system

Abstract

The system according to this embodiment aims to monitor the usage of electricity, water, and gas within a household and to ensure the safety of residents. [Solution] The system according to the embodiment comprises a monitoring unit, a surveillance unit, a warning unit, a prediction unit, a notification unit, and a recording unit. The monitoring unit monitors the usage of electricity, water, and gas in the home. The surveillance unit monitors the behavior of residents based on the data collected by the monitoring unit. The warning unit issues a warning when it detects an abnormality based on the behavior monitored by the surveillance unit. The prediction unit predicts dangers such as falls based on the warnings issued by the warning unit. The notification unit makes an emergency call based on the danger predicted by the prediction unit. The recording unit records programs based on the hobbies and viewing history of residents monitored by the surveillance unit.

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 the 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 prior art, a system for comprehensively monitoring the usage status of electricity, water, and gas in a household and ensuring the safety of residents is not sufficiently developed and there is room for improvement.

[0005] [[ID=3&]] The system according to the embodiment aims to monitor the usage status of electricity, water, and gas in a household and ensure the safety of residents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a surveillance unit, a warning unit, a prediction unit, a notification unit, and a recording unit. The monitoring unit monitors the usage of electricity, water, and gas within the home. The surveillance unit monitors the behavior of residents based on the data collected by the monitoring unit. The warning unit issues a warning when it detects an abnormality based on the behavior monitored by the surveillance unit. The prediction unit predicts dangers such as falls based on the warnings issued by the warning unit. The notification unit makes an emergency call based on the dangers predicted by the prediction unit. The recording unit records programs based on the hobbies and viewing history of residents monitored by the surveillance unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the usage of electricity, water, and gas within a household and ensure the safety of the residents. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a home monitoring system that monitors the use of electricity, water, and gas within a home and predicts dangers such as falls via a webcam. This home monitoring system monitors the use of electricity, water, and gas within the home in real time and issues a warning if an abnormality is detected. Next, it monitors the behavior of residents via a webcam and predicts dangers such as falls. For example, if an elderly person falls, the AI ​​detects the situation and activates the emergency call system to summon an ambulance. Furthermore, it also has a function that allows the AI ​​to automatically record programs based on the resident's hobbies and viewing history. This allows residents to enjoy their favorite programs without missing them. The AI ​​agent aims to provide safety and comfort within the home and improve the quality of life for residents. For example, the home monitoring system monitors the use of electricity, water, and gas within the home in real time. For example, the home monitoring system monitors the behavior of residents via a webcam. For example, the home monitoring system issues a warning if an abnormality is detected. For example, the home monitoring system predicts dangers such as falls. For example, the home monitoring system makes an emergency call. For example, a home monitoring system records programs based on the resident's hobbies and viewing history. This allows the home monitoring system to monitor the usage of electricity, water, and gas within the home, monitor the resident's behavior to detect abnormalities, predict dangers such as falls and make emergency calls, and record programs tailored to the resident's interests.

[0029] The in-home monitoring system according to this embodiment comprises a monitoring unit, a surveillance unit, a warning unit, a prediction unit, a notification unit, and a recording unit. The monitoring unit monitors the usage of electricity, water, and gas within the home. The monitoring unit can, for example, monitor the usage of electricity, water, and gas within the home in real time. For example, the monitoring unit can use an electricity meter to monitor electricity usage. The monitoring unit can also use a water meter to monitor water usage. The monitoring unit can also use a gas meter to monitor gas usage. The surveillance unit monitors the behavior of residents based on the data collected by the monitoring unit. For example, the surveillance unit can monitor the behavior of residents through a webcam. The surveillance unit can, for example, use a webcam to monitor the movement of residents in real time. The surveillance unit can also use a webcam to record the behavior of residents. The surveillance unit can also use a webcam to analyze the behavior of residents. The warning unit issues a warning when it detects an abnormality based on the behavior monitored by the surveillance unit. The warning unit can, for example, issue an audible warning if it detects an anomaly. It can also issue a visual warning if it detects an anomaly. Furthermore, it can issue a vibration warning if it detects an anomaly. The prediction unit predicts dangers such as falls based on the warnings issued by the warning unit. For example, the prediction unit can record the resident's movement speed and posture in detail to assess the risk of falling. It can also assess the risk of falling by referring to the resident's past behavior patterns. Furthermore, it can assess the risk of falling while considering the resident's health condition. The notification unit makes an emergency call based on the dangers predicted by the prediction unit. For example, the notification unit can use a telephone to make an emergency call. It can also use the internet to make an emergency call. Furthermore, it can use wireless communication to make an emergency call. The recording unit records programs based on the resident's hobbies and viewing history monitored by the monitoring unit.The recording unit can, for example, determine recording priorities by referring to the resident's viewing history. It can also customize recording content based on the resident's hobbies and interests. Furthermore, it can estimate the resident's emotions and adjust recording timing based on those emotions. As a result, the home monitoring system according to this embodiment can monitor the usage of electricity, water, and gas within the home, monitor the resident's behavior to detect abnormalities, predict dangers such as falls and issue emergency alerts, and record programs tailored to the resident's interests.

[0030] The monitoring unit monitors the usage of electricity, water, and gas within the home. Specifically, it uses electricity meters, water meters, and gas meters to monitor the usage of various energy sources in real time. The electricity meter records the power consumption of each electrical appliance in the home in detail, allowing for the analysis of usage patterns. This helps prevent wasted electricity and enables efficient energy management. The water meter measures the amount of water used in the home, enabling early detection of leaks and abnormal usage patterns. The gas meter monitors gas usage, ensuring safety by detecting gas leaks and abnormal usage. This data is transmitted to a central database and can be linked with other systems for comprehensive energy management. Furthermore, the monitoring unit stores the collected data on a cloud server, making it accessible to the analysis and prediction units. This allows for centralized management of energy usage within the home, enabling efficient energy utilization.

[0031] The monitoring unit monitors residents' behavior based on data collected by the monitoring unit. Specifically, it can use webcams to monitor and record residents' movements in real time. Webcams are installed in each room of the house to record residents' movements in detail. This allows for an understanding of residents' behavior patterns and early detection of abnormal behavior. The monitoring unit analyzes the collected video data to analyze residents' behavior. For example, it can detect abnormal behavior such as residents falling or remaining motionless for extended periods. The monitoring unit also records residents' behavior for later review. This ensures residents' safety and allows for a quick response if abnormal behavior occurs. Furthermore, the monitoring unit stores the collected data on a cloud server and can integrate with other systems for comprehensive monitoring. This improves safety within the home.

[0032] The warning unit issues a warning when it detects an anomaly based on the behavior monitored by the monitoring unit. Specifically, it can issue an audible warning when an anomaly is detected. For example, if a resident falls or remains motionless for a long period of time, it can issue an audible warning to alert the resident. The warning unit can also issue a visual warning. For example, it can alert a resident to an anomaly by flashing lights in the home. Furthermore, the warning unit can issue a warning through vibration. For example, it can alert a resident to an anomaly by vibrating a device they are wearing. This allows the warning unit to quickly detect anomalies and issue an appropriate warning to the resident. In addition, the warning unit can store the collected data on a cloud server and provide comprehensive warnings in conjunction with other systems. This can improve safety within the home.

[0033] The prediction unit predicts hazards such as falls based on warnings issued by the warning unit. Specifically, it can record the resident's movement speed and posture in detail and assess the risk of falling. For example, if a resident suddenly starts moving or loses their balance, the risk of falling increases, and the prediction unit can detect this and issue a warning. The prediction unit can also assess the risk of falling by referring to the resident's past behavior patterns. For example, a resident who has fallen in the past has a higher risk of falling again, and the prediction unit can take this into account when assessing the risk. Furthermore, the prediction unit can also assess the risk of falling by considering the resident's health condition. For example, a resident whose health condition is deteriorating has a higher risk of falling, and the prediction unit can take this into account when assessing the risk. As a result, the prediction unit can quickly predict hazards such as falls and issue appropriate warnings to residents. In addition, the prediction unit can store the collected data on a cloud server and perform comprehensive predictions in conjunction with other systems. This can improve safety within the home.

[0034] The notification unit makes emergency calls based on the dangers predicted by the prediction unit. Specifically, it can use the telephone to make emergency calls. For example, if a resident falls or remains motionless for a long period of time, an emergency call can be made via telephone for a quick response. The notification unit can also make emergency calls using the internet. For example, an emergency call can be made via the internet for a quick response. Furthermore, the notification unit can also make emergency calls using wireless communication. For example, an emergency call can be made via wireless communication for a quick response. This allows the notification unit to quickly make emergency calls based on predicted dangers and ensure the safety of residents. In addition, the notification unit can store collected data on a cloud server and coordinate with other systems to provide comprehensive notifications. This can improve safety within the home.

