System

Abstract

The system according to the embodiment aims to enhance the security of the home and secure the safety of the residents and the dwelling.SOLUTION: A system according to an embodiment includes a confirmation unit, a detection unit, a notification unit, an alert unit, and a dialogue unit. The confirmation unit confirms the image of the security camera in real time. The detection unit detects a fire or gas leakage based on the image confirmed by the confirmation unit. The reporting unit reports to the police or the fire department based on the emergency situation detected by the detection unit. The alert unit recognizes a face of a family member or an authorized person, and transmits an alert when a suspicious person intrudes into the house. The interaction unit performs voice interaction when a suspicious person intrudes while the resident is absent.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide systems to enhance home security and ensure the safety of residents and their homes, and there is room for improvement.

[0005] The system according to the embodiment aims to strengthen the security of a home and ensure the safety of the resident and the residence. [Means for solving the problem]

[0006] The system according to the embodiment includes a confirmation unit, a detection unit, a reporting unit, an alert unit, and a dialogue unit. The confirmation unit checks security camera footage in real time. The detection unit detects fires and gas leaks based on the footage checked by the confirmation unit. The reporting unit notifies the police and fire department based on an emergency detected by the detection unit. The alert unit recognizes the faces of family members and authorized individuals and issues an alert if a suspicious person enters the residence. The dialogue unit engages in voice dialogue if a suspicious person enters the residence when no resident is present. [Effects of the Invention]

[0007] The system according to the embodiment can enhance the security of a home and ensure the safety of the resident and the residence. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A mobile app according to an embodiment of the present invention is a system for enhancing home security and ensuring the safety of residents and their homes. The mobile app monitors security camera footage in real time, detects fires and gas leaks, and notifies the police and fire department in emergencies. It also learns the faces of family members and authorized individuals and issues an alert if a suspicious individual breaks into the residence. Furthermore, it engages in virtual conversations if a suspicious individual breaks into the residence while the resident is away. For example, the mobile app allows users to view security camera footage in real time from their smartphone. For example, users can check the status of their home while away from home. It also works with security sensors to detect fires and gas leaks. For example, if a gas leak occurs in the kitchen, the sensor detects it and notifies the user. Furthermore, it automatically notifies the police and fire department in emergencies. For example, if a fire breaks out, the app automatically notifies the fire department. The app then learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual breaks into the residence. For example, if a non-family member is standing at the front door, the app detects it and notifies the user. Furthermore, if a suspicious individual breaks into the home while the residents are away, a virtual conversation will be held through the speaker. For example, if a suspicious individual breaks into the front door, a message will be played over the speaker saying, "This area is under security. Please leave immediately." The app also evaluates the home's security based on photos and floor plan, suggesting appropriate security measures. For example, it suggests the best locations for security cameras based on the location of windows and doors. Furthermore, the app obtains the latest local information and suggests optimal disaster prevention measures and evacuation routes. For example, in the event of an earthquake, the app notifies the user of the optimal evacuation route. This allows the mobile app to enhance home security and ensure the safety of residents and their homes. For example, the app allows users to check the status of their home while away from home, quickly detects fires and gas leaks, and automatically reports emergencies. Furthermore, if a suspicious individual breaks into the home, an alert will be sent and a virtual conversation will be held to intimidate the intruder and prevent them from entering. Furthermore, the app performs a security assessment of the home and suggests appropriate security measures, improving the safety of the home.By proposing optimal disaster prevention measures and evacuation routes based on the latest local information, safety can be ensured in the event of a disaster.

[0029] A security system according to an embodiment includes a confirmation unit, a detection unit, a notification unit, an alert unit, and a dialogue unit. The confirmation unit checks security camera footage in real time. For example, the confirmation unit can check security camera footage in real time from a smartphone. The confirmation unit can also set the resolution of the footage and the frequency of checking. For example, the confirmation unit provides high-resolution footage, allowing the user to enlarge the footage as needed. The detection unit detects fires and gas leaks based on the footage checked by the confirmation unit. For example, the detection unit works in conjunction with a security sensor to detect fires and gas leaks. The detection unit can set the detection method and type of sensor for fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or a gas sensor. The notification unit notifies the police or fire department based on an emergency detected by the detection unit. For example, the notification unit automatically notifies the police or fire department in an emergency. The notification unit can set the timing of the notification and information on the destination of the notification. For example, the notification unit automatically notifies the fire department in the event of a fire. The alert unit recognizes the faces of family members and authorized individuals and issues an alert if a suspicious individual breaks into the residence. For example, the alert unit learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual breaks into the residence. The alert unit can set a facial recognition algorithm and recognition accuracy. For example, the alert unit detects a suspicious individual using a highly accurate facial recognition algorithm. The dialogue unit engages in a voice dialogue if a suspicious individual breaks into the residence when no resident is present. For example, the dialogue unit engages in a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, if a suspicious individual breaks into the front door, the dialogue unit plays a message such as "This area is guarded. Please leave immediately." As a result, the security system according to the embodiment can strengthen the security of the residence and ensure the safety of the residents and the residence by checking security camera footage, detecting fires and gas leaks, reporting emergencies, issuing suspicious individual alerts, and conducting virtual dialogue.

[0030] The confirmation unit can check security camera footage in real time from a smartphone. The confirmation unit can, for example, check security camera footage in real time from a smartphone. For example, the confirmation unit can check the status of one's home while away from home. The confirmation unit can also set the resolution of the footage and the frequency of checking. For example, the confirmation unit can provide high-resolution footage and allow the user to enlarge the footage as needed. This allows one to check the status of one's home while away from home by checking security camera footage in real time from a smartphone. Some or all of the above-described processing in the confirmation unit may be performed using, or without, AI, for example. For example, the confirmation unit can input video data acquired from a smartphone to a generation AI and have the generation AI analyze the video data.

[0031] The detection unit can cooperate with a security sensor to detect fires and gas leaks. The detection unit, for example, cooperates with a security sensor to detect fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or a gas sensor. The detection unit can also set the detection method and type of sensor for fires and gas leaks. For example, the detection unit can adjust the sensitivity of the smoke sensor to perform early fire detection. The detection unit can also select the type of gas sensor to detect specific gas leaks. This allows for rapid detection of fires and gas leaks by working with the security sensor. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data obtained from the security sensor into the generation AI and cause the generation AI to detect fires and gas leaks.

[0032] The reporting unit can automatically notify the police or fire department in an emergency. The reporting unit, for example, automatically notifies the police or fire department in an emergency. For example, the reporting unit automatically notifies the fire department in the event of a fire. The reporting unit can also set the timing of the report and the information on the reporting destination. For example, the reporting unit can make an immediate report in the event of a fire. The reporting unit can also register the information on the reporting destination in advance and make a prompt report. This enables a prompt response by automatically reporting in an emergency. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input emergency data into a generation AI and have the generation AI execute the report.

[0033] The alert unit can recognize the faces of family members or authorized individuals and send an alert to the user if a suspicious individual breaks into the residence. For example, the alert unit can recognize the faces of family members or authorized individuals and send an alert to the user if a suspicious individual breaks into the residence. For example, the alert unit can detect when a person other than a family member is standing at the front door and notify the user. The alert unit can also set the facial recognition algorithm and recognition accuracy. For example, the alert unit can detect suspicious individuals using a highly accurate facial recognition algorithm. The alert unit can also adjust the recognition accuracy to prevent false recognition. This enhances security by quickly sending an alert to the user if a suspicious individual breaks into the residence. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input facial recognition data into a generation AI and have the generation AI detect suspicious individuals.

