calling unit can use AI to make an audio call. The notification unit notifies a security company when an abnormality is detected by the analysis unit. For example, the notification unit notifies the security company via telephone or email. The notification unit can use AI to make notifications. The control unit operates IoT devices based on the resident's voice commands during normal operation. The control unit operates IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI. As a result, the home security system according to this embodiment can enhance security within the house and improve the convenience of the resident's life.

[0069] The data collection unit gathers information from cameras and microphones installed within the house. Specifically, cameras capture high-resolution video in real time, monitoring key points such as rooms, entrances, and windows. The cameras are equipped with night vision and motion detection capabilities, providing clear images even at night or in low-light environments. Microphones collect audio from within the house with high sensitivity, detecting unusual or suspicious sounds. For example, they can detect sounds such as breaking glass, doors opening and closing, and footsteps, and transmit this information to the data collection unit. The data collection unit aggregates this video and audio information into a central database via Wi-Fi and provides it to the analysis unit in real time. Furthermore, the data collection unit can also collect data from various sensors within the house. For example, it collects data from open / close sensors, motion detection sensors, temperature sensors, and humidity sensors installed on doors and windows, providing a detailed understanding of the environment and conditions within the house. As a result, the data collection unit can gather a wide range of data from diverse sources, providing a foundation for enhancing home security.

[0070] The analysis unit analyzes the information collected by the collection unit to capture when, who, where, and what residents are doing. Specifically, it analyzes video information to identify residents' locations and analyzes audio information to understand residents' behavior. The analysis unit can use AI to analyze information. For example, video analysis uses facial recognition technology and motion detection technology to identify residents and suspicious individuals. Facial recognition technology can identify specific individuals by comparing them with pre-registered facial data of residents. Motion detection technology detects movement in the video and identifies abnormal or suspicious movements. Audio analysis uses speech recognition technology and natural language processing technology to analyze the content and patterns of residents' speech. Speech recognition technology converts residents' speech into text data, and natural language processing technology is used to understand the content of the speech. As a result, the analysis unit can grasp the behavior and situation of residents in detail and respond quickly if an anomaly occurs. Furthermore, the analysis unit can also use past data and statistical information to analyze patterns and trends of anomalies and predict future risks. This allows the analysis unit to not only grasp the situation in real time but also handle long-term risk management, thereby improving the reliability and safety of the entire system.

[0071] The calling unit issues an audible alert when an anomaly is detected by the analysis unit. Specifically, it emits a warning message through a microphone. The calling unit can use AI to issue voice alerts. For example, if an anomaly is detected, the calling unit will emit a warning message such as "An intruder has been detected. Please leave immediately" through the speakers in the house. The voice alert is also made to residents to inform them that an anomaly has occurred. For example, it may emit a message such as "There is an intruder at the front door. We will contact the security company," prompting residents to take a quick action. The calling unit can generate warning messages in a natural voice using speech synthesis technology. This allows the calling unit to issue warnings quickly and effectively when an anomaly occurs, ensuring the safety of residents. Furthermore, the calling unit can also emit specific messages based on voice instructions from residents. For example, if a resident instructs "Emit a warning message," the calling unit will emit the specified message. This allows the calling unit to respond flexibly and provide security measures tailored to the needs of residents.

[0072] The reporting unit notifies the security company when an anomaly is detected by the analysis unit. Specifically, it notifies the security company via telephone or email. The reporting unit can use AI to make notifications. For example, if an anomaly is detected, the reporting unit will automatically call the security company and deliver a message such as, "An anomaly has been detected inside the residence. Please take immediate action." It can also send detailed information about the anomaly and video data to the security company via email. This allows the security company to respond quickly. The reporting unit can pre-configure the content of notifications and the recipients, and can make appropriate notifications depending on the type and situation of the anomaly. For example, in the event of a fire, it will notify the fire department, and if an intruder enters, it will notify the police. This allows the reporting unit to support a quick and appropriate response when an anomaly occurs, ensuring the safety of residents. Furthermore, the reporting unit records the notification history and can review it later. This allows for an understanding of the content of notifications and the response status, and enables the implementation of improvement measures as needed.