[0035] The recording unit records programs based on the residents' hobbies and viewing history, which are monitored by the monitoring unit. Specifically, it can determine recording priorities by referring to the residents' viewing history. For example, based on the history of programs the resident has watched in the past, it can prioritize recording programs of similar genres and content. The recording unit can also customize the content of recordings based on the residents' hobbies and interests. For example, if a resident is interested in a particular sport or movie, it can prioritize recording programs of that genre. Furthermore, the recording unit can estimate the resident's emotions and adjust the timing of recordings based on those emotions. For example, by starting recording during times when the resident is relaxed, the timing of viewing can be optimized. In this way, the recording unit can record programs tailored to the resident's hobbies and interests, improving the viewing experience. In addition, the recording unit can store the collected data on a cloud server and perform comprehensive recording management in conjunction with other systems. This can improve the in-home entertainment experience.

[0036] The monitoring unit can monitor the usage of electricity, water, and gas within a household in real time. For example, the monitoring unit can monitor the electricity usage within a household in real time. For example, the monitoring unit can monitor the water usage within a household in real time. For example, the monitoring unit can monitor the gas usage within a household in real time. By monitoring the electricity, water, and gas usage within a household in real time, abnormalities can be detected early. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can use AI to analyze electricity meter data in order to monitor the electricity usage within a household in real time.

[0037] The monitoring unit can monitor the residents' actions via webcams. The monitoring unit can, for example, use webcams to monitor residents' movements in real time. The monitoring unit can, for example, use webcams to record residents' actions. The monitoring unit can, for example, use webcams to analyze residents' actions. This allows for early detection of dangers such as falls by monitoring residents' actions via webcams. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by webcams into a generating AI and have the generating AI perform monitoring of residents' actions.

[0038] The warning unit can issue a warning when it detects an abnormality. For example, the warning unit can issue a voice warning when it detects an abnormality. For example, the warning unit can issue a visual warning when it detects an abnormality. For example, the warning unit can issue a vibration warning when it detects an abnormality. This allows residents to be notified of abnormalities early by issuing a warning when an abnormality is detected. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a voice warning using AI when it detects an abnormality.

[0039] The prediction unit can predict dangers such as falls. For example, the prediction unit can record the resident's movement speed and posture in detail to assess the risk of falling. For example, the prediction unit can assess the risk of falling by referring to the resident's past behavior patterns. For example, the prediction unit can assess the risk of falling by considering the resident's health condition. This ensures the safety of residents by predicting dangers such as falls. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on the resident's movement speed and posture into a generating AI and have the generating AI perform an assessment of the risk of falling.

[0040] The notification unit can make emergency calls. The notification unit can use a telephone, for example, to make an emergency call. The notification unit can use the internet, for example, to make an emergency call. The notification unit can use wireless communication, for example, to make an emergency call. This allows for the rapid dispatch of an ambulance by making an emergency call. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the content of the emergency call into a generating AI and have the generating AI execute the call.

[0041] The recording unit can record programs based on the resident's hobbies and viewing history. The recording unit can, for example, determine recording priorities by referring to the resident's viewing history. The recording unit can, for example, customize the content of recordings based on the resident's hobbies and interests. The recording unit can, for example, estimate the resident's emotions and adjust the timing of recordings based on the estimated emotions. This allows residents to enjoy their favorite programs without missing them by recording programs based on their hobbies and viewing history. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the resident's viewing history into a generating AI and have the generating AI determine the recording priorities.

[0042] The monitoring unit can analyze past usage history and predict abnormal usage patterns in advance, issuing warnings. For example, the monitoring unit can analyze from past usage history the tendency for abnormal usage to occur during specific time periods. For example, the monitoring unit can detect abnormal usage patterns based on usage history and issue warnings in advance. For example, the monitoring unit can have an AI learn from usage history and predict abnormal usage patterns in real time. This allows the monitoring unit to predict abnormal usage patterns in advance and issue warnings by analyzing past usage history. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past usage history data into a generating AI and have the generating AI perform predictions of abnormal usage patterns.

[0043] The monitoring unit can optimize energy efficiency by recording the power consumption and usage time of each device in detail when monitoring usage. For example, the monitoring unit can optimize energy efficiency by recording the power consumption of each device in real time. For example, the monitoring unit can reduce unnecessary energy consumption by recording the usage time of each device in detail. For example, the monitoring unit can have AI analyze the power consumption and usage time of each device and propose the optimal energy efficiency. In this way, energy efficiency can be optimized by recording the power consumption and usage time of each device in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the power consumption data of each device into a generating AI and have the generating AI perform the optimization of energy efficiency.

[0044] The monitoring unit can filter usage data based on the residents' daily routines and detect abnormalities during monitoring. For example, the monitoring unit can learn the residents' daily routines and filter out abnormal usage data. For example, the monitoring unit can detect abnormal usage data in real time based on the residents' daily routines. For example, the monitoring unit can predict abnormal usage data in advance by considering the residents' daily routines. This allows for early detection of abnormalities by filtering usage data based on the residents' daily routines. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the residents' daily routine data into a generating AI and have the generating AI perform the filtering of usage data.

[0045] The monitoring unit can analyze usage patterns while considering the residents' health status during monitoring and detect abnormalities. For example, the monitoring unit can monitor the residents' health status and detect abnormal usage patterns. For example, the monitoring unit can analyze abnormal usage patterns in real time while considering the residents' health status. For example, the monitoring unit can use AI to learn the residents' health status and predict abnormal usage patterns in advance. This allows for early detection of abnormalities by analyzing usage patterns while considering the residents' health status. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input residents' health status data into a generating AI and have the generating AI perform the usage pattern analysis.

[0046] The monitoring unit can learn residents' behavior patterns during monitoring and predict abnormal behavior in advance, issuing warnings. For example, the monitoring unit can learn residents' behavior patterns and predict abnormal behavior in advance. For example, the monitoring unit can use AI to analyze residents' behavior patterns and detect abnormal behavior in real time. For example, the monitoring unit can warn about abnormal behavior in advance based on residents' behavior patterns. In this way, by learning residents' behavior patterns, it can predict abnormal behavior in advance and issue warnings. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input resident behavior pattern data into a generating AI and have the generating AI perform predictions of abnormal behavior.

[0047] The monitoring unit can record the resident's movement speed and posture in detail during monitoring and assess the risk of falls. For example, the monitoring unit can record the resident's movement speed in real time and assess the risk of falls. For example, the monitoring unit can record the resident's posture in detail and assess the risk of falls. For example, the monitoring unit can use AI to analyze the resident's movement speed and posture and predict the risk of falls in advance. This allows the risk of falls to be assessed by recording the resident's movement speed and posture in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the resident's movement speed and posture data into a generating AI and have the generating AI perform a fall risk assessment.

[0048] The monitoring unit can adjust the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can expand the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can narrow the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can have an AI analyze the residents' living environment and propose the optimal monitoring range. This makes optimal monitoring possible by adjusting the monitoring range while taking into account the residents' living environment. Some or all of the above processing in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input residents' living environment data into a generating AI and have the generating AI perform the adjustment of the monitoring range.

[0049] The monitoring unit can adjust the accuracy of monitoring while taking into account the health status of the residents. For example, the monitoring unit can monitor the health status of residents and adjust the accuracy of monitoring. For example, the monitoring unit can adjust the accuracy of monitoring in real time, taking into account the health status of residents. For example, the monitoring unit can have an AI learn the health status of residents and propose the optimal accuracy of monitoring. This allows for early detection of abnormalities by adjusting the accuracy of monitoring while taking into account the health status of residents. Some or all of the above processes in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input resident health status data into a generating AI and have the generating AI perform the adjustment of the accuracy of monitoring.

[0050] The warning unit can select different warning methods depending on the type of anomaly when an alarm is issued. For example, the warning unit can select an audio warning depending on the type of anomaly. For example, the warning unit can select a visual warning depending on the type of anomaly. For example, the warning unit can select a vibration warning depending on the type of anomaly. This allows the unit to quickly notify residents of an anomaly by selecting the appropriate warning method according to the type of anomaly. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can have a generating AI perform the selection of a warning method according to the type of anomaly.

[0051] The warning unit can determine the priority of warnings by referring to past warning history when an warning is issued. For example, the warning unit can determine the priority of warnings by referring to past warning history. For example, the warning unit can have AI analyze past warning history and propose the optimal warning priority. For example, the warning unit can adjust the warning priority in real time based on past warning history. This allows for the appropriate determination of warning priority by referring to past warning history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning history data into a generating AI and have the generating AI perform the determination of warning priority.

[0052] The warning unit can adjust the frequency of warnings when a warning is issued, taking into account the resident's daily rhythm. For example, the warning unit can increase the frequency of warnings, taking into account the resident's daily rhythm. For example, the warning unit can decrease the frequency of warnings, taking into account the resident's daily rhythm. For example, the warning unit can have an AI analyze the resident's daily rhythm and suggest the optimal warning frequency. By adjusting the warning frequency while taking into account the resident's daily rhythm, warnings can be provided at an appropriate frequency. Some or all of the above-described processes in the warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input the resident's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the warning frequency.