[0034] The dialogue unit can conduct a voice dialogue when a suspicious individual breaks into a building when no resident is present. The dialogue unit conducts a voice dialogue when a suspicious individual breaks into a building when no resident is present. For example, the dialogue unit conducts a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, when a suspicious individual breaks into the entrance, the dialogue unit can play a message such as "This area is guarded. Please leave immediately." The dialogue unit can also adjust the tone of the dialogue to enhance the intimidating effect. This allows a virtual dialogue to be conducted when a suspicious individual breaks into a building when no resident is present, thereby intimidating the suspicious individual and preventing them from breaking in. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input voice data into a generation AI and have the generation AI execute a virtual dialogue.

[0035] The security system includes an evaluation unit that performs a security evaluation of a residence and proposes appropriate crime prevention measures. For example, when a user inputs photos and floor plan data of the user's home into an app, the evaluation unit performs a security evaluation of the residence and proposes appropriate crime prevention measures. For example, the evaluation unit proposes locations for installing security cameras based on the positions of windows and doors. The evaluation unit can also set criteria and evaluation items for the security evaluation of the residence. For example, the evaluation unit sets evaluation items such as the number of windows, the positions of doors, and the surrounding environment to perform a comprehensive security evaluation. This allows the security evaluation of the residence to be performed and appropriate crime prevention measures to be proposed, thereby improving the safety of the residence. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the photos and floor plan data input by the user into a generation AI and have the generation AI perform a security evaluation.

[0036] The security system includes a proposal unit that acquires the latest local information and proposes optimal disaster countermeasures and evacuation routes. The proposal unit, for example, acquires the latest local information and proposes optimal disaster countermeasures and evacuation routes. For example, the proposal unit notifies the user of the optimal evacuation route when an earthquake occurs. The proposal unit can also set a method for acquiring local information and proposal criteria. For example, the proposal unit acquires meteorological information and disaster information in real time and proposes the optimal evacuation route based on the information. This makes it possible to ensure safety in the event of a disaster by proposing optimal disaster countermeasures and evacuation routes based on the latest local information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input local information data to a generation AI and cause the generation AI to propose disaster countermeasures and evacuation routes.

[0037] The confirmation unit can prioritize display of video based on a specific time period or location when checking video. The confirmation unit, for example, prioritizes display of video based on a specific time period or location when checking video. For example, the confirmation unit can prioritize display of video of the entrance or windows at night. The confirmation unit can also prioritize display of video of the living room during the daytime on weekdays. The confirmation unit can also prioritize display of video of the entire home when traveling. In this way, by prioritizing display of video based on a specific time period or location, important video can be quickly checked. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input data on the time period and location into the generation AI and cause the generation AI to prioritize display of video.

[0038] The confirmation unit can select the optimal display method by referring to the user's past confirmation history when checking video. For example, the confirmation unit selects the optimal display method by referring to the user's past confirmation history when checking video. For example, the confirmation unit may prioritize displaying video from cameras that the user has frequently checked in the past. The confirmation unit may also prioritize displaying video during a specific time period based on video checked by the user during the same time period. The confirmation unit may also select video to prioritize display during a specific event based on the user's past confirmation history. In this way, the optimal video display method can be provided by referring to the user's past confirmation history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input past confirmation history data into a generation AI and cause the generation AI to select the optimal display method.

[0039] The confirmation unit can apply an anomaly detection algorithm to highlight abnormal areas when checking the video. The confirmation unit, for example, applies an anomaly detection algorithm to highlight abnormal areas when checking the video. For example, if the confirmation unit detects an abnormal sound, it highlights the area. Furthermore, if the confirmation unit detects an abnormal movement, it can also highlight the area. Furthermore, if the confirmation unit detects an abnormal temperature change, it can also highlight the area. In this way, by applying the anomaly detection algorithm, abnormal areas can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the anomaly detection data to the generation AI and cause the generation AI to highlight the abnormal areas.

[0040] The confirmation unit can prioritize displaying highly relevant video in consideration of the user's geographical location information when confirming video. For example, the confirmation unit prioritizes displaying highly relevant video in consideration of the user's geographical location information when confirming video. For example, if the user is close to their home, the confirmation unit can prioritize displaying video of the entrance or windows. Furthermore, if the user is far away, the confirmation unit can also prioritize displaying video of the entire home. Furthermore, if the user is in a specific location, the confirmation unit can also prioritize displaying video related to that location. This allows highly relevant video to be quickly confirmed by considering the user's geographical location information. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input geographical location information data to the generation AI and cause the generation AI to prioritize displaying highly relevant video.

[0041] The confirmation unit can analyze the user's social media activity and display related videos when confirming the video. For example, the confirmation unit can analyze the user's social media activity and display related videos when confirming the video. For example, the confirmation unit can display videos related to places where the user has checked in on social media. The confirmation unit can also analyze the content of the user's social media posts and display related videos. The confirmation unit can also display related videos by referring to the activities of the user's friends on social media. In this way, related videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the confirmation unit can be performed using AI, for example, or without AI. For example, the confirmation unit can input social media activity data to a generation AI and cause the generation AI to display related videos.

[0042] The confirmation unit can customize the display method by reflecting the user's past feedback when confirming the video. The confirmation unit, for example, customizes the display method by reflecting the user's past feedback when confirming the video. For example, the confirmation unit customizes the video display method based on the user's preferred display method in the past. The confirmation unit can also provide an optimal display method for a specific time period based on the user's past feedback. The confirmation unit can also provide an optimal display method for a specific event based on the user's past feedback. In this way, the optimal video display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input past feedback data into the generation AI and cause the generation AI to customize the display method.

[0043] The detection unit can determine the detection priority based on a specific time period or location during detection. The detection unit can, for example, determine the detection priority based on a specific time period or location during detection. For example, the detection unit can prioritize detection of the entrance and windows at night. The detection unit can also prioritize detection of the living room during the daytime on weekdays. The detection unit can also prioritize detection of the entire home when the user is traveling. In this way, by determining the detection priority based on a specific time period or location, important detections can be performed quickly. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the time period and location into the generation AI and have the generation AI determine the detection priority.

[0044] The detection unit can select the optimal detection method by referring to the user's past detection history during detection. For example, the detection unit can select the optimal detection method by referring to the user's past detection history during detection. For example, the detection unit prioritizes detection of locations where the user has frequently detected in the past. The detection unit can also prioritize detection during a specific time period based on locations where the user has detected during the same time period. The detection unit can also select a location to prioritize detection during a specific event based on the user's past detection history. This allows the optimal detection method to be provided by referring to the user's past detection history. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input past detection history data into a generation AI and have the generation AI select the optimal detection method.

[0045] The detection unit can apply an anomaly detection algorithm to highlight an abnormal location upon detection. The detection unit can, for example, apply an anomaly detection algorithm to highlight an abnormal location upon detection. For example, if the detection unit detects an abnormal sound, it highlights the location. Furthermore, if the detection unit detects an abnormal movement, it can also highlight the location. Furthermore, if the detection unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the anomaly detection data to a generation AI and cause the generation AI to highlight the abnormal location.

[0046] The detection unit can prioritize highly relevant detections during detection by taking into account the user's geographical location information. For example, the detection unit can prioritize highly relevant detections during detection by taking into account the user's geographical location information. For example, when the user is close to their home, the detection unit prioritizes detection of the entrance or windows. Furthermore, when the user is far away, the detection unit can prioritize detection of the entire home. Furthermore, when the user is in a specific location, the detection unit can prioritize detections related to that location. In this way, highly relevant detections can be performed quickly by taking the user's geographical location information into account. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant detections.