[0073] The control unit operates IoT devices based on the resident's voice commands during normal operation. Specifically, it controls IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI. For example, if a resident commands "Turn on the lights," the control unit will automatically turn on the lights. If a resident commands "Turn on the air conditioner," the control unit will automatically start the air conditioner and adjust it to the set temperature. Furthermore, if a resident commands "Fill the bath," the control unit will automatically heat the bath water and maintain it at the appropriate temperature. The control unit can accurately understand the resident's commands using voice recognition technology and perform appropriate operations. This allows the control unit to improve the convenience of the resident's life. In addition, the control unit can learn the resident's lifestyle patterns and preferences to provide a more comfortable environment. For example, if a resident has a habit of turning on the lights at a specific time every day, the control unit will automatically turn on the lights at that time. Also, if a resident prefers a specific temperature setting, the control unit will remember that setting and automatically adjust the air conditioner temperature. This allows the control unit to respond flexibly to the needs of residents, providing a comfortable living environment.

[0074] The data collection unit can aggregate information via Wi-Fi. For example, the data collection unit aggregates video information from cameras and audio information from microphones via Wi-Fi. By using Wi-Fi, the data collection unit can efficiently aggregate information from various devices. This allows the data collection unit to centrally manage various types of information within the home. Some or all of the processing described above in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can collect information from cameras and microphones via Wi-Fi and input that information into AI for analysis.

[0075] The analysis unit can analyze collected video and audio information to capture when, who, where, and what residents are doing. For example, the analysis unit can analyze video information to identify residents' locations and analyze audio information to understand residents' actions. The analysis unit can use AI to analyze the information. This allows the analysis unit to accurately capture residents' actions. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected video and audio information into the AI, which can then analyze that information to capture residents' actions.

[0076] The calling unit can make an audio call through the microphone when an anomaly is detected. For example, the calling unit can issue a warning message through the microphone. The calling unit can use AI to make the audio call. This allows the calling unit to respond quickly in the event of an anomaly. Some or all of the above-described processes in the calling unit may be performed using AI or not. For example, when the calling unit makes an audio call through the microphone when an anomaly is detected, the AI ​​can generate a warning message and broadcast that message through the microphone.

[0077] The reporting unit can notify the security company when an anomaly is detected. The reporting unit can notify the security company, for example, by telephone or email. The reporting unit can use AI to make notifications. This allows the reporting unit to quickly notify the security company in the event of an anomaly. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, when the reporting unit detects an anomaly and notifies the security company, the AI ​​can generate the content of the notification and send it to the security company by telephone or email.

[0078] The control unit can operate IoT devices such as lighting, air conditioning, baths, and electrical appliances based on the resident's voice commands. For example, if the resident says "turn off the lights" by voice, the control unit will turn off the lights. The control unit can operate IoT devices using AI. This allows the control unit to improve the convenience of the resident's life. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input the resident's voice commands into the AI, which can then analyze the commands and operate the IoT devices.

[0079] The data collection unit can estimate the resident's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the resident is relaxed, the data collection unit will reduce the frequency of information collection to respect privacy. If the resident is stressed, the data collection unit will increase the frequency of information collection to aim for early detection of abnormalities. If the resident is absent, the data collection unit will automatically adjust the timing of information collection to minimize energy consumption. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, to estimate the resident's emotions, the data collection unit can input information from cameras and microphones into the AI, which can then analyze the information to estimate the resident's emotions.

[0080] The data collection unit can dynamically change the types of information it collects based on the resident's behavior patterns. For example, if the resident is in the living room, the data collection unit prioritizes collecting video information from the camera. If the resident is in the bedroom, the data collection unit prioritizes collecting audio information from the microphone. If the resident is out, the data collection unit temporarily suspends all information collection to reduce energy consumption. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's behavior patterns, and the AI ​​can analyze that information and dynamically change the types of information it collects.