[0053] The warning unit can adjust the content of a warning when it issues one, taking into account the resident's health condition. For example, the warning unit can monitor the resident's health condition and adjust the content of the warning. For example, the warning unit can adjust the content of the warning in real time, taking into account the resident's health condition. For example, the warning unit can have an AI learn the resident's health condition and suggest the optimal warning content. This allows the warning unit to provide appropriate warnings by adjusting the content of the warning, taking into account the resident's health condition. Some or all of the above-described processes in the warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input the resident's health condition data into a generating AI and have the generating AI perform the adjustment of the warning content.

[0054] The prediction unit can assess the risk of falling by referring to the resident's past behavior patterns during prediction. For example, the prediction unit can assess the risk of falling by referring to the resident's past behavior patterns. For example, the prediction unit can have an AI analyze the resident's past behavior patterns and assess the risk of falling in real time. For example, the prediction unit can predict the risk of falling in advance based on the resident's past behavior patterns. This allows for an appropriate assessment of the risk of falling by referring to the resident's past behavior patterns. Some or all of the above processing in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input the resident's past behavior pattern data into a generating AI and have the generating AI perform the assessment of the risk of falling.

[0055] The prediction unit can record the resident's movement speed and posture in detail during prediction and assess the risk of falling. For example, the prediction unit can record the resident's movement speed in real time and assess the risk of falling. For example, the prediction unit can record the resident's posture in detail and assess the risk of falling. For example, the prediction unit can use AI to analyze the resident's movement speed and posture and predict the risk of falling in advance. This allows for an appropriate assessment of the risk of falling by recording the resident's movement speed and posture in detail. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the resident's movement speed and posture data into a generating AI and have the generating AI perform the assessment of the risk of falling.

[0056] The prediction unit can adjust the range of risk prediction by considering the residents' living environment during the prediction process. For example, the prediction unit can broaden the range of risk prediction by considering the residents' living environment. For example, the prediction unit can narrow the range of risk prediction by considering the residents' living environment. For example, the prediction unit can have an AI analyze the residents' living environment and propose the optimal range of risk prediction. By adjusting the range of risk prediction by considering the residents' living environment, risks can be predicted within an appropriate range. Some or all of the above-described processes in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input residents' living environment data into a generating AI and have the generating AI perform the adjustment of the risk prediction range.

[0057] The prediction unit can adjust the accuracy of risk prediction by considering the health status of residents during prediction. For example, the prediction unit can monitor the health status of residents and adjust the accuracy of risk prediction. For example, the prediction unit can adjust the accuracy of risk prediction in real time by considering the health status of residents. For example, the prediction unit can have an AI learn the health status of residents and propose the optimal accuracy of risk prediction. By adjusting the accuracy of risk prediction by considering the health status of residents, risks can be predicted with appropriate accuracy. Some or all of the above processing in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input resident health status data into a generating AI and have the generating AI perform the adjustment of the accuracy of risk prediction.

[0058] The notification unit can select different notification methods depending on the type of anomaly when an alert is issued. For example, the notification unit can select an audio alert depending on the type of anomaly. For example, the notification unit can select a visual alert depending on the type of anomaly. For example, the notification unit can select a vibration alert depending on the type of anomaly. This allows for rapid notification of anomalies by selecting the appropriate notification method according to the type of anomaly. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can have a generating AI perform the selection of a notification method according to the type of anomaly.

[0059] The reporting unit can determine the priority of a report by referring to past reporting history when a report is made. For example, the reporting unit can determine the priority of a report by referring to past reporting history. For example, the reporting unit can have AI analyze past reporting history and propose the optimal priority of reports. For example, the reporting unit can adjust the priority of reports in real time based on past reporting history. This allows for the appropriate determination of the priority of reports by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the determination of the priority of reports.

[0060] The notification unit can adjust the frequency of notifications when a notification is made, taking into account the resident's daily rhythm. For example, the notification unit can increase the frequency of notifications, taking into account the resident's daily rhythm. For example, the notification unit can decrease the frequency of notifications, taking into account the resident's daily rhythm. For example, the notification unit can have an AI analyze the resident's daily rhythm and suggest the optimal frequency of notifications. This allows notifications to be provided at an appropriate frequency by adjusting the frequency of notifications, taking into account the resident's daily rhythm. Some or all of the above processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the resident's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the notification frequency.

[0061] The notification unit can adjust the content of a notification considering the resident's health condition at the time of notification. For example, the notification unit can monitor the resident's health condition and adjust the content of the notification. For example, the notification unit can adjust the content of the notification in real time considering the resident's health condition. For example, the notification unit can have an AI learn the resident's health condition and suggest the optimal content of the notification. This allows for the provision of appropriate notifications by adjusting the content of the notification considering the resident's health condition. Some or all of the above-described processes in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the resident's health condition data into a generating AI and have the generating AI perform the adjustment of the content of the notification.

[0062] The recording unit can determine recording priorities by referring to the resident's viewing history during recording. For example, the recording unit can refer to the resident's viewing history to determine recording priorities. For example, the recording unit can have an AI analyze the resident's viewing history and suggest the optimal recording priorities. For example, the recording unit can adjust recording priorities in real time based on the resident's viewing history. This allows for the appropriate determination of recording priorities by referring to the resident's viewing history. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the resident's viewing history data into a generating AI and have the generating AI perform the determination of recording priorities.

[0063] The recording unit can customize the content of recordings based on the resident's hobbies and interests during recording. For example, the recording unit can refer to the resident's hobbies and customize the content of the recordings. For example, the recording unit can use AI to analyze the resident's interests and suggest the most suitable content for recording. For example, the recording unit can customize the content of recordings in real time based on the resident's hobbies and interests. This allows for the provision of appropriate recordings by customizing the content of recordings based on the resident's hobbies and interests. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data on the resident's hobbies and interests into a generating AI and have the generating AI perform the customization of the recording content.

[0064] The recording unit can adjust the recording frequency while taking into account the residents' daily routines. For example, the recording unit can increase the recording frequency while taking into account the residents' daily routines. For example, the recording unit can decrease the recording frequency while taking into account the residents' daily routines. For example, the recording unit can have an AI analyze the residents' daily routines and suggest the optimal recording frequency. This allows the recording unit to provide recordings at an appropriate frequency by adjusting the recording frequency while taking into account the residents' daily routines. Some or all of the above processing in the recording unit may be performed using an AI, for example, or without an AI. For example, the recording unit can input the residents' daily routine data into a generating AI and have the generating AI perform the adjustment of the recording frequency.

[0065] The recording unit can adjust the content of the recording while taking into account the health status of the residents. For example, the recording unit can monitor the health status of the residents and adjust the content of the recording. For example, the recording unit can adjust the content of the recording in real time, taking into account the health status of the residents. For example, the recording unit can have an AI learn the health status of the residents and suggest the optimal content of the recording. In this way, appropriate recordings can be provided by adjusting the content of the recording while taking into account the health status of the residents. Some or all of the above processing in the recording unit may be performed using an AI, for example, or without an AI. For example, the recording unit can input the health status data of the residents into a generating AI and have the generating AI perform the adjustment of the content of the recording.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The monitoring unit can monitor not only the usage of electricity, water, and gas within the home, but also the temperature and humidity. For example, the monitoring unit can issue a warning if the temperature inside the home is abnormally high. It can also issue a warning if the humidity inside the home is abnormally low. Furthermore, the monitoring unit can collect temperature and humidity data and make suggestions for maintaining a comfortable environment. In this way, monitoring the temperature and humidity inside the home can improve the comfort of the residents.

[0068] The monitoring unit can not only monitor residents' actions via webcams but also monitor their voices. For example, it can analyze the tone and volume of residents' voices to detect abnormalities. It can also monitor changes in residents' voices in real time to detect abnormalities early. Furthermore, it can collect data on residents' voices to predict changes in their health. By monitoring residents' voices, it is possible to detect abnormalities early and ensure the safety of residents.

[0069] The warning unit not only issues a warning when it detects an abnormality, but can also emit different warning sounds depending on the type of abnormality. For example, the warning unit can emit a high-pitched warning sound when it detects a fire. It can also emit a low-pitched warning sound when it detects a water leak. Furthermore, it can emit a medium-pitched warning sound when it detects a gas leak. This allows the unit to quickly notify residents of abnormalities by emitting different warning sounds depending on the type of abnormality.

[0070] The prediction unit can not only predict dangers such as falls, but also predict dangers while considering the residents' health status. For example, the prediction unit can monitor residents' heart rate and blood pressure and detect abnormalities. It can also collect data on residents' health status and predict changes in their health status. Furthermore, the prediction unit can assess the risk of falls while considering the residents' health status. In this way, by predicting dangers while considering the residents' health status, the safety of residents can be ensured.