[0047] The detection unit can analyze the user's social media activity and perform related detection at the time of detection. The detection unit, for example, can analyze the user's social media activity and perform related detection at the time of detection. For example, the detection unit can perform related detection based on locations where the user has checked in on social media. The detection unit can also analyze the content of the user's social media posts and perform related detection. The detection unit can also perform related detection based on the activities of the user's friends on social media. In this way, related detection can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input social media activity data to a generation AI and have the generation AI perform related detection.

[0048] The detection unit can customize the detection method by reflecting the user's past feedback at the time of detection. The detection unit, for example, customizes the detection method by reflecting the user's past feedback at the time of detection. For example, the detection unit customizes the detection method based on the user's preferred detection method in the past. The detection unit can also provide the optimal detection method for a specific time period based on the user's past feedback. The detection unit can also provide the optimal detection method for a specific event based on the user's past feedback. In this way, the optimal detection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0049] The reporting unit can determine the priority of a report based on a specific time period or location when making a report. The reporting unit, for example, determines the priority of a report based on a specific time period or location when making a report. For example, the reporting unit prioritizes reports about the front door or window at night. The reporting unit can also prioritize reports about the living room during the daytime on weekdays. The reporting unit can also prioritize reports about the entire home when the user is traveling. In this way, by determining the priority of a report based on a specific time period or location, important reports can be made quickly. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input data about the time period and location into the generation AI and have the generation AI determine the priority of the reports.

[0050] The reporting unit can select the optimal reporting method by referring to the user's past reporting history when making a report. The reporting unit, for example, selects the optimal reporting method by referring to the user's past reporting history when making a report. For example, the reporting unit prioritizes reporting locations that the user has frequently reported in the past. The reporting unit can also prioritize reporting during a specific time period based on locations that the user has reported during the same time period. The reporting unit can also select locations to prioritize reporting during a specific event from the user's past reporting history. In this way, the optimal reporting method can be provided by referring to the user's past reporting history. Some or all of the above-described processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input past reporting history data into a generation AI and cause the generation AI to select the optimal reporting method.

[0051] The reporting unit can apply an anomaly detection algorithm to highlight an abnormal location when reporting. The reporting unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when reporting. For example, when the reporting unit detects an abnormal sound, it highlights the location. Furthermore, when the reporting unit detects an abnormal movement, it can also highlight the location. Furthermore, when the reporting unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input the anomaly detection data to a generation AI and cause the generation AI to highlight the abnormal location.

[0052] When making a report, the reporting unit can prioritize highly relevant reports by taking into account the user's geographical location information. When making a report, the reporting unit can prioritize highly relevant reports by taking into account the user's geographical location information, for example. For example, when the user is close to their home, the reporting unit can prioritize reports about the front door or windows. Furthermore, when the user is far away, the reporting unit can prioritize reports about the entire home. Furthermore, when the user is in a specific location, the reporting unit can prioritize reports related to that location. In this way, by taking the user's geographical location information into account, highly relevant reports can be made quickly. Some or all of the above-described processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input geographical location information data to a generation AI and cause the generation AI to prioritize highly relevant reports.

[0053] The reporting unit can analyze the user's social media activity and issue a related report when a report is made. For example, the reporting unit can analyze the user's social media activity and issue a related report when a report is made. For example, the reporting unit can issue a report related to a location where the user checked in on social media. The reporting unit can also analyze the content of the user's social media posts and issue a related report. The reporting unit can also issue a related report by referring to the activity of the user's friends on social media. In this way, a related report can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input social media activity data into a generation AI and have the generation AI execute a related report.

[0054] The reporting unit can customize the reporting method by reflecting the user's past feedback when reporting. The reporting unit, for example, customizes the reporting method by reflecting the user's past feedback when reporting. For example, the reporting unit customizes the reporting method based on the user's preferred reporting method in the past. The reporting unit can also provide the optimal reporting method for a specific time period based on the user's past feedback. The reporting unit can also provide the optimal reporting method for a specific event based on the user's past feedback. In this way, the optimal reporting method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input past feedback data into a generation AI and cause the generation AI to customize the reporting method.

[0055] The alert unit can determine the priority of alerts based on a specific time period or location when issuing an alert. For example, the alert unit can prioritize alerts for the entrance and windows at night. The alert unit can also prioritize alerts for the living room during the daytime on weekdays. The alert unit can also prioritize alerts for the entire home when traveling. In this way, by determining the priority of alerts based on a specific time period or location, important alerts can be issued quickly. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input data on time period and location into the generation AI and have the generation AI determine the priority of alerts.

[0056] When issuing an alert, the alert unit can select the optimal alert method by referring to the user's past alert history. For example, when issuing an alert, the alert unit selects the optimal alert method by referring to the user's past alert history. For example, the alert unit prioritizes alerts for locations where the user has frequently received alerts in the past. The alert unit can also prioritize alerts for locations where the user received alerts during a specific time period based on the locations where the user received alerts during the same time period. The alert unit can also select locations to prioritize alerts for during a specific event from the user's past alert history. This allows the optimal alert method to be provided by referring to the user's past alert history. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input past alert history data into a generation AI and have the generation AI select the optimal alert method.

[0057] The alert unit can apply an anomaly detection algorithm to highlight an abnormal location when issuing an alert. The alert unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when issuing an alert. For example, if the alert unit detects an abnormal sound, it highlights the location. Furthermore, if the alert unit detects an abnormal movement, it can also highlight the location. Furthermore, if the alert unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0058] When issuing an alert, the alert unit can prioritize highly relevant alerts by taking into account the user's geographical location information. For example, when issuing an alert, the alert unit can prioritize highly relevant alerts by taking into account the user's geographical location information. For example, when the user is close to home, the alert unit prioritizes alerts for the entrance or windows. Furthermore, when the user is far away, the alert unit can prioritize alerts for the entire home. Furthermore, when the user is in a specific location, the alert unit can prioritize alerts related to that location. In this way, by taking the user's geographical location information into account, highly relevant alerts can be issued quickly. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input geographical location information data to a generation AI and cause the generation AI to prioritize highly relevant alerts.

[0059] The alert unit can analyze the user's social media activity and issue a related alert when issuing an alert. For example, the alert unit can analyze the user's social media activity and issue a related alert when issuing an alert. For example, the alert unit can issue an alert related to a location where the user checked in on social media. The alert unit can also analyze the content of the user's social media posts and issue a related alert. The alert unit can also issue a related alert based on the activity of the user's friends on social media. In this way, related alerts can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the alert unit can be performed using, for example, AI, or can be performed without using AI. For example, the alert unit can input social media activity data to a generation AI and have the generation AI execute a related alert.

[0060] The alert unit can customize the alert method by reflecting the user's past feedback when issuing an alert. For example, the alert unit customizes the alert method by reflecting the user's past feedback when issuing an alert. For example, the alert unit customizes the alert method based on the user's preferred alert method in the past. The alert unit can also provide the optimal alert method for a specific time period based on the user's past feedback. The alert unit can also provide the optimal alert method for a specific event based on the user's past feedback. In this way, the optimal alert method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input past feedback data into the generation AI and cause the generation AI to customize the alert method.

[0061] The dialogue unit can determine the priority of dialogues based on a specific time period or location during a dialogue. For example, the dialogue unit can prioritize dialogues based on a specific time period or location during a dialogue. For example, the dialogue unit can prioritize dialogues for the entrance or window at night. The dialogue unit can also prioritize dialogues for the living room during the daytime on weekdays. The dialogue unit can also prioritize dialogues for the entire home when traveling. In this way, by determining the priority of dialogues based on a specific time period or location, important dialogues can be carried out quickly. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the time period and location into the generation AI and have the generation AI determine the priority of dialogues.