[0081] The data collection unit can adjust the accuracy of the information it collects according to the resident's current activities. For example, if the resident is watching television, the data collection unit will lower the accuracy of audio information and increase the accuracy of video information. If the resident is on the phone, the data collection unit will increase the accuracy of audio information and decrease the accuracy of video information. If the resident is cooking, the data collection unit will increase the accuracy of both audio and video information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's current activities, and the AI ​​can analyze that information to adjust the accuracy of the information it collects.

[0082] The data collection unit can estimate the resident's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the resident is relaxed, the data collection unit will prioritize collecting video information over audio information. If the resident is stressed, the data collection unit will prioritize collecting audio information over video information. If the resident is absent, the data collection unit will collect all information equally. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, to estimate the resident's emotions, the data collection unit can input information from cameras and microphones into an AI, which can then analyze the information to estimate the resident's emotions and determine the priority of information to collect.

[0083] The data collection unit can filter the information it collects based on the resident's geographical location. For example, if the resident is in the living room, the data collection unit prioritizes collecting information from the living room camera and microphone. If the resident is in the bedroom, the data collection unit prioritizes collecting information from the bedroom camera and microphone. If the resident is out, the data collection unit pauses all information collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location, and the AI ​​can analyze that information and filter the information to be collected.

[0084] The data collection unit can supplement the information it collects based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the data collection unit will reduce the frequency of information collection. If the resident posts on social media that they are stressed, the data collection unit will increase the frequency of information collection. If the resident posts on social media that they are out, the data collection unit will temporarily suspend information collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the data collection unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the information it collects.

[0085] The analysis unit can estimate the resident's emotions and adjust the analysis method based on the estimated emotions. For example, if the resident is relaxed, the analysis unit can reduce the frequency of analysis to respect privacy. If the resident is stressed, the analysis unit can increase the frequency of analysis to aim for early detection of abnormalities. If the resident is absent, the analysis unit can automatically adjust the timing of analysis to minimize energy consumption. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, to estimate the resident's emotions, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information to estimate the resident's emotions and adjust the analysis method.

[0086] The analysis unit can dynamically change the level of detail of the information being analyzed based on the resident's behavior patterns. For example, if the resident is in the living room, the analysis unit increases the level of detail of the camera video information. If the resident is in the bedroom, the analysis unit increases the level of detail of the microphone audio information. If the resident is out, the analysis unit decreases the level of detail of all information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's behavior patterns, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and dynamically change the level of detail of the information being analyzed.

[0087] The analysis unit can adjust the type of information to be analyzed according to the resident's current activities. For example, if the resident is watching television, the analysis unit prioritizes the analysis of audio information. If the resident is on the phone, the analysis unit prioritizes the analysis of video information. If the resident is cooking, the analysis unit analyzes both audio and video information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's current activities, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and adjust the type of information to be analyzed.

[0088] The analysis unit can estimate the resident's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the resident is tense, the analysis unit provides a simple and highly visible display method. If the resident is relaxed, the analysis unit provides a display method that includes detailed information. If the resident is in a hurry, the analysis unit provides a concise display method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, to estimate the resident's emotions, the analysis unit can input information from a camera or microphone into the AI, the AI ​​can analyze that information to estimate the resident's emotions, and adjust the display method of the analysis results.

[0089] The analysis unit can filter the information to be analyzed based on the resident's geographical location. For example, if the resident is in the living room, the analysis unit prioritizes analyzing the information from the living room camera and microphone. If the resident is in the bedroom, the analysis unit prioritizes analyzing the information from the bedroom camera and microphone. If the resident is out, the analysis unit temporarily suspends the analysis of all information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's geographical location, the analysis unit can input information from cameras and microphones into the AI, which can then analyze that information and filter the information to be analyzed.

[0090] The analysis unit can supplement the information it analyzes based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the analysis unit will reduce the frequency of analysis. If the resident posts on social media that they are stressed, the analysis unit will increase the frequency of analysis. If the resident posts on social media that they are out, the analysis unit will pause the analysis. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the analysis unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the information it analyzes.