[0071] The notification unit can not only make emergency calls, but also customize the content of the calls. For example, if the notification unit detects a fire, it can report detailed information about the fire. It can also report the location and extent of a water leak if it detects one. Furthermore, if it detects a gas leak, it can report the type and amount of the leak. This allows for a quick and appropriate response by customizing the content of the calls.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The monitoring unit monitors the usage of electricity, water, and gas within the home. For example, it can use electricity meters, water meters, and gas meters to monitor their usage in real time. Step 2: The monitoring unit monitors residents' behavior based on the data collected by the monitoring unit. For example, it can use webcams to monitor residents' movements in real time and also record and analyze their behavior. Step 3: The warning unit issues a warning if it detects an anomaly based on the behavior monitored by the monitoring unit. For example, it can issue warnings via sound, visual, or vibration. Step 4: The prediction unit predicts risks such as falls based on warnings issued by the warning unit. For example, it can assess the risk of falls by considering the resident's movement speed and posture, past behavioral patterns, and health condition. Step 5: The notification unit makes an emergency call based on the hazard predicted by the prediction unit. For example, the emergency call can be made using telephone, the internet, or wireless communication. Step 6: The recording unit records programs based on the residents' hobbies and viewing history monitored by the monitoring unit. For example, it can determine recording priorities by referring to the residents' viewing history and customize the recording content based on their hobbies and interests.

[0074] (Example of form 2) An embodiment of the present invention provides a home monitoring system that monitors the use of electricity, water, and gas within a home and predicts dangers such as falls via a webcam. This home monitoring system monitors the use of electricity, water, and gas within the home in real time and issues a warning if an abnormality is detected. Next, it monitors the behavior of residents via a webcam and predicts dangers such as falls. For example, if an elderly person falls, the AI ​​detects the situation and activates the emergency call system to summon an ambulance. Furthermore, it also has a function that allows the AI ​​to automatically record programs based on the resident's hobbies and viewing history. This allows residents to enjoy their favorite programs without missing them. The AI ​​agent aims to provide safety and comfort within the home and improve the quality of life for residents. For example, the home monitoring system monitors the use of electricity, water, and gas within the home in real time. For example, the home monitoring system monitors the behavior of residents via a webcam. For example, the home monitoring system issues a warning if an abnormality is detected. For example, the home monitoring system predicts dangers such as falls. For example, the home monitoring system makes an emergency call. For example, a home monitoring system records programs based on the resident's hobbies and viewing history. This allows the home monitoring system to monitor the usage of electricity, water, and gas within the home, monitor the resident's behavior to detect abnormalities, predict dangers such as falls and make emergency calls, and record programs tailored to the resident's interests.

[0075] The in-home monitoring system according to this embodiment comprises a monitoring unit, a surveillance unit, a warning unit, a prediction unit, a notification unit, and a recording unit. The monitoring unit monitors the usage of electricity, water, and gas within the home. The monitoring unit can, for example, monitor the usage of electricity, water, and gas within the home in real time. For example, the monitoring unit can use an electricity meter to monitor electricity usage. The monitoring unit can also use a water meter to monitor water usage. The monitoring unit can also use a gas meter to monitor gas usage. The surveillance unit monitors the behavior of residents based on the data collected by the monitoring unit. For example, the surveillance unit can monitor the behavior of residents through a webcam. The surveillance unit can, for example, use a webcam to monitor the movement of residents in real time. The surveillance unit can also use a webcam to record the behavior of residents. The surveillance unit can also use a webcam to analyze the behavior of residents. The warning unit issues a warning when it detects an abnormality based on the behavior monitored by the surveillance unit. The warning unit can, for example, issue an audible warning if it detects an anomaly. It can also issue a visual warning if it detects an anomaly. Furthermore, it can issue a vibration warning if it detects an anomaly. The prediction unit predicts dangers such as falls based on the warnings issued by the warning unit. For example, the prediction unit can record the resident's movement speed and posture in detail to assess the risk of falling. It can also assess the risk of falling by referring to the resident's past behavior patterns. Furthermore, it can assess the risk of falling while considering the resident's health condition. The notification unit makes an emergency call based on the dangers predicted by the prediction unit. For example, the notification unit can use a telephone to make an emergency call. It can also use the internet to make an emergency call. Furthermore, it can use wireless communication to make an emergency call. The recording unit records programs based on the resident's hobbies and viewing history monitored by the monitoring unit.The recording unit can, for example, determine recording priorities by referring to the resident's viewing history. It can also customize recording content based on the resident's hobbies and interests. Furthermore, it can estimate the resident's emotions and adjust recording timing based on those emotions. As a result, the home monitoring system according to this embodiment can monitor the usage of electricity, water, and gas within the home, monitor the resident's behavior to detect abnormalities, predict dangers such as falls and issue emergency alerts, and record programs tailored to the resident's interests.

[0076] The monitoring unit monitors the usage of electricity, water, and gas within the home. Specifically, it uses electricity meters, water meters, and gas meters to monitor the usage of various energy sources in real time. The electricity meter records the power consumption of each electrical appliance in the home in detail, allowing for the analysis of usage patterns. This helps prevent wasted electricity and enables efficient energy management. The water meter measures the amount of water used in the home, enabling early detection of leaks and abnormal usage patterns. The gas meter monitors gas usage, ensuring safety by detecting gas leaks and abnormal usage. This data is transmitted to a central database and can be linked with other systems for comprehensive energy management. Furthermore, the monitoring unit stores the collected data on a cloud server, making it accessible to the analysis and prediction units. This allows for centralized management of energy usage within the home, enabling efficient energy utilization.

[0077] The monitoring unit monitors residents' behavior based on data collected by the monitoring unit. Specifically, it can use webcams to monitor and record residents' movements in real time. Webcams are installed in each room of the house to record residents' movements in detail. This allows for an understanding of residents' behavior patterns and early detection of abnormal behavior. The monitoring unit analyzes the collected video data to analyze residents' behavior. For example, it can detect abnormal behavior such as residents falling or remaining motionless for extended periods. The monitoring unit also records residents' behavior for later review. This ensures residents' safety and allows for a quick response if abnormal behavior occurs. Furthermore, the monitoring unit stores the collected data on a cloud server and can integrate with other systems for comprehensive monitoring. This improves safety within the home.

[0078] The warning unit issues a warning when it detects an anomaly based on the behavior monitored by the monitoring unit. Specifically, it can issue an audible warning when an anomaly is detected. For example, if a resident falls or remains motionless for a long period of time, it can issue an audible warning to alert the resident. The warning unit can also issue a visual warning. For example, it can alert a resident to an anomaly by flashing lights in the home. Furthermore, the warning unit can issue a warning through vibration. For example, it can alert a resident to an anomaly by vibrating a device they are wearing. This allows the warning unit to quickly detect anomalies and issue an appropriate warning to the resident. In addition, the warning unit can store the collected data on a cloud server and provide comprehensive warnings in conjunction with other systems. This can improve safety within the home.

[0079] The prediction unit predicts hazards such as falls based on warnings issued by the warning unit. Specifically, it can record the resident's movement speed and posture in detail and assess the risk of falling. For example, if a resident suddenly starts moving or loses their balance, the risk of falling increases, and the prediction unit can detect this and issue a warning. The prediction unit can also assess the risk of falling by referring to the resident's past behavior patterns. For example, a resident who has fallen in the past has a higher risk of falling again, and the prediction unit can take this into account when assessing the risk. Furthermore, the prediction unit can also assess the risk of falling by considering the resident's health condition. For example, a resident whose health condition is deteriorating has a higher risk of falling, and the prediction unit can take this into account when assessing the risk. As a result, the prediction unit can quickly predict hazards such as falls and issue appropriate warnings to residents. In addition, the prediction unit can store the collected data on a cloud server and perform comprehensive predictions in conjunction with other systems. This can improve safety within the home.

[0080] The notification unit makes emergency calls based on the dangers predicted by the prediction unit. Specifically, it can use the telephone to make emergency calls. For example, if a resident falls or remains motionless for a long period of time, an emergency call can be made via telephone for a quick response. The notification unit can also make emergency calls using the internet. For example, an emergency call can be made via the internet for a quick response. Furthermore, the notification unit can also make emergency calls using wireless communication. For example, an emergency call can be made via wireless communication for a quick response. This allows the notification unit to quickly make emergency calls based on predicted dangers and ensure the safety of residents. In addition, the notification unit can store collected data on a cloud server and coordinate with other systems to provide comprehensive notifications. This can improve safety within the home.