[0062] The dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit prioritizes dialogue with locations where the user has frequently interacted in the past. The dialogue unit can also prioritize dialogue during a specific time period based on locations where the user has interacted during the same time period. The dialogue unit can also select a location to prioritize dialogue at the time of a specific event from the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past dialogue history data into a generation AI and cause the generation AI to select an optimal dialogue method.

[0063] The dialogue unit can apply an anomaly detection algorithm during dialogue to highlight an abnormal location. The dialogue unit, for example, applies an anomaly detection algorithm during dialogue to highlight an abnormal location. For example, if the dialogue unit detects an abnormal sound, it highlights the location. Furthermore, if the dialogue unit detects an abnormal movement, it can also highlight the location. Furthermore, if the dialogue unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0064] The dialogue unit can prioritize highly relevant dialogues during dialogue, taking into account the user's geographical location information. For example, the dialogue unit can prioritize highly relevant dialogues during dialogue, taking into account the user's geographical location information. For example, if the user is close to their home, the dialogue unit can prioritize dialogues about the entrance or windows. Furthermore, if the user is far away, the dialogue unit can also prioritize dialogues about the entire home. Furthermore, if the user is in a specific location, the dialogue unit can also prioritize dialogues related to that location. In this way, by taking the user's geographical location information into account, highly relevant dialogues can be quickly carried out. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input geographical location information data to the generation AI and cause the generation AI to prioritize execution of highly relevant dialogues.

[0065] The dialogue unit can analyze the user's social media activity during the dialogue and carry out a related dialogue. For example, the dialogue unit can analyze the user's social media activity during the dialogue and carry out a related dialogue. For example, the dialogue unit can carry out a dialogue related to a place where the user checked in on social media. The dialogue unit can also analyze the content of the user's social media posts and carry out a related dialogue. The dialogue unit can also carry out a related dialogue by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a related dialogue. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input social media activity data to a generation AI and have the generation AI carry out a related dialogue.

[0066] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. The dialogue unit, for example, customizes the dialogue method by reflecting the user's past feedback during dialogue. For example, the dialogue unit customizes the dialogue method based on the user's preferred dialogue methods in the past. The dialogue unit can also provide the optimal dialogue method for a specific time period based on the user's past feedback. The dialogue unit can also provide the optimal dialogue method for a specific event based on the user's past feedback. In this way, the optimal dialogue method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input past feedback data into a generation AI and cause the generation AI to customize the dialogue method.

[0067] The evaluation unit can determine the priority of evaluations based on specific time periods and locations during evaluation. For example, the evaluation unit can determine the priority of evaluations based on specific time periods and locations during evaluation. For example, the evaluation unit can prioritize evaluations of entrances and windows at night. The evaluation unit can also prioritize evaluations of the living room during the daytime on weekdays. The evaluation unit can also prioritize evaluations of the entire home when traveling. In this way, by prioritizing evaluations based on specific time periods and locations, important evaluations can be performed quickly. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on time periods and locations into the generation AI and have the generation AI determine the priority of evaluations.

[0068] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating. For example, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating. For example, the evaluation unit prioritizes evaluation of places that the user has frequently rated in the past. The evaluation unit can also prioritize evaluation of places that the user has rated during a specific time period based on the places that the user rated during the same time period. The evaluation unit can also select places to prioritize evaluation during a specific event from the user's past evaluation history. In this way, the optimal evaluation method can be provided by referring to the user's past evaluation history. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation history data into a generation AI and cause the generation AI to select the optimal evaluation method.

[0069] The evaluation unit can apply an anomaly detection algorithm to highlight an abnormal location during evaluation. The evaluation unit can, for example, apply an anomaly detection algorithm to highlight an abnormal location during evaluation. For example, if the evaluation unit detects an abnormal sound, it highlights the location. Furthermore, if the evaluation unit detects an abnormal movement, it can also highlight the location. Furthermore, if the evaluation unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the anomaly detection data to the generation AI and cause the generation AI to highlight the abnormal location.

[0070] The evaluation unit can prioritize highly relevant evaluations during evaluation by taking into account the user's geographical location information. For example, the evaluation unit can prioritize highly relevant evaluations during evaluation by taking into account the user's geographical location information. For example, if the user is close to their home, the evaluation unit can prioritize evaluations of the entrance and windows. Furthermore, if the user is far away, the evaluation unit can prioritize evaluations of the entire home. Furthermore, if the user is in a specific location, the evaluation unit can prioritize evaluations related to that location. In this way, by taking the user's geographical location information into account, highly relevant evaluations can be made quickly. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant evaluations.

[0071] The evaluation unit can analyze the user's social media activity and provide a related evaluation during the evaluation. For example, the evaluation unit can analyze the user's social media activity and provide a related evaluation during the evaluation. For example, the evaluation unit can provide an evaluation related to places where the user checked in on social media. The evaluation unit can also analyze the content of the user's social media posts and provide a related evaluation. The evaluation unit can also provide a related evaluation by referring to the activities of the user's friends on social media. In this way, the related evaluation can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input social media activity data into a generation AI and have the generation AI perform a related evaluation.

[0072] The evaluation unit can customize the evaluation method by reflecting the user's past feedback at the time of evaluation. The evaluation unit, for example, customizes the evaluation method by reflecting the user's past feedback at the time of evaluation. For example, the evaluation unit customizes the evaluation method based on the user's past preferred evaluation method. The evaluation unit can also provide an optimal evaluation method for a specific time period based on the user's past feedback. The evaluation unit can also provide an optimal evaluation method for a specific event based on the user's past feedback. In this way, the optimal evaluation method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input past feedback data into the generation AI and cause the generation AI to customize the evaluation method.

[0073] The suggestion unit can determine the priority of suggestions based on a specific time period or location when making suggestions. For example, the suggestion unit can prioritize suggestions for the entrance or windows at night. The suggestion unit can also prioritize suggestions for the living room during the daytime on weekdays. The suggestion unit can also prioritize suggestions for the entire home when traveling. In this way, by prioritizing suggestions based on a specific time period or location, important suggestions can be made quickly. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on time periods and locations into the generation AI and have the generation AI determine the priority of suggestions.

[0074] The suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making a suggestion. For example, the suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making a suggestion. For example, the suggestion unit prioritizes suggesting locations for which the user has frequently received suggestions in the past. The suggestion unit can also prioritize suggestions for locations for which the user has received suggestions during a specific time period based on the locations for which the user has received suggestions during the same time period. The suggestion unit can also select locations to prioritize suggestions for during a specific event from the user's past suggestion history. In this way, the optimal suggestion method can be provided by referring to the user's past suggestion history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past suggestion history data into a generation AI and cause the generation AI to select the optimal suggestion method.

[0075] The suggestion unit can apply an anomaly detection algorithm to highlight an abnormal location when making a suggestion. The suggestion unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when making a suggestion. For example, if the suggestion unit detects an abnormal sound, it highlights the location. Furthermore, if the suggestion unit detects an abnormal movement, it can also highlight the location. Furthermore, if the suggestion unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0076] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when the user is close to their home, the suggestion unit can prioritize suggestions for the entrance or windows. Furthermore, when the user is far away, the suggestion unit can prioritize suggestions for the entire home. Furthermore, when the user is in a specific location, the suggestion unit can prioritize suggestions related to that location. In this way, highly relevant suggestions can be made quickly by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant suggestions.

[0077] The suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can make suggestions related to places the user has checked in to on social media. The suggestion unit can also analyze the content of the user's social media posts and make related suggestions. The suggestion unit can also make related suggestions by referring to the activities of the user's friends on social media. In this way, related suggestions can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input social media activity data into a generation AI and have the generation AI execute related suggestions.