[0091] The calling unit can estimate the resident's emotions and adjust the content of the call based on the estimated emotions. For example, if the resident is relaxed, the calling unit will call in a gentle tone. If the resident is stressed, the calling unit will call quickly and clearly. If the resident is absent, the calling unit will pause the call. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calling unit may be performed using AI or not. For example, to estimate the resident's emotions, the calling unit can input information from a camera or microphone into the AI, which can then analyze the information to estimate the resident's emotions and adjust the content of the call.

[0092] The calling unit can dynamically change the timing of voice calls depending on the type of anomaly. For example, if the anomaly is minor, the calling unit will delay the timing of the call. If the anomaly is serious, the calling unit will make a call immediately. If the anomaly persists, the calling unit will make calls periodically. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from the camera and microphone into the AI ​​to analyze the type of anomaly, and the AI ​​can analyze that information to dynamically change the timing of voice calls.

[0093] The calling unit can adjust the volume of the voice call according to the severity of the anomaly. For example, if the anomaly is minor, the calling unit will set the volume low. If the anomaly is serious, the calling unit will set the volume high. If the anomaly persists, the calling unit will gradually increase the volume. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from the camera and microphone into the AI ​​to analyze the severity of the anomaly, and the AI ​​can analyze that information to adjust the volume of the voice call.

[0094] The calling unit can estimate the resident's emotions and determine the priority of calls based on the estimated emotions. For example, if the resident is relaxed, the calling unit will prioritize less important calls. If the resident is stressed, the calling unit will prioritize more important calls. If the resident is absent, the calling unit will pause all calls. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calling unit may be performed using AI or not. For example, to estimate the resident's emotions, the calling unit can input information from a camera or microphone into an AI, which can then analyze that information to estimate the resident's emotions and determine the priority of calls.

[0095] The calling unit can adjust the content of voice calls based on the resident's geographical location information. For example, if the resident is in the living room, the calling unit will make a call related to the living room. If the resident is in the bedroom, the calling unit will make a call related to the bedroom. If the resident is out, the calling unit will pause the call. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location information, and the AI ​​can analyze that information to adjust the content of the voice call.

[0096] The calling unit can supplement the content of the voice call based on the resident's social media activity. For example, if the calling unit posts on social media that the resident is relaxed, it will make a call in a gentle tone. If the calling unit posts on social media that the resident is stressed, it will make a quick and clear call. If the calling unit posts on social media that the resident is out, it will pause the call. Some or all of the above processing in the calling unit may be performed using AI or not. For example, the calling unit can input the content of social media posts into an AI to analyze the resident's social media activity, and the AI ​​can analyze that information to supplement the content of the voice call.

[0097] The notification unit can estimate the resident's emotions and adjust the content of the notification based on the estimated emotions. For example, if the resident is relaxed, the notification unit will summarize the content of the notification concisely. If the resident is stressed, the notification unit will describe the content of the notification in detail. If the resident is absent, the notification unit will automatically generate the content of the notification and send it quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, in order to estimate the resident's emotions, the notification unit can input information from cameras and microphones into the AI, which can then analyze that information to estimate the resident's emotions and adjust the content of the notification.

[0098] The notification unit can dynamically change the timing of notifications depending on the type of anomaly. For example, if the anomaly is minor, the notification unit will delay the timing of the notification. If the anomaly is serious, the notification unit will make an immediate notification. If the anomaly persists, the notification unit will make periodic notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information from cameras and microphones into the AI ​​to analyze the type of anomaly, and the AI ​​can analyze that information to dynamically change the timing of notifications.

[0099] The reporting unit can adjust the level of detail in the report according to the severity of the anomaly. For example, if the anomaly is minor, the reporting unit will summarize the report concisely. If the anomaly is serious, the reporting unit will describe the report in detail. If the anomaly is ongoing, the reporting unit will gradually increase the level of detail in the report. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, in order to analyze the severity of the anomaly, the reporting unit can input information from cameras and microphones into the AI, which can then analyze that information and adjust the level of detail in the report.