[0081] The recording unit records programs based on the residents' hobbies and viewing history, which are monitored by the monitoring unit. Specifically, it can determine recording priorities by referring to the residents' viewing history. For example, based on the history of programs the resident has watched in the past, it can prioritize recording programs of similar genres and content. The recording unit can also customize the content of recordings based on the residents' hobbies and interests. For example, if a resident is interested in a particular sport or movie, it can prioritize recording programs of that genre. Furthermore, the recording unit can estimate the resident's emotions and adjust the timing of recordings based on those emotions. For example, by starting recording during times when the resident is relaxed, the timing of viewing can be optimized. In this way, the recording unit can record programs tailored to the resident's hobbies and interests, improving the viewing experience. In addition, the recording unit can store the collected data on a cloud server and perform comprehensive recording management in conjunction with other systems. This can improve the in-home entertainment experience.

[0082] The monitoring unit can monitor the usage of electricity, water, and gas within a household in real time. For example, the monitoring unit can monitor the electricity usage within a household in real time. For example, the monitoring unit can monitor the water usage within a household in real time. For example, the monitoring unit can monitor the gas usage within a household in real time. By monitoring the electricity, water, and gas usage within a household in real time, abnormalities can be detected early. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can use AI to analyze electricity meter data in order to monitor the electricity usage within a household in real time.

[0083] The monitoring unit can monitor the residents' actions via webcams. The monitoring unit can, for example, use webcams to monitor residents' movements in real time. The monitoring unit can, for example, use webcams to record residents' actions. The monitoring unit can, for example, use webcams to analyze residents' actions. This allows for early detection of dangers such as falls by monitoring residents' actions via webcams. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by webcams into a generating AI and have the generating AI perform monitoring of residents' actions.

[0084] The warning unit can issue a warning when it detects an abnormality. For example, the warning unit can issue a voice warning when it detects an abnormality. For example, the warning unit can issue a visual warning when it detects an abnormality. For example, the warning unit can issue a vibration warning when it detects an abnormality. This allows residents to be notified of abnormalities early by issuing a warning when an abnormality is detected. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a voice warning using AI when it detects an abnormality.

[0085] The prediction unit can predict dangers such as falls. For example, the prediction unit can record the resident's movement speed and posture in detail to assess the risk of falling. For example, the prediction unit can assess the risk of falling by referring to the resident's past behavior patterns. For example, the prediction unit can assess the risk of falling by considering the resident's health condition. This ensures the safety of residents by predicting dangers such as falls. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on the resident's movement speed and posture into a generating AI and have the generating AI perform an assessment of the risk of falling.

[0086] The notification unit can make emergency calls. The notification unit can use a telephone, for example, to make an emergency call. The notification unit can use the internet, for example, to make an emergency call. The notification unit can use wireless communication, for example, to make an emergency call. This allows for the rapid dispatch of an ambulance by making an emergency call. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the content of the emergency call into a generating AI and have the generating AI execute the call.

[0087] The recording unit can record programs based on the resident's hobbies and viewing history. The recording unit can, for example, determine recording priorities by referring to the resident's viewing history. The recording unit can, for example, customize the content of recordings based on the resident's hobbies and interests. The recording unit can, for example, estimate the resident's emotions and adjust the timing of recordings based on the estimated emotions. This allows residents to enjoy their favorite programs without missing them by recording programs based on their hobbies and viewing history. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the resident's viewing history into a generating AI and have the generating AI determine the recording priorities.

[0088] The monitoring unit can estimate the residents' emotions and adjust the monitoring frequency of electricity, water, and gas usage based on the estimated emotions. For example, if a resident is stressed, the monitoring unit can increase the monitoring frequency to detect abnormalities early. For example, if a resident is relaxed, the monitoring unit can decrease the monitoring frequency to reduce the system load. For example, if a resident is anxious, the monitoring unit can appropriately adjust the monitoring frequency to provide a sense of security. By adjusting the monitoring frequency based on the residents' emotions, abnormalities can be detected early and the system load can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input resident emotion data into the generative AI and have the generative AI adjust the monitoring frequency.

[0089] The monitoring unit can analyze past usage history and predict abnormal usage patterns in advance, issuing warnings. For example, the monitoring unit can analyze from past usage history the tendency for abnormal usage to occur during specific time periods. For example, the monitoring unit can detect abnormal usage patterns based on usage history and issue warnings in advance. For example, the monitoring unit can have an AI learn from usage history and predict abnormal usage patterns in real time. This allows the monitoring unit to predict abnormal usage patterns in advance and issue warnings by analyzing past usage history. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past usage history data into a generating AI and have the generating AI perform predictions of abnormal usage patterns.

[0090] The monitoring unit can optimize energy efficiency by recording the power consumption and usage time of each device in detail when monitoring usage. For example, the monitoring unit can optimize energy efficiency by recording the power consumption of each device in real time. For example, the monitoring unit can reduce unnecessary energy consumption by recording the usage time of each device in detail. For example, the monitoring unit can have AI analyze the power consumption and usage time of each device and propose the optimal energy efficiency. In this way, energy efficiency can be optimized by recording the power consumption and usage time of each device in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the power consumption data of each device into a generating AI and have the generating AI perform the optimization of energy efficiency.

[0091] The monitoring unit can estimate the resident's emotions and determine the priority of devices to monitor based on the estimated emotions. For example, if the resident is stressed, the monitoring unit can prioritize monitoring important devices. For example, if the resident is relaxed, the monitoring unit can perform overall monitoring. For example, if the resident is anxious, the monitoring unit can intensify monitoring of specific devices. This allows for prioritization of monitoring important devices by determining the priority of devices to monitor based on the resident's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input resident emotion data into a generative AI and have the generative AI determine the priority of devices to monitor.

[0092] The monitoring unit can filter usage data based on the residents' daily routines and detect abnormalities during monitoring. For example, the monitoring unit can learn the residents' daily routines and filter out abnormal usage data. For example, the monitoring unit can detect abnormal usage data in real time based on the residents' daily routines. For example, the monitoring unit can predict abnormal usage data in advance by considering the residents' daily routines. This allows for early detection of abnormalities by filtering usage data based on the residents' daily routines. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the residents' daily routine data into a generating AI and have the generating AI perform the filtering of usage data.

[0093] The monitoring unit can analyze usage patterns while considering the residents' health status during monitoring and detect abnormalities. For example, the monitoring unit can monitor the residents' health status and detect abnormal usage patterns. For example, the monitoring unit can analyze abnormal usage patterns in real time while considering the residents' health status. For example, the monitoring unit can use AI to learn the residents' health status and predict abnormal usage patterns in advance. This allows for early detection of abnormalities by analyzing usage patterns while considering the residents' health status. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input residents' health status data into a generating AI and have the generating AI perform the usage pattern analysis.

[0094] The monitoring unit can estimate the emotions of residents and adjust the accuracy of monitoring based on the estimated emotions. For example, if a resident is feeling stressed, the monitoring unit can increase the accuracy of monitoring to detect abnormalities early. For example, if a resident is relaxed, the monitoring unit can decrease the accuracy of monitoring to reduce the system load. For example, if a resident is feeling anxious, the monitoring unit can appropriately adjust the accuracy of monitoring to provide a sense of security. In this way, by adjusting the accuracy of monitoring based on the emotions of residents, abnormalities can be detected early and the system load can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input resident emotion data into a generative AI and have the generative AI perform the adjustment of the monitoring accuracy.

[0095] The monitoring unit can learn residents' behavior patterns during monitoring and predict abnormal behavior in advance, issuing warnings. For example, the monitoring unit can learn residents' behavior patterns and predict abnormal behavior in advance. For example, the monitoring unit can use AI to analyze residents' behavior patterns and detect abnormal behavior in real time. For example, the monitoring unit can warn about abnormal behavior in advance based on residents' behavior patterns. In this way, by learning residents' behavior patterns, it can predict abnormal behavior in advance and issue warnings. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input resident behavior pattern data into a generating AI and have the generating AI perform predictions of abnormal behavior.

[0096] The monitoring unit can record the resident's movement speed and posture in detail during monitoring and assess the risk of falls. For example, the monitoring unit can record the resident's movement speed in real time and assess the risk of falls. For example, the monitoring unit can record the resident's posture in detail and assess the risk of falls. For example, the monitoring unit can use AI to analyze the resident's movement speed and posture and predict the risk of falls in advance. This allows the risk of falls to be assessed by recording the resident's movement speed and posture in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the resident's movement speed and posture data into a generating AI and have the generating AI perform a fall risk assessment.

[0097] The monitoring unit can estimate the emotions of residents and adjust the monitoring time based on the estimated emotions. For example, if a resident is feeling stressed, the monitoring unit can increase the monitoring time to detect abnormalities early. For example, if a resident is relaxed, the monitoring unit can decrease the monitoring time to reduce the system load. For example, if a resident is feeling anxious, the monitoring unit can appropriately adjust the monitoring time to provide a sense of security. In this way, by adjusting the monitoring time based on the emotions of residents, abnormalities can be detected early and the system load can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input resident emotion data into the generative AI and have the generative AI perform the adjustment of the monitoring time.