[0078] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method based on suggestion methods that the user has previously preferred. The suggestion unit can also provide a suggestion method that is optimal for a specific time period based on the user's past feedback. The suggestion unit can also provide a suggestion method that is optimal for a specific event based on the user's past feedback. In this way, the optimal suggestion method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past feedback data into a generation AI and cause the generation AI to customize the suggestion method.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The security system can also be equipped with a voice recognition unit. The voice recognition unit can analyze sounds within the residence in real time and detect abnormal sounds. For example, the voice recognition unit can detect the sound of glass breaking or a door being forcibly opened and closed and notify the user. The voice recognition unit can also recognize the resident's voice and operate the system based on specific voice commands. For example, if a resident shouts "emergency," the system can automatically notify the police. This means that adding voice recognition functionality can further strengthen security.

[0081] The detection unit can further include an environmental sensor. The environmental sensor can acquire environmental data such as temperature, humidity, and carbon dioxide concentration in real time and detect abnormalities. For example, if the detection unit detects an abnormal rise in temperature, it can notify the user of the possibility of a fire. The detection unit can also notify the user to ventilate if the carbon dioxide concentration becomes high. In this way, adding an environmental sensor can further improve safety within the home.

[0082] The alert unit can further include a vibration sensor. The vibration sensor can detect vibrations within the home and issue an alert if an abnormal vibration is detected. For example, the alert unit can detect vibrations caused by breaking a window and notify the user. The alert unit can also detect vibrations caused by forcibly opening or closing a door and issue a warning. Thus, adding a vibration sensor can strengthen security against physical intrusion.

[0083] The evaluation unit can also refer to past crime data to perform security evaluations. For example, the evaluation unit can analyze past crime data for a region and propose enhanced crime prevention measures for areas where specific crimes occur frequently. The evaluation unit can also perform risk evaluations for specific time periods based on past crime data. This makes it possible to utilize past crime data to perform more accurate security evaluations and propose crime prevention measures.

[0084] The confirmation unit may further include a voice assistant function. The voice assistant function can display security camera footage or switch to a specific camera in response to a user's voice command. For example, if the user says, "Show the camera at the front door," the confirmation unit may preferentially display footage from the front door. Also, if the user says, "Show all cameras," the confirmation unit may display footage from all cameras at once. This allows for improved user convenience by adding the voice assistant function.

[0085] The notification unit can also have a function for managing a list of emergency contacts. For example, the notification unit can automatically notify emergency contacts registered by the user in advance in the event of an emergency. The notification unit can also set priorities for emergency contacts and prioritize notifications to the most important contacts. This allows for a quick and appropriate response in the event of an emergency.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The checking unit checks the security camera footage in real time. For example, the checking unit can check the security camera footage in real time from a smartphone. The checking unit can also set the video resolution and checking frequency. For example, the checking unit can provide high-resolution video and allow the user to enlarge the video as needed. Step 2: The detection unit detects fires and gas leaks based on the video confirmed by the confirmation unit. For example, the detection unit detects fires and gas leaks in cooperation with a security sensor. The detection unit can set the detection method and type of sensor for fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or gas sensor. Step 3: The notification unit notifies the police or fire department based on the emergency detected by the detection unit. For example, the notification unit automatically notifies the police or fire department in the event of an emergency. The notification unit can set the timing of the notification and the information on the notification destination. For example, the notification unit automatically notifies the fire department in the event of a fire. Step 4: The alert unit recognizes the faces of family members and authorized individuals and issues an alert if a suspicious individual enters the residence. For example, the alert unit learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual enters the residence. The alert unit can set the facial recognition algorithm and recognition accuracy. For example, the alert unit detects suspicious individuals using a highly accurate facial recognition algorithm. Step 5: The dialogue unit conducts a voice dialogue if a suspicious individual breaks into the building while the resident is away. For example, the dialogue unit conducts a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, if a suspicious individual breaks into the entrance, the dialogue unit can play a message such as "This area is guarded. Please leave immediately."

[0088] (Example 2) A mobile app according to an embodiment of the present invention is a system for enhancing home security and ensuring the safety of residents and their homes. The mobile app monitors security camera footage in real time, detects fires and gas leaks, and notifies the police and fire department in emergencies. It also learns the faces of family members and authorized individuals and issues an alert if a suspicious individual breaks into the residence. Furthermore, it engages in virtual conversations if a suspicious individual breaks into the residence while the resident is away. For example, the mobile app allows users to view security camera footage in real time from their smartphone. For example, users can check the status of their home while away from home. It also works with security sensors to detect fires and gas leaks. For example, if a gas leak occurs in the kitchen, the sensor detects it and notifies the user. Furthermore, it automatically notifies the police and fire department in emergencies. For example, if a fire breaks out, the app automatically notifies the fire department. The app then learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual breaks into the residence. For example, if a non-family member is standing at the front door, the app detects it and notifies the user. Furthermore, if a suspicious individual breaks into the home while the residents are away, a virtual conversation will be held through the speaker. For example, if a suspicious individual breaks into the front door, a message will be played over the speaker saying, "This area is under security. Please leave immediately." The app also evaluates the home's security based on photos and floor plan, suggesting appropriate security measures. For example, it suggests the best locations for security cameras based on the location of windows and doors. Furthermore, the app obtains the latest local information and suggests optimal disaster prevention measures and evacuation routes. For example, in the event of an earthquake, the app notifies the user of the optimal evacuation route. This allows the mobile app to enhance home security and ensure the safety of residents and their homes. For example, the app allows users to check the status of their home while away from home, quickly detects fires and gas leaks, and automatically reports emergencies. Furthermore, if a suspicious individual breaks into the home, an alert will be sent and a virtual conversation will be held to intimidate the intruder and prevent them from entering. Furthermore, the app performs a security assessment of the home and suggests appropriate security measures, improving the safety of the home.By proposing optimal disaster prevention measures and evacuation routes based on the latest local information, safety can be ensured in the event of a disaster.

[0089] A security system according to an embodiment includes a confirmation unit, a detection unit, a notification unit, an alert unit, and a dialogue unit. The confirmation unit checks security camera footage in real time. For example, the confirmation unit can check security camera footage in real time from a smartphone. The confirmation unit can also set the resolution of the footage and the frequency of checking. For example, the confirmation unit provides high-resolution footage, allowing the user to enlarge the footage as needed. The detection unit detects fires and gas leaks based on the footage checked by the confirmation unit. For example, the detection unit works in conjunction with a security sensor to detect fires and gas leaks. The detection unit can set the detection method and type of sensor for fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or a gas sensor. The notification unit notifies the police or fire department based on an emergency detected by the detection unit. For example, the notification unit automatically notifies the police or fire department in an emergency. The notification unit can set the timing of the notification and information on the destination of the notification. For example, the notification unit automatically notifies the fire department in the event of a fire. The alert unit recognizes the faces of family members and authorized individuals and issues an alert if a suspicious individual breaks into the residence. For example, the alert unit learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual breaks into the residence. The alert unit can set a facial recognition algorithm and recognition accuracy. For example, the alert unit detects a suspicious individual using a highly accurate facial recognition algorithm. The dialogue unit engages in a voice dialogue if a suspicious individual breaks into the residence when no resident is present. For example, the dialogue unit engages in a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, if a suspicious individual breaks into the front door, the dialogue unit plays a message such as "This area is guarded. Please leave immediately." As a result, the security system according to the embodiment can strengthen the security of the residence and ensure the safety of the residents and the residence by checking security camera footage, detecting fires and gas leaks, reporting emergencies, issuing suspicious individual alerts, and conducting virtual dialogue.