[0100] The notification system can estimate the resident's emotions and prioritize notifications based on the estimated emotions. For example, if the resident is relaxed, the system will prioritize less important notifications. If the resident is stressed, the system will prioritize more important notifications. If the resident is absent, the system will pause all notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification system may be performed using AI or not. For example, to estimate the resident's emotions, the notification system can input information from cameras and microphones into the AI, which can then analyze the information to estimate the resident's emotions and determine the priority of notifications.

[0101] The notification unit can adjust the content of its notifications based on the resident's geographical location. For example, if the resident is in the living room, the notification unit will issue a notification related to the living room. If the resident is in the bedroom, the notification unit will issue a notification related to the bedroom. If the resident is out, the notification unit will pause the notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location, and the AI ​​can analyze that information to adjust the content of the notification.

[0102] The reporting unit can supplement the content of reports based on the resident's social media activity. For example, if the reporting unit posts on social media that the resident is relaxing, it will make a brief report. If the reporting unit posts on social media that the resident is stressed, it will make a detailed report. If the reporting unit posts on social media that the resident is out, it will suspend reporting. Some or all of the above processing by the reporting unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the reporting unit can input the content of social media posts into an AI, and the AI ​​can analyze that information to supplement the content of the report.

[0103] The control unit can estimate the resident's emotions and adjust the operation method of IoT devices based on the estimated resident's emotions. For example, if the resident is relaxed, the control unit provides a gentle operation method. If the resident is stressed, the control unit provides a quick and clear operation method. If the resident is absent, the control unit pauses all operations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, to estimate the resident's emotions, the control unit can input information from a camera or microphone into the AI, which can then analyze the information to estimate the resident's emotions and adjust the operation method of IoT devices.

[0104] The control unit can dynamically change the timing of IoT device operations based on the resident's behavior patterns. For example, if the resident is in the living room, the control unit prioritizes operating IoT devices in the living room. If the resident is in the bedroom, the control unit prioritizes operating IoT devices in the bedroom. If the resident is out, the control unit pauses all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's behavior patterns, and the AI ​​can analyze that information to dynamically change the timing of IoT device operations.

[0105] The control unit can adjust the level of detail in the operation of IoT devices according to the resident's current activities. For example, if the resident is watching television, the control unit will lower the level of detail in voice control. If the resident is on the phone, the control unit will increase the level of detail in voice control. If the resident is cooking, the control unit will increase the level of detail in both voice and video control. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's current activities, and the AI ​​can analyze that information to adjust the level of detail in the operation of IoT devices.

[0106] The control unit can estimate the resident's emotions and determine the priority of IoT device operations based on the estimated emotions. For example, if the resident is relaxed, the control unit will prioritize low-priority operations. If the resident is stressed, the control unit will prioritize high-priority operations. If the resident is absent, the control unit will pause all operations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, to estimate the resident's emotions, the control unit can input information from cameras and microphones into the AI, which will analyze the information to estimate the resident's emotions and determine the priority of IoT device operations.

[0107] The control unit can adjust the operation of IoT devices based on the resident's geographical location information. For example, if the resident is in the living room, the control unit will prioritize the operation of IoT devices in the living room. If the resident is in the bedroom, the control unit will prioritize the operation of IoT devices in the bedroom. If the resident is out, the control unit will pause all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input information from cameras and microphones into the AI ​​to analyze the resident's geographical location information, and the AI ​​can analyze that information to adjust the operation of IoT devices.

[0108] The control unit can supplement the operation of IoT devices based on the resident's social media activity. For example, if the resident posts on social media that they are relaxed, the control unit will perform gentle operations. If the resident posts on social media that they are stressed, the control unit will perform quick and clear operations. If the resident posts on social media that they are out, the control unit will pause all operations. Some or all of the above processing in the control unit may be performed using AI or not. For example, in order to analyze the resident's social media activity, the control unit can input the content of social media posts into AI, and the AI ​​can analyze that information to supplement the operation of IoT devices.