[0098] The monitoring unit can adjust the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can expand the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can narrow the monitoring range while taking into account the residents' living environment. For example, the monitoring unit can have an AI analyze the residents' living environment and propose the optimal monitoring range. This makes optimal monitoring possible by adjusting the monitoring range while taking into account the residents' living environment. Some or all of the above processing in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input residents' living environment data into a generating AI and have the generating AI perform the adjustment of the monitoring range.

[0099] The monitoring unit can adjust the accuracy of monitoring while taking into account the health status of the residents. For example, the monitoring unit can monitor the health status of residents and adjust the accuracy of monitoring. For example, the monitoring unit can adjust the accuracy of monitoring in real time, taking into account the health status of residents. For example, the monitoring unit can have an AI learn the health status of residents and propose the optimal accuracy of monitoring. This allows for early detection of abnormalities by adjusting the accuracy of monitoring while taking into account the health status of residents. Some or all of the above processes in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input resident health status data into a generating AI and have the generating AI perform the adjustment of the accuracy of monitoring.

[0100] The warning unit can estimate the resident's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the resident is stressed, the warning unit can simplify the way the warning is expressed. For example, if the resident is relaxed, the warning unit can provide a more detailed warning. For example, if the resident is anxious, the warning unit can appropriately adjust the way the warning is expressed. This allows for the provision of appropriate warnings by adjusting the way the warning is expressed based on the resident's emotions. 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 warning unit may be performed using AI, or not using AI. For example, the warning unit can input the resident's emotion data into the generative AI and have the generative AI adjust the way the warning is expressed.

[0101] The warning unit can select different warning methods depending on the type of anomaly when an alarm is issued. For example, the warning unit can select an audio warning depending on the type of anomaly. For example, the warning unit can select a visual warning depending on the type of anomaly. For example, the warning unit can select a vibration warning depending on the type of anomaly. This allows the unit to quickly notify residents of an anomaly by selecting the appropriate warning method according to the type of anomaly. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can have a generating AI perform the selection of a warning method according to the type of anomaly.

[0102] The warning unit can determine the priority of warnings by referring to past warning history when an warning is issued. For example, the warning unit can determine the priority of warnings by referring to past warning history. For example, the warning unit can have AI analyze past warning history and propose the optimal warning priority. For example, the warning unit can adjust the warning priority in real time based on past warning history. This allows for the appropriate determination of warning priority by referring to past warning history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning history data into a generating AI and have the generating AI perform the determination of warning priority.

[0103] The warning unit can estimate the resident's emotions and adjust the timing of the warning based on the estimated emotions. For example, if the resident is feeling stressed, the warning unit can speed up the warning. For example, if the resident is relaxed, the warning unit can delay the warning. For example, if the resident is feeling anxious, the warning unit can appropriately adjust the timing of the warning. In this way, by adjusting the timing of the warning based on the resident's emotions, a warning can be provided at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the resident's emotion data into the generative AI and have the generative AI adjust the timing of the warning.

[0104] The warning unit can adjust the frequency of warnings when a warning is issued, taking into account the resident's daily rhythm. For example, the warning unit can increase the frequency of warnings, taking into account the resident's daily rhythm. For example, the warning unit can decrease the frequency of warnings, taking into account the resident's daily rhythm. For example, the warning unit can have an AI analyze the resident's daily rhythm and suggest the optimal warning frequency. By adjusting the warning frequency while taking into account the resident's daily rhythm, warnings can be provided at an appropriate frequency. Some or all of the above-described processes in the warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input the resident's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the warning frequency.

[0105] The warning unit can adjust the content of a warning when it issues one, taking into account the resident's health condition. For example, the warning unit can monitor the resident's health condition and adjust the content of the warning. For example, the warning unit can adjust the content of the warning in real time, taking into account the resident's health condition. For example, the warning unit can have an AI learn the resident's health condition and suggest the optimal warning content. This allows the warning unit to provide appropriate warnings by adjusting the content of the warning, taking into account the resident's health condition. Some or all of the above-described processes in the warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input the resident's health condition data into a generating AI and have the generating AI perform the adjustment of the warning content.

[0106] The prediction unit can estimate the residents' emotions and adjust the accuracy of the risk prediction based on the estimated emotions. For example, if a resident is feeling stressed, the prediction unit can increase the accuracy of the risk prediction. For example, if a resident is relaxed, the prediction unit can decrease the accuracy of the risk prediction. For example, if a resident is feeling anxious, the prediction unit can appropriately adjust the accuracy of the risk prediction. In this way, by adjusting the accuracy of the risk prediction based on the residents' emotions, risks can be predicted with appropriate accuracy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input resident emotion data into the generative AI and have the generative AI perform the adjustment of the accuracy of the risk prediction.

[0107] The prediction unit can assess the risk of falling by referring to the resident's past behavior patterns during prediction. For example, the prediction unit can assess the risk of falling by referring to the resident's past behavior patterns. For example, the prediction unit can have an AI analyze the resident's past behavior patterns and assess the risk of falling in real time. For example, the prediction unit can predict the risk of falling in advance based on the resident's past behavior patterns. This allows for an appropriate assessment of the risk of falling by referring to the resident's past behavior patterns. Some or all of the above processing in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input the resident's past behavior pattern data into a generating AI and have the generating AI perform the assessment of the risk of falling.

[0108] The prediction unit can record the resident's movement speed and posture in detail during prediction and assess the risk of falling. For example, the prediction unit can record the resident's movement speed in real time and assess the risk of falling. For example, the prediction unit can record the resident's posture in detail and assess the risk of falling. For example, the prediction unit can use AI to analyze the resident's movement speed and posture and predict the risk of falling in advance. This allows for an appropriate assessment of the risk of falling by recording the resident's movement speed and posture in detail. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the resident's movement speed and posture data into a generating AI and have the generating AI perform the assessment of the risk of falling.

[0109] The prediction unit can estimate the residents' emotions and adjust the timing of danger prediction based on the estimated emotions. For example, if a resident is feeling stressed, the prediction unit can advance the timing of danger prediction. For example, if a resident is relaxed, the prediction unit can delay the timing of danger prediction. For example, if a resident is feeling anxious, the prediction unit can appropriately adjust the timing of danger prediction. In this way, by adjusting the timing of danger prediction based on the residents' emotions, danger can be predicted at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input resident emotion data into the generative AI and have the generative AI perform the adjustment of the timing of danger prediction.

[0110] The prediction unit can adjust the range of risk prediction by considering the residents' living environment during the prediction process. For example, the prediction unit can broaden the range of risk prediction by considering the residents' living environment. For example, the prediction unit can narrow the range of risk prediction by considering the residents' living environment. For example, the prediction unit can have an AI analyze the residents' living environment and propose the optimal range of risk prediction. By adjusting the range of risk prediction by considering the residents' living environment, risks can be predicted within an appropriate range. Some or all of the above-described processes in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input residents' living environment data into a generating AI and have the generating AI perform the adjustment of the risk prediction range.

[0111] The prediction unit can adjust the accuracy of risk prediction by considering the health status of residents during prediction. For example, the prediction unit can monitor the health status of residents and adjust the accuracy of risk prediction. For example, the prediction unit can adjust the accuracy of risk prediction in real time by considering the health status of residents. For example, the prediction unit can have an AI learn the health status of residents and propose the optimal accuracy of risk prediction. By adjusting the accuracy of risk prediction by considering the health status of residents, risks can be predicted with appropriate accuracy. Some or all of the above processing in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input resident health status data into a generating AI and have the generating AI perform the adjustment of the accuracy of risk prediction.

[0112] The reporting unit can estimate the resident's emotions and adjust the way the report is expressed based on the estimated emotions. For example, if the resident is stressed, the reporting unit can simplify the way the report is expressed. For example, if the resident is relaxed, the reporting unit can provide a detailed report. For example, if the resident is anxious, the reporting unit can appropriately adjust the way the report is expressed. In this way, by adjusting the way the report is expressed based on the resident's emotions, an appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input resident emotion data into a generative AI and have the generative AI adjust the way the report is expressed.

[0113] The notification unit can select different notification methods depending on the type of anomaly when an alert is issued. For example, the notification unit can select an audio alert depending on the type of anomaly. For example, the notification unit can select a visual alert depending on the type of anomaly. For example, the notification unit can select a vibration alert depending on the type of anomaly. This allows for rapid notification of anomalies by selecting the appropriate notification method according to the type of anomaly. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can have a generating AI perform the selection of a notification method according to the type of anomaly.