[0090] The confirmation unit can check security camera footage in real time from a smartphone. The confirmation unit can, for example, check security camera footage in real time from a smartphone. For example, the confirmation unit can check the status of one's home while away from home. The confirmation unit can also set the resolution of the footage and the frequency of checking. For example, the confirmation unit can provide high-resolution footage and allow the user to enlarge the footage as needed. This allows one to check the status of one's home while away from home by checking security camera footage in real time from a smartphone. Some or all of the above-described processing in the confirmation unit may be performed using, or without, AI, for example. For example, the confirmation unit can input video data acquired from a smartphone to a generation AI and have the generation AI analyze the video data.

[0091] The detection unit can cooperate with a security sensor to detect fires and gas leaks. The detection unit, for example, cooperates with a security sensor to detect fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or a gas sensor. The detection unit can also set the detection method and type of sensor for fires and gas leaks. For example, the detection unit can adjust the sensitivity of the smoke sensor to perform early fire detection. The detection unit can also select the type of gas sensor to detect specific gas leaks. This allows for rapid detection of fires and gas leaks by working with the security sensor. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data obtained from the security sensor into the generation AI and cause the generation AI to detect fires and gas leaks.

[0092] The reporting unit can automatically notify the police or fire department in an emergency. The reporting unit, for example, automatically notifies the police or fire department in an emergency. For example, the reporting unit automatically notifies the fire department in the event of a fire. The reporting unit can also set the timing of the report and the information on the reporting destination. For example, the reporting unit can make an immediate report in the event of a fire. The reporting unit can also register the information on the reporting destination in advance and make a prompt report. This enables a prompt response by automatically reporting in an emergency. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input emergency data into a generation AI and have the generation AI execute the report.

[0093] The alert unit can recognize the faces of family members or authorized individuals and send an alert to the user if a suspicious individual breaks into the residence. For example, the alert unit can recognize the faces of family members or authorized individuals and send an alert to the user if a suspicious individual breaks into the residence. For example, the alert unit can detect when a person other than a family member is standing at the front door and notify the user. The alert unit can also set the facial recognition algorithm and recognition accuracy. For example, the alert unit can detect suspicious individuals using a highly accurate facial recognition algorithm. The alert unit can also adjust the recognition accuracy to prevent false recognition. This enhances security by quickly sending an alert to the user if a suspicious individual breaks into the residence. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input facial recognition data into a generation AI and have the generation AI detect suspicious individuals.

[0094] The dialogue unit can conduct a voice dialogue when a suspicious individual breaks into a building when no resident is present. The dialogue unit conducts a voice dialogue when a suspicious individual breaks into a building when no resident is present. For example, the dialogue unit conducts a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, when a suspicious individual breaks into the entrance, the dialogue unit can play a message such as "This area is guarded. Please leave immediately." The dialogue unit can also adjust the tone of the dialogue to enhance the intimidating effect. This allows a virtual dialogue to be conducted when a suspicious individual breaks into a building when no resident is present, thereby intimidating the suspicious individual and preventing them from breaking in. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input voice data into a generation AI and have the generation AI execute a virtual dialogue.

[0095] The security system includes an evaluation unit that performs a security evaluation of a residence and proposes appropriate crime prevention measures. For example, when a user inputs photos and floor plan data of the user's home into an app, the evaluation unit performs a security evaluation of the residence and proposes appropriate crime prevention measures. For example, the evaluation unit proposes locations for installing security cameras based on the positions of windows and doors. The evaluation unit can also set criteria and evaluation items for the security evaluation of the residence. For example, the evaluation unit sets evaluation items such as the number of windows, the positions of doors, and the surrounding environment to perform a comprehensive security evaluation. This allows the security evaluation of the residence to be performed and appropriate crime prevention measures to be proposed, thereby improving the safety of the residence. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the photos and floor plan data input by the user into a generation AI and have the generation AI perform a security evaluation.

[0096] The security system includes a proposal unit that acquires the latest local information and proposes optimal disaster countermeasures and evacuation routes. The proposal unit, for example, acquires the latest local information and proposes optimal disaster countermeasures and evacuation routes. For example, the proposal unit notifies the user of the optimal evacuation route when an earthquake occurs. The proposal unit can also set a method for acquiring local information and proposal criteria. For example, the proposal unit acquires meteorological information and disaster information in real time and proposes the optimal evacuation route based on the information. This makes it possible to ensure safety in the event of a disaster by proposing optimal disaster countermeasures and evacuation routes based on the latest local information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input local information data to a generation AI and cause the generation AI to propose disaster countermeasures and evacuation routes.

[0097] The confirmation unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, the confirmation unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, if the user is nervous, the confirmation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the confirmation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can provide a display method that focuses on the main points. This allows the video display method to be optimized for the user by adjusting the video display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the confirmation unit can be performed using, for example, an AI, or without an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the video display method.

[0098] The confirmation unit can prioritize display of video based on a specific time period or location when checking video. The confirmation unit, for example, prioritizes display of video based on a specific time period or location when checking video. For example, the confirmation unit can prioritize display of video of the entrance or windows at night. The confirmation unit can also prioritize display of video of the living room during the daytime on weekdays. The confirmation unit can also prioritize display of video of the entire home when traveling. In this way, by prioritizing display of video based on a specific time period or location, important video can be quickly checked. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input data on the time period and location into the generation AI and cause the generation AI to prioritize display of video.

[0099] The confirmation unit can select the optimal display method by referring to the user's past confirmation history when checking video. For example, the confirmation unit selects the optimal display method by referring to the user's past confirmation history when checking video. For example, the confirmation unit may prioritize displaying video from cameras that the user has frequently checked in the past. The confirmation unit may also prioritize displaying video during a specific time period based on video checked by the user during the same time period. The confirmation unit may also select video to prioritize display during a specific event based on the user's past confirmation history. In this way, the optimal video display method can be provided by referring to the user's past confirmation history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input past confirmation history data into a generation AI and cause the generation AI to select the optimal display method.

[0100] The confirmation unit can apply an anomaly detection algorithm to highlight abnormal areas when checking the video. The confirmation unit, for example, applies an anomaly detection algorithm to highlight abnormal areas when checking the video. For example, if the confirmation unit detects an abnormal sound, it highlights the area. Furthermore, if the confirmation unit detects an abnormal movement, it can also highlight the area. Furthermore, if the confirmation unit detects an abnormal temperature change, it can also highlight the area. In this way, by applying the anomaly detection algorithm, abnormal areas can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the anomaly detection data to the generation AI and cause the generation AI to highlight the abnormal areas.

[0101] The confirmation unit can estimate the user's emotion and automatically start recording video based on the estimated user's emotion. The confirmation unit, for example, estimates the user's emotion and automatically starts recording video based on the estimated user's emotion. For example, the confirmation unit automatically starts recording video when the user is feeling anxious. The confirmation unit can also automatically start recording video when the user is nervous. The confirmation unit can also automatically start recording video when the user is excited. This allows important video to be recorded without missing by automatically starting video recording according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the confirmation unit may be performed using an AI, for example, or without an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and cause the generation AI to start recording video.

[0102] The confirmation unit can prioritize displaying highly relevant video in consideration of the user's geographical location information when confirming video. For example, the confirmation unit prioritizes displaying highly relevant video in consideration of the user's geographical location information when confirming video. For example, if the user is close to their home, the confirmation unit can prioritize displaying video of the entrance or windows. Furthermore, if the user is far away, the confirmation unit can also prioritize displaying video of the entire home. Furthermore, if the user is in a specific location, the confirmation unit can also prioritize displaying video related to that location. This allows highly relevant video to be quickly confirmed by considering the user's geographical location information. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input geographical location information data to the generation AI and cause the generation AI to prioritize displaying highly relevant video.