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

[0110] Home security systems can also include a health management unit that monitors the health status of residents. This unit collects vital signs such as heart rate, body temperature, and blood pressure, and issues warnings if abnormalities are detected. For example, if a resident's heart rate increases rapidly, the health management unit analyzes the information and sends a message encouraging the resident to rest. If a resident's body temperature is high, the health management unit can automatically adjust the air conditioning to promote cooling. Furthermore, if a resident's blood pressure is abnormally high, the health management unit can notify a medical institution. In this way, home security systems can provide functions to protect the health of residents.

[0111] Home security systems can also be equipped with a learning unit that learns the residents' lifestyle patterns. The learning unit collects the residents' behavioral data over a long period and analyzes their lifestyle patterns. For example, if the system learns that a resident wakes up at 7 a.m. every morning and leaves for work at 8 a.m., the learning unit can use that information to automatically turn on the lights before the resident wakes up. Similarly, if the system learns that a resident goes to bed at 10 p.m. every night, the learning unit can use that information to play relaxing music before bedtime. Furthermore, if the system learns that a resident goes out on weekends, the learning unit can use that information to enhance security while the resident is away. This allows home security systems to respond flexibly to the resident's lifestyle.

[0112] The home security system can also be equipped with a music provider that estimates the resident's emotions and selects music based on those emotions. The music provider analyzes the resident's emotions and plays music that matches those emotions. For example, if the resident is relaxed, the music provider can select and play relaxing music. If the resident is stressed, the music provider can select and play music to reduce stress. Furthermore, if the resident wants to feel energized, the music provider can select and play uplifting music. In this way, the home security system can provide music that matches the resident's emotions and support a comfortable life for the resident.

[0113] The home security system can also include a meal management unit to manage the resident's diet. This unit records the resident's meals and analyzes their nutritional balance. For example, when a resident eats, the unit can recognize the meal content via camera and analyze its nutritional balance. Furthermore, if a resident is consuming too much of a particular nutrient, the unit can use this information to suggest nutrients that should be consumed in the next meal. Additionally, if a resident is on a diet, the unit can suggest low-calorie recipes to support calorie restriction. In this way, the home security system can support the resident's healthy eating habits.

[0114] The home security system can also include an exercise management unit to monitor the residents' exercise. This unit records the residents' activity levels and suggests appropriate exercises. For example, if a resident is habitually inactive, the unit can use this information to suggest a suitable exercise program. Furthermore, if a resident is performing a specific exercise, the unit can analyze its effectiveness and adjust its intensity and frequency. Additionally, while a resident is exercising, the unit can analyze their form in real time and provide guidance to ensure correct form. In this way, the home security system can support residents in developing healthy exercise habits.

[0115] The home security system may also include a lighting control unit that estimates the resident's emotions and adjusts the color and brightness of the lighting based on those emotions. The lighting control unit analyzes the resident's emotions and provides a lighting environment that matches those emotions. For example, if the resident is relaxed, the lighting control unit can provide warm, soft light. If the resident wants to concentrate, the lighting control unit can provide white, bright light. Furthermore, if the resident wants to sleep, the lighting control unit can provide light that gradually dims, helping the resident fall asleep naturally. In this way, the home security system can provide a lighting environment that matches the resident's emotions and support a comfortable life for the resident.

[0116] The home security system may also include an air conditioning control unit that estimates the resident's emotions and adjusts the air conditioning settings based on those emotions. The air conditioning control unit analyzes the resident's emotions and provides an air conditioning environment that suits those emotions. For example, if the resident is relaxed, the air conditioning control unit can provide a comfortable temperature and humidity. If the resident is stressed, the air conditioning control unit can provide a cooler temperature to reduce stress. Furthermore, if the resident is exercising, the air conditioning control unit can provide a temperature and humidity suitable for exercise. In this way, the home security system can provide an air conditioning environment tailored to the resident's emotions and support a comfortable life for the resident.