[0114] The reporting unit can determine the priority of a report by referring to past reporting history when a report is made. For example, the reporting unit can determine the priority of a report by referring to past reporting history. For example, the reporting unit can have AI analyze past reporting history and propose the optimal priority of reports. For example, the reporting unit can adjust the priority of reports in real time based on past reporting history. This allows for the appropriate determination of the priority of reports by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the determination of the priority of reports.

[0115] The notification unit can estimate the resident's emotions and adjust the timing of the notification based on the estimated emotions. For example, if the resident is feeling stressed, the notification unit can speed up the timing of the notification. For example, if the resident is relaxed, the notification unit can delay the timing of the notification. For example, if the resident is feeling anxious, the notification unit can appropriately adjust the timing of the notification. In this way, by adjusting the timing of the notification based on the resident's emotions, notifications can be provided at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using 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 notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input resident emotion data into a generative AI and have the generative AI perform the adjustment of the timing of the notification.

[0116] The notification unit can adjust the frequency of notifications when a notification is made, taking into account the resident's daily rhythm. For example, the notification unit can increase the frequency of notifications, taking into account the resident's daily rhythm. For example, the notification unit can decrease the frequency of notifications, taking into account the resident's daily rhythm. For example, the notification unit can have an AI analyze the resident's daily rhythm and suggest the optimal frequency of notifications. This allows notifications to be provided at an appropriate frequency by adjusting the frequency of notifications, taking into account the resident's daily rhythm. Some or all of the above processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the resident's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the notification frequency.

[0117] The notification unit can adjust the content of a notification considering the resident's health condition at the time of notification. For example, the notification unit can monitor the resident's health condition and adjust the content of the notification. For example, the notification unit can adjust the content of the notification in real time considering the resident's health condition. For example, the notification unit can have an AI learn the resident's health condition and suggest the optimal content of the notification. This allows for the provision of appropriate notifications by adjusting the content of the notification considering the resident's health condition. Some or all of the above-described processes in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the resident's health condition data into a generating AI and have the generating AI perform the adjustment of the content of the notification.

[0118] The recording unit can estimate the resident's emotions and adjust the recording's presentation based on the estimated emotions. For example, if the resident is stressed, the recording unit can provide a simple recording method. For example, if the resident is relaxed, the recording unit can provide a detailed recording method. For example, if the resident is anxious, the recording unit can appropriately adjust the recording's presentation. This allows for the provision of appropriate recordings by adjusting the recording's presentation based on the resident's emotions. 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 recording unit may be performed using AI, or not using AI. For example, the recording unit can input the resident's emotion data into the generative AI and have the generative AI adjust the recording's presentation.

[0119] The recording unit can determine recording priorities by referring to the resident's viewing history during recording. For example, the recording unit can refer to the resident's viewing history to determine recording priorities. For example, the recording unit can have an AI analyze the resident's viewing history and suggest the optimal recording priorities. For example, the recording unit can adjust recording priorities in real time based on the resident's viewing history. This allows for the appropriate determination of recording priorities by referring to the resident's viewing history. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the resident's viewing history data into a generating AI and have the generating AI perform the determination of recording priorities.

[0120] The recording unit can customize the content of recordings based on the resident's hobbies and interests during recording. For example, the recording unit can refer to the resident's hobbies and customize the content of the recordings. For example, the recording unit can use AI to analyze the resident's interests and suggest the most suitable content for recording. For example, the recording unit can customize the content of recordings in real time based on the resident's hobbies and interests. This allows for the provision of appropriate recordings by customizing the content of recordings based on the resident's hobbies and interests. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data on the resident's hobbies and interests into a generating AI and have the generating AI perform the customization of the recording content.

[0121] The recording unit can estimate the emotions of the residents and adjust the timing of the recording based on the estimated emotions. For example, if the resident is feeling stressed, the recording unit can advance the timing of the recording. For example, if the resident is relaxed, the recording unit can delay the timing of the recording. For example, if the resident is feeling anxious, the recording unit can appropriately adjust the timing of the recording. In this way, by adjusting the timing of the recording based on the emotions of the residents, recordings can be provided at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input the resident's emotion data into the generative AI and have the generative AI perform the adjustment of the recording timing.

[0122] The recording unit can adjust the recording frequency while taking into account the residents' daily routines. For example, the recording unit can increase the recording frequency while taking into account the residents' daily routines. For example, the recording unit can decrease the recording frequency while taking into account the residents' daily routines. For example, the recording unit can have an AI analyze the residents' daily routines and suggest the optimal recording frequency. This allows the recording unit to provide recordings at an appropriate frequency by adjusting the recording frequency while taking into account the residents' daily routines. Some or all of the above processing in the recording unit may be performed using an AI, for example, or without an AI. For example, the recording unit can input the residents' daily routine data into a generating AI and have the generating AI perform the adjustment of the recording frequency.

[0123] The recording unit can adjust the content of the recording while taking into account the health status of the residents. For example, the recording unit can monitor the health status of the residents and adjust the content of the recording. For example, the recording unit can adjust the content of the recording in real time, taking into account the health status of the residents. For example, the recording unit can have an AI learn the health status of the residents and suggest the optimal content of the recording. In this way, appropriate recordings can be provided by adjusting the content of the recording while taking into account the health status of the residents. Some or all of the above processing in the recording unit may be performed using an AI, for example, or without an AI. For example, the recording unit can input the health status data of the residents into a generating AI and have the generating AI perform the adjustment of the content of the recording.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The monitoring unit can monitor not only the usage of electricity, water, and gas within the home, but also the temperature and humidity. For example, the monitoring unit can issue a warning if the temperature inside the home is abnormally high. It can also issue a warning if the humidity inside the home is abnormally low. Furthermore, the monitoring unit can collect temperature and humidity data and make suggestions for maintaining a comfortable environment. In this way, monitoring the temperature and humidity inside the home can improve the comfort of the residents.

[0126] The monitoring unit can not only monitor residents' actions via webcams but also monitor their voices. For example, it can analyze the tone and volume of residents' voices to detect abnormalities. It can also monitor changes in residents' voices in real time to detect abnormalities early. Furthermore, it can collect data on residents' voices to predict changes in their health. By monitoring residents' voices, it is possible to detect abnormalities early and ensure the safety of residents.

[0127] The warning unit not only issues a warning when it detects an abnormality, but can also emit different warning sounds depending on the type of abnormality. For example, the warning unit can emit a high-pitched warning sound when it detects a fire. It can also emit a low-pitched warning sound when it detects a water leak. Furthermore, it can emit a medium-pitched warning sound when it detects a gas leak. This allows the unit to quickly notify residents of abnormalities by emitting different warning sounds depending on the type of abnormality.

[0128] The prediction unit can not only predict dangers such as falls, but also predict dangers while considering the residents' health status. For example, the prediction unit can monitor residents' heart rate and blood pressure and detect abnormalities. It can also collect data on residents' health status and predict changes in their health status. Furthermore, the prediction unit can assess the risk of falls while considering the residents' health status. In this way, by predicting dangers while considering the residents' health status, the safety of residents can be ensured.

[0129] The notification unit can not only make emergency calls, but also customize the content of the calls. For example, if the notification unit detects a fire, it can report detailed information about the fire. It can also report the location and extent of a water leak if it detects one. Furthermore, if it detects a gas leak, it can report the type and amount of the leak. This allows for a quick and appropriate response by customizing the content of the calls.

[0130] The monitoring unit can estimate the residents' emotions and adjust the monitoring frequency of electricity, water, and gas usage based on the estimated emotions. For example, if a resident is stressed, the monitoring frequency can be increased to detect abnormalities early. Conversely, if a resident is relaxed, the monitoring frequency can be decreased to reduce the system load. Furthermore, if a resident is feeling anxious, the monitoring frequency can be appropriately adjusted to provide a sense of security. In this way, by adjusting the monitoring frequency based on the residents' emotions, abnormalities can be detected early and the system load can be reduced.

[0131] The monitoring unit can estimate the residents' emotions and adjust the monitoring accuracy based on those estimates. For example, if a resident is feeling stressed, the monitoring accuracy can be increased to detect abnormalities early. Conversely, if a resident is relaxed, the monitoring accuracy can be decreased to reduce the system load. Furthermore, if a resident is feeling anxious, the monitoring accuracy can be appropriately adjusted to provide a sense of security. In this way, by adjusting the monitoring accuracy based on the residents' emotions, abnormalities can be detected early and the system load can be reduced.

[0132] The warning unit can estimate the resident's emotions and adjust the way the warning is expressed based on those emotions. For example, if the resident is stressed, the warning can be simplified. Conversely, if the resident is relaxed, a more detailed warning can be provided. Furthermore, if the resident is anxious, the warning can be appropriately adjusted. In this way, by adjusting the warning expression based on the resident's emotions, an appropriate warning can be provided.