[0103] The confirmation unit can analyze the user's social media activity and display related videos when confirming the video. For example, the confirmation unit can analyze the user's social media activity and display related videos when confirming the video. For example, the confirmation unit can display videos related to places where the user has checked in on social media. The confirmation unit can also analyze the content of the user's social media posts and display related videos. The confirmation unit can also display related videos by referring to the activities of the user's friends on social media. In this way, related videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the confirmation unit can be performed using AI, for example, or without AI. For example, the confirmation unit can input social media activity data to a generation AI and cause the generation AI to display related videos.

[0104] The confirmation unit can customize the display method by reflecting the user's past feedback when confirming the video. The confirmation unit, for example, customizes the display method by reflecting the user's past feedback when confirming the video. For example, the confirmation unit customizes the video display method based on the user's preferred display method in the past. The confirmation unit can also provide an optimal display method for a specific time period based on the user's past feedback. The confirmation unit can also provide an optimal display method for a specific event based on the user's past feedback. In this way, the optimal video display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input past feedback data into the generation AI and cause the generation AI to customize the display method.

[0105] The detection unit can estimate the user's emotion and adjust the detection sensitivity based on the estimated user's emotion. For example, the detection unit can estimate the user's emotion and adjust the detection sensitivity based on the estimated user's emotion. For example, the detection unit can increase the detection sensitivity when the user is feeling anxious. The detection unit can also return the detection sensitivity to normal when the user is relaxed. The detection unit can also increase the detection sensitivity when the user is excited. This enables optimal detection by adjusting the detection sensitivity according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the detection unit can be performed using an AI, for example, or without an AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the detection sensitivity.

[0106] The detection unit can determine the detection priority based on a specific time period or location during detection. The detection unit can, for example, determine the detection priority based on a specific time period or location during detection. For example, the detection unit can prioritize detection of the entrance and windows at night. The detection unit can also prioritize detection of the living room during the daytime on weekdays. The detection unit can also prioritize detection of the entire home when the user is traveling. In this way, by determining the detection priority based on a specific time period or location, important detections can be performed quickly. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the time period and location into the generation AI and have the generation AI determine the detection priority.

[0107] The detection unit can select the optimal detection method by referring to the user's past detection history during detection. For example, the detection unit can select the optimal detection method by referring to the user's past detection history during detection. For example, the detection unit prioritizes detection of locations where the user has frequently detected in the past. The detection unit can also prioritize detection during a specific time period based on locations where the user has detected during the same time period. The detection unit can also select a location to prioritize detection during a specific event based on the user's past detection history. This allows the optimal detection method to be provided by referring to the user's past detection history. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input past detection history data into a generation AI and have the generation AI select the optimal detection method.

[0108] The detection unit can apply an anomaly detection algorithm to highlight an abnormal location upon detection. The detection unit can, for example, apply an anomaly detection algorithm to highlight an abnormal location upon detection. For example, if the detection unit detects an abnormal sound, it highlights the location. Furthermore, if the detection unit detects an abnormal movement, it can also highlight the location. Furthermore, if the detection unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the anomaly detection data to a generation AI and cause the generation AI to highlight the abnormal location.

[0109] The detection unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the detection unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the detection unit can provide an immediate notification if the user is feeling anxious. The detection unit can also use a normal notification method if the user is relaxed. The detection unit can also provide an immediate notification if the user is excited. This allows for optimal notification by adjusting the notification method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the detection unit can be performed using an AI, for example, or without an AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0110] The detection unit can prioritize highly relevant detections during detection by taking into account the user's geographical location information. For example, the detection unit can prioritize highly relevant detections during detection by taking into account the user's geographical location information. For example, when the user is close to their home, the detection unit prioritizes detection of the entrance or windows. Furthermore, when the user is far away, the detection unit can prioritize detection of the entire home. Furthermore, when the user is in a specific location, the detection unit can prioritize detections related to that location. In this way, highly relevant detections can be performed quickly by taking the user's geographical location information into account. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant detections.

[0111] The detection unit can analyze the user's social media activity and perform related detection at the time of detection. The detection unit, for example, can analyze the user's social media activity and perform related detection at the time of detection. For example, the detection unit can perform related detection based on locations where the user has checked in on social media. The detection unit can also analyze the content of the user's social media posts and perform related detection. The detection unit can also perform related detection based on the activities of the user's friends on social media. In this way, related detection can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input social media activity data to a generation AI and have the generation AI perform related detection.

[0112] The detection unit can customize the detection method by reflecting the user's past feedback at the time of detection. The detection unit, for example, customizes the detection method by reflecting the user's past feedback at the time of detection. For example, the detection unit customizes the detection method based on the user's preferred detection method in the past. The detection unit can also provide the optimal detection method for a specific time period based on the user's past feedback. The detection unit can also provide the optimal detection method for a specific event based on the user's past feedback. In this way, the optimal detection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0113] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. The notification unit, for example, estimates the user's emotions and adjusts the timing of the notification based on the estimated user emotions. For example, the notification unit makes an immediate notification when the user is feeling anxious. The notification unit can also use a normal notification timing when the user is relaxed. The notification unit can also make an immediate notification when the user is excited. This allows the notification to be made at the optimal timing by adjusting the timing of the notification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned 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 user's emotion data into the generation AI and cause the generation AI to adjust the timing of the notification.

[0114] The reporting unit can determine the priority of a report based on a specific time period or location when making a report. The reporting unit, for example, determines the priority of a report based on a specific time period or location when making a report. For example, the reporting unit prioritizes reports about the front door or window at night. The reporting unit can also prioritize reports about the living room during the daytime on weekdays. The reporting unit can also prioritize reports about the entire home when the user is traveling. In this way, by determining the priority of a report based on a specific time period or location, important reports can be made quickly. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input data about the time period and location into the generation AI and have the generation AI determine the priority of the reports.

[0115] The reporting unit can select the optimal reporting method by referring to the user's past reporting history when making a report. The reporting unit, for example, selects the optimal reporting method by referring to the user's past reporting history when making a report. For example, the reporting unit prioritizes reporting locations that the user has frequently reported in the past. The reporting unit can also prioritize reporting during a specific time period based on locations that the user has reported during the same time period. The reporting unit can also select locations to prioritize reporting during a specific event from the user's past reporting history. In this way, the optimal reporting method can be provided by referring to the user's past reporting history. Some or all of the above-described processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input past reporting history data into a generation AI and cause the generation AI to select the optimal reporting method.

[0116] The reporting unit can apply an anomaly detection algorithm to highlight an abnormal location when reporting. The reporting unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when reporting. For example, when the reporting unit detects an abnormal sound, it highlights the location. Furthermore, when the reporting unit detects an abnormal movement, it can also highlight the location. Furthermore, when the reporting unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input the anomaly detection data to a generation AI and cause the generation AI to highlight the abnormal location.

[0117] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. The notification unit, for example, estimates the user's emotions and adjusts the content of the notification based on the estimated user emotions. For example, the notification unit provides detailed notification content when the user is feeling anxious. The notification unit can also provide normal notification content when the user is relaxed. The notification unit can also provide detailed notification content when the user is excited. This allows the optimal notification content to be provided by adjusting the content of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input user emotion data into the generation AI and cause the generation AI to adjust the content of the notification.