[0117] The home security system may also include a notification adjustment unit that estimates the resident's emotions and adjusts the content of notifications based on those emotions. The notification adjustment unit analyzes the resident's emotions and provides a notification method that suits those emotions. For example, if the resident is relaxed, the notification adjustment unit can deliver notifications in a calm tone. If the resident is concentrating, the notification adjustment unit can deliver only important notifications. Furthermore, if the resident is stressed, the notification adjustment unit can reduce the frequency of notifications to alleviate stress. In this way, the home security system can provide a notification method tailored to the resident's emotions and support a comfortable life for the resident.

[0118] The home security system may also include an entertainment unit that estimates the resident's emotions and provides entertainment content based on those emotions. The entertainment unit analyzes the resident's emotions and provides entertainment that matches those emotions. For example, if the resident is relaxed, the entertainment unit can provide relaxing movies or music. If the resident wants to be energized, the entertainment unit can provide upbeat action movies or music. Furthermore, if the resident is stressed, the entertainment unit can provide meditation apps or relaxation music to reduce stress. In this way, the home security system can provide entertainment tailored to the resident's emotions and support a comfortable life for the resident.

[0119] The home security system may also include an appliance control unit that estimates the resident's emotions and adjusts the operation of appliances based on those emotions. The appliance control unit analyzes the resident's emotions and provides appliance operation that matches those emotions. For example, if the resident is relaxed, the appliance control unit can appropriately adjust the television volume. If the resident wants to concentrate, the appliance control unit can temporarily pause the vacuum cleaner. Furthermore, if the resident is stressed, the appliance control unit can automatically activate the massage chair. In this way, the home security system can provide appliance operation tailored to the resident's emotions, supporting a comfortable life for the resident.