[0133] The prediction unit can estimate the residents' emotions and adjust the accuracy of the risk prediction based on those emotions. For example, if a resident is feeling stressed, the accuracy of the risk prediction can be increased. Conversely, if a resident is relaxed, the accuracy can be decreased. Furthermore, if a resident is feeling anxious, the accuracy of the risk prediction can be appropriately adjusted. In this way, by adjusting the accuracy of the risk prediction based on the residents' emotions, risks can be predicted with appropriate accuracy.

[0134] The reporting system can estimate the resident's emotions and adjust the wording of the report based on those estimates. For example, if the resident is feeling stressed, the report can be simplified. Conversely, if the resident is relaxed, a more detailed report can be provided. Furthermore, if the resident is feeling anxious, the report can be appropriately adjusted. By adjusting the wording of the report based on the resident's emotions, the system can provide appropriate reports.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The monitoring unit monitors the usage of electricity, water, and gas within the home. For example, it can use electricity meters, water meters, and gas meters to monitor their usage in real time. Step 2: The monitoring unit monitors residents' behavior based on the data collected by the monitoring unit. For example, it can use webcams to monitor residents' movements in real time and also record and analyze their behavior. Step 3: The warning unit issues a warning if it detects an anomaly based on the behavior monitored by the monitoring unit. For example, it can issue warnings via sound, visual, or vibration. Step 4: The prediction unit predicts risks such as falls based on warnings issued by the warning unit. For example, it can assess the risk of falls by considering the resident's movement speed and posture, past behavioral patterns, and health condition. Step 5: The notification unit makes an emergency call based on the hazard predicted by the prediction unit. For example, the emergency call can be made using telephone, the internet, or wireless communication. Step 6: The recording unit records programs based on the residents' hobbies and viewing history monitored by the monitoring unit. For example, it can determine recording priorities by referring to the residents' viewing history and customize the recording content based on their hobbies and interests.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the monitoring unit, surveillance unit, warning unit, prediction unit, notification unit, and recording unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the usage of electricity, water, and gas in the home in real time. The surveillance unit monitors the behavior of residents using, for example, the camera 42 of the smart device 14. The warning unit issues a warning when it detects an abnormality using, for example, the output device 40 of the smart device 14. The prediction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and predicts dangers such as falls. The notification unit makes an emergency notification using, for example, the communication I / F 26 of the data processing unit 12. The recording unit is implemented by, for example, the control unit 46A of the smart device 14 and records programs based on the resident's hobbies and viewing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 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.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the 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.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 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.

[0156] Each of the multiple elements described above, including the monitoring unit, surveillance unit, warning unit, prediction unit, notification unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the usage of electricity, water, and gas in the home in real time. The surveillance unit monitors the behavior of residents using the camera 42 of the smart glasses 214. The warning unit issues a warning when an abnormality is detected using the speaker 240 of the smart glasses 214. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts dangers such as falls. The notification unit makes an emergency notification using the communication I / F 26 of the data processing unit 12. The recording unit is implemented by the control unit 46A of the smart glasses 214 and records programs based on the resident's hobbies and viewing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the monitoring unit, surveillance unit, warning unit, prediction unit, notification unit, and recording unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the usage of electricity, water, and gas in the home in real time. The surveillance unit monitors the behavior of residents using the camera 42 of the headset terminal 314. The warning unit issues a warning when an abnormality is detected using the speaker 240 of the headset terminal 314. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts dangers such as falls. The notification unit makes an emergency notification using the communication I / F 26 of the data processing unit 12. The recording unit is implemented by the control unit 46A of the headset terminal 314 and records programs based on the resident's hobbies and viewing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the monitoring unit, surveillance unit, warning unit, prediction unit, notification unit, and recording unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the usage of electricity, water, and gas in the home in real time. The surveillance unit monitors the actions of residents using the camera 42 of the robot 414. The warning unit issues a warning when an abnormality is detected using the speaker 240 of the robot 414. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts dangers such as falls. The notification unit makes an emergency notification using the communication I / F 26 of the data processing unit 12. The recording unit is implemented by the control unit 46A of the robot 414 and records programs based on the residents' hobbies and viewing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A monitoring unit that monitors the usage of electricity, water, and gas within the home, A monitoring unit monitors the behavior of residents based on the data collected by the aforementioned monitoring unit, A warning unit that issues a warning when it detects an abnormality based on the actions monitored by the aforementioned monitoring unit, A prediction unit that predicts dangers such as falls based on warnings issued by the aforementioned warning unit, An emergency notification unit that issues an emergency notification based on the danger predicted by the prediction unit, The system includes a recording unit that records programs based on the hobbies and viewing history of residents monitored by the aforementioned monitoring unit. A system characterized by the following features. (Note 2) The monitoring unit, Monitor household electricity, water, and gas usage in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, Monitoring residents' behavior via webcams The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is It issues a warning when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, Predicting risks such as falls The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reporting unit, Make an emergency call The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is Record programs based on residents' hobbies and viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, The system estimates residents' emotions and adjusts the frequency of monitoring electricity, water, and gas usage based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, It analyzes past usage history and predicts abnormal usage patterns in advance, issuing warnings. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During usage monitoring, the power consumption and usage time of each device are recorded in detail to optimize energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, It estimates the residents' emotions and prioritizes monitoring devices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, During monitoring, usage patterns are filtered based on the residents' daily routines to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, During monitoring, usage patterns are analyzed while considering the residents' health status, and abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, The system estimates the residents' emotions and adjusts the accuracy of monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned monitoring unit, During monitoring, the system learns the residents' behavior patterns and predicts abnormal behavior in advance, issuing warnings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned monitoring unit, During monitoring, the movement speed and posture of residents are recorded in detail to assess the risk of falls. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, The system estimates the residents' emotions and adjusts the monitoring schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, When monitoring, adjust the scope of monitoring to take into account the residents' living environment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, During monitoring, the accuracy of the monitoring is adjusted taking into account the health status of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is The system estimates the residents' emotions and adjusts the way warnings are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When a warning is issued, a different warning method is selected depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When a warning is issued, the system prioritizes the warning by referring to past warning history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is The system estimates the residents' emotions and adjusts the timing of warnings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When issuing a warning, the frequency of the warning will be adjusted to take into account the residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is When issuing a warning, the content of the warning will be adjusted to take into account the health condition of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, The system estimates the residents' emotions and adjusts the accuracy of risk predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, the risk of falls is assessed by referring to the residents' past behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, During prediction, the movement speed and posture of residents are recorded in detail to assess the risk of falls. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, The system estimates the residents' emotions and adjusts the timing of risk predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, the range of risk predictions is adjusted considering the living environment of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, When making predictions, the accuracy of the risk predictions is adjusted by taking into account the health status of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting unit, The system estimates the residents' emotions and adjusts the wording of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reporting unit, When reporting an issue, a different reporting method will be selected depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reporting unit, When a report is submitted, the priority of the report is determined by referring to past reporting history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reporting unit, The system estimates the residents' emotions and adjusts the timing of reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reporting unit, When reporting an incident, the frequency of reporting will be adjusted to take into account the residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned reporting unit, When making a report, the content of the report will be adjusted to take into consideration the resident's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned recording unit is The system estimates the residents' emotions and adjusts the way the recording is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned recording unit is When recording, the recording priority is determined by referring to the resident's viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned recording unit is When recording, the content of the recording is customized based on the residents' hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned recording unit is The system estimates the residents' emotions and adjusts the recording timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recording unit is When recording, adjust the recording frequency to take into account the residents' daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned recording unit is When recording, the content of the recording will be adjusted to take into account the health condition of the residents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 monitoring unit that monitors the usage of electricity, water, and gas within the home, A monitoring unit monitors the behavior of residents based on the data collected by the aforementioned monitoring unit, A warning unit that issues a warning when it detects an abnormality based on the actions monitored by the aforementioned monitoring unit, A prediction unit that predicts dangers such as falls based on warnings issued by the aforementioned warning unit, An emergency notification unit that issues an emergency notification based on the danger predicted by the prediction unit, The system includes a recording unit that records programs based on the hobbies and viewing history of residents monitored by the aforementioned monitoring unit. A system characterized by the following features.

2. The monitoring unit, Monitor household electricity, water, and gas usage in real time. The system according to feature 1.

3. The aforementioned monitoring unit, Monitoring residents' behavior via webcams The system according to feature 1.

4. The aforementioned warning unit is It issues a warning when an anomaly is detected. The system according to feature 1.

5. The prediction unit, Predicting risks such as falls The system according to feature 1.

6. The aforementioned reporting unit, Make an emergency call The system according to feature 1.

7. The aforementioned recording unit is Record programs based on residents' hobbies and viewing history. The system according to feature 1.

8. The monitoring unit, The system estimates residents' emotions and adjusts the frequency of monitoring electricity, water, and gas usage based on these estimated emotions. The system according to feature 1.

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