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

[0121] Step 1: The collection unit collects information from cameras and microphones installed within the house. For example, it collects video information from cameras and audio information from microphones. The collection unit can aggregate the information via Wi-Fi. Step 2: The analysis unit analyzes the information collected by the collection unit to capture when, who, where, and what residents are doing. For example, it analyzes video information to identify residents' locations and analyzes audio information to understand residents' behavior. The analysis unit can use AI to analyze the information. Step 3: The calling unit issues a voice call when an anomaly is detected by the analysis unit. For example, it may issue a warning message through the microphone. The calling unit can use AI to issue voice calls. Step 4: The reporting unit notifies the security company if an anomaly is detected by the analysis unit. For example, it may notify the security company by phone or email. The reporting unit can use AI to make notifications. Step 5: The control unit operates IoT devices based on the resident's voice commands during normal operation. For example, it operates IoT devices such as lighting, air conditioning, baths, and electrical appliances. The control unit can operate IoT devices using AI.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, calling unit, notification unit, and operation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and aggregates the data using the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to capture the resident's actions. The calling unit makes a voice call using the speaker 40B of the smart device 14, for example. The notification unit notifies the security company via the communication I / F 26 of the data processing unit 12, for example. The operation unit operates the IoT device based on the resident's voice instructions using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, calling unit, notification unit, and operation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and aggregates the data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to capture the resident's actions. The calling unit makes a voice call using, for example, the speaker 240 of the smart glasses 214. The notification unit notifies the security company, for example, via the communication I / F 26 of the data processing unit 12. The operation unit operates IoT devices based on the resident's voice instructions using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, calling unit, notification unit, and operation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and aggregates the data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to capture the resident's actions. The calling unit makes a voice call using, for example, the speaker 240 of the headset terminal 314. The notification unit notifies the security company via, for example, the communication I / F 26 of the data processing unit 12. The operation unit operates the IoT device based on the resident's voice instructions using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, calling unit, notification unit, and operation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and aggregates the data with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to capture the actions of the resident. The calling unit makes a voice call using, for example, the speaker 240 of the robot 414. The notification unit notifies the security company, for example, via the communication I / F 26 of the data processing unit 12. The operation unit operates IoT devices based on the resident's voice instructions using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A collection unit that collects information from cameras and microphones installed inside the house, The analysis unit analyzes the information collected by the aforementioned collection unit to capture when, who, where, and what residents are doing. The calling unit makes an audible call when an abnormality is detected by the analysis unit, The aforementioned analysis unit detects an abnormality and notifies the security company; It includes an operating unit that normally operates IoT devices based on the resident's voice commands. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information is collected via Wi-Fi. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing collected video and audio information, it captures when, who, where, and what residents are doing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned calling section is, If an anomaly is detected, a voice message will be sent via the microphone. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, If an anomaly is detected, an alert will be sent to the security company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned operating unit is IoT devices such as lighting, air conditioning, baths, and electrical appliances are operated based on the resident's voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the residents' emotions and adjust the timing of information collection based on the estimated residents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The types of information collected are dynamically changed based on residents' behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The accuracy of the information collected will be adjusted according to the current activities of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the residents' emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The information collected is filtered based on the geographical location of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The information collected will be supplemented based on residents' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the residents' emotions and adjust the analysis method based on the estimated residents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The level of detail in the information being analyzed is dynamically changed based on the residents' behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The types of information to be analyzed are adjusted according to the current activities of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the residents' emotions and adjusts how the analysis results are displayed based on the estimated emotions of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The information to be analyzed is filtered based on the geographical location of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The information to be analyzed is supplemented based on residents' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned calling section is, The system estimates the residents' emotions and adjusts the content of the appeals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned calling section is, The timing of voice prompts is dynamically changed depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned calling section is, The volume of voice alerts is adjusted according to the severity of the abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned calling section is, The system estimates the residents' emotions and determines the priority of appeals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned calling section is, The content of voice announcements will be adjusted based on the resident's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned calling section is, The content of the voice call will be supplemented based on residents' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, The system estimates the residents' emotions and adjusts the content of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, The timing of notifications is dynamically changed depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The level of detail in the report will be adjusted according to the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, The system estimates the residents' emotions and prioritizes reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, The content of the report will be adjusted based on the resident's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reporting unit, The content of the report will be supplemented with information on the resident's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned operating unit is It estimates the emotions of residents and adjusts the operation of IoT devices based on the estimated emotions of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned operating unit is The timing of IoT device operation is dynamically changed based on the residents' behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned operating unit is The level of detail in the operation of IoT devices is adjusted according to the current activities of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned operating unit is It estimates the emotions of residents and determines the priority of IoT device operations based on the estimated emotions of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned operating unit is The operation of IoT devices is adjusted based on the geographical location information of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned operating unit is The operation of IoT devices is supplemented based on residents' social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A collection unit that collects information from cameras and microphones installed inside the house, The analysis unit analyzes the information collected by the aforementioned collection unit to capture when, who, where, and what residents are doing. The calling unit makes an audible call when an abnormality is detected by the analysis unit, The aforementioned analysis unit detects an abnormality and notifies the security company; It includes an operating unit that normally operates IoT devices based on the resident's voice commands. A system characterized by the following features.

2. The aforementioned collection unit is Information is collected via Wi-Fi. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing collected video and audio information, it captures when, who, where, and what residents are doing. The system according to feature 1.

4. The aforementioned calling section is, If an anomaly is detected, a voice message will be sent via the microphone. The system according to feature 1.

5. The aforementioned reporting unit, If an anomaly is detected, an alert will be sent to the security company. The system according to feature 1.

6. The aforementioned operating unit is IoT devices such as lighting, air conditioning, baths, and electrical appliances are operated based on the resident's voice commands. The system according to feature 1.

7. The aforementioned collection unit is We estimate the residents' emotions and adjust the timing of information collection based on the estimated residents' emotions. The system according to feature 1.

8. The aforementioned collection unit is The types of information collected are dynamically changed based on residents' behavioral patterns. The system according to feature 1.

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