[0118] When making a report, the reporting unit can prioritize highly relevant reports by taking into account the user's geographical location information. When making a report, the reporting unit can prioritize highly relevant reports by taking into account the user's geographical location information, for example. For example, when the user is close to their home, the reporting unit can prioritize reports about the front door or windows. Furthermore, when the user is far away, the reporting unit can prioritize reports about the entire home. Furthermore, when the user is in a specific location, the reporting unit can prioritize reports related to that location. In this way, by taking the user's geographical location information into account, highly relevant reports can be made quickly. Some or all of the above-described processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input geographical location information data to a generation AI and cause the generation AI to prioritize highly relevant reports.

[0119] The reporting unit can analyze the user's social media activity and issue a related report when a report is made. For example, the reporting unit can analyze the user's social media activity and issue a related report when a report is made. For example, the reporting unit can issue a report related to a location where the user checked in on social media. The reporting unit can also analyze the content of the user's social media posts and issue a related report. The reporting unit can also issue a related report by referring to the activity of the user's friends on social media. In this way, a related report can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input social media activity data into a generation AI and have the generation AI execute a related report.

[0120] The reporting unit can customize the reporting method by reflecting the user's past feedback when reporting. The reporting unit, for example, customizes the reporting method by reflecting the user's past feedback when reporting. For example, the reporting unit customizes the reporting method based on the user's preferred reporting method in the past. The reporting unit can also provide the optimal reporting method for a specific time period based on the user's past feedback. The reporting unit can also provide the optimal reporting method for a specific event based on the user's past feedback. In this way, the optimal reporting method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input past feedback data into a generation AI and cause the generation AI to customize the reporting method.

[0121] The alert unit can estimate the user's emotions and adjust the alert display method based on the estimated user emotions. For example, the alert unit can estimate the user's emotions and adjust the alert display method based on the estimated user emotions. For example, if the user is nervous, the alert unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the alert unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the alert unit can provide a display method that focuses on the main points. This allows for optimal alert display by adjusting the alert display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the alert unit can be performed using, for example, an AI, or without an AI. For example, the alert unit can input the user's emotion data into the generation AI and have the generation AI adjust the alert display method.

[0122] The alert unit can determine the priority of alerts based on a specific time period or location when issuing an alert. For example, the alert unit can prioritize alerts for the entrance and windows at night. The alert unit can also prioritize alerts for the living room during the daytime on weekdays. The alert unit can also prioritize alerts for the entire home when traveling. In this way, by determining the priority of alerts based on a specific time period or location, important alerts can be issued quickly. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input data on time period and location into the generation AI and have the generation AI determine the priority of alerts.

[0123] When issuing an alert, the alert unit can select the optimal alert method by referring to the user's past alert history. For example, when issuing an alert, the alert unit selects the optimal alert method by referring to the user's past alert history. For example, the alert unit prioritizes alerts for locations where the user has frequently received alerts in the past. The alert unit can also prioritize alerts for locations where the user received alerts during a specific time period based on the locations where the user received alerts during the same time period. The alert unit can also select locations to prioritize alerts for during a specific event from the user's past alert history. This allows the optimal alert method to be provided by referring to the user's past alert history. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input past alert history data into a generation AI and have the generation AI select the optimal alert method.

[0124] The alert unit can apply an anomaly detection algorithm to highlight an abnormal location when issuing an alert. The alert unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when issuing an alert. For example, if the alert unit detects an abnormal sound, it highlights the location. Furthermore, if the alert unit detects an abnormal movement, it can also highlight the location. Furthermore, if the alert unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0125] The alert unit can estimate the user's emotion and adjust the content of the alert based on the estimated user's emotion. For example, the alert unit can estimate the user's emotion and adjust the content of the alert based on the estimated user's emotion. For example, the alert unit can provide detailed alert content when the user is feeling anxious. The alert unit can also provide normal alert content when the user is relaxed. The alert unit can also provide detailed alert content when the user is excited. This allows the alert content to be adjusted according to the user's emotion, thereby providing optimal alert content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the alert unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the alert unit can input the user's emotion data into the generation AI and have the generation AI adjust the alert content.

[0126] When issuing an alert, the alert unit can prioritize highly relevant alerts by taking into account the user's geographical location information. For example, when issuing an alert, the alert unit can prioritize highly relevant alerts by taking into account the user's geographical location information. For example, when the user is close to home, the alert unit prioritizes alerts for the entrance or windows. Furthermore, when the user is far away, the alert unit can prioritize alerts for the entire home. Furthermore, when the user is in a specific location, the alert unit can prioritize alerts related to that location. In this way, by taking the user's geographical location information into account, highly relevant alerts can be issued quickly. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input geographical location information data to a generation AI and cause the generation AI to prioritize highly relevant alerts.

[0127] The alert unit can analyze the user's social media activity and issue a related alert when issuing an alert. For example, the alert unit can analyze the user's social media activity and issue a related alert when issuing an alert. For example, the alert unit can issue an alert related to a location where the user checked in on social media. The alert unit can also analyze the content of the user's social media posts and issue a related alert. The alert unit can also issue a related alert based on the activity of the user's friends on social media. In this way, related alerts can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the alert unit can be performed using, for example, AI, or can be performed without using AI. For example, the alert unit can input social media activity data to a generation AI and have the generation AI execute a related alert.

[0128] The alert unit can customize the alert method by reflecting the user's past feedback when issuing an alert. For example, the alert unit customizes the alert method by reflecting the user's past feedback when issuing an alert. For example, the alert unit customizes the alert method based on the user's preferred alert method in the past. The alert unit can also provide the optimal alert method for a specific time period based on the user's past feedback. The alert unit can also provide the optimal alert method for a specific event based on the user's past feedback. In this way, the optimal alert method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input past feedback data into the generation AI and cause the generation AI to customize the alert method.

[0129] The dialogue unit can estimate the user's emotions and adjust the dialogue content based on the estimated user emotions. For example, the dialogue unit can estimate the user's emotions and adjust the dialogue content based on the estimated user emotions. For example, if the user is feeling anxious, the dialogue unit can provide dialogue content that gives the user a sense of security. Furthermore, if the user is relaxed, the dialogue unit can provide normal dialogue content. Furthermore, if the user is excited, the dialogue unit can provide dialogue content that encourages the user to stay calm. By adjusting the dialogue content according to the user's emotions, optimal dialogue content can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the dialogue unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the dialogue unit can input the user's emotion data into the generation AI and have the generation AI adjust the dialogue content.

[0130] The dialogue unit can determine the priority of dialogues based on a specific time period or location during a dialogue. For example, the dialogue unit can prioritize dialogues based on a specific time period or location during a dialogue. For example, the dialogue unit can prioritize dialogues for the entrance or window at night. The dialogue unit can also prioritize dialogues for the living room during the daytime on weekdays. The dialogue unit can also prioritize dialogues for the entire home when traveling. In this way, by determining the priority of dialogues based on a specific time period or location, important dialogues can be carried out quickly. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the time period and location into the generation AI and have the generation AI determine the priority of dialogues.

[0131] The dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit prioritizes dialogue with locations where the user has frequently interacted in the past. The dialogue unit can also prioritize dialogue during a specific time period based on locations where the user has interacted during the same time period. The dialogue unit can also select a location to prioritize dialogue at the time of a specific event from the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past dialogue history data into a generation AI and cause the generation AI to select an optimal dialogue method.

[0132] The dialogue unit can apply an anomaly detection algorithm during dialogue to highlight an abnormal location. The dialogue unit, for example, applies an anomaly detection algorithm during dialogue to highlight an abnormal location. For example, if the dialogue unit detects an abnormal sound, it highlights the location. Furthermore, if the dialogue unit detects an abnormal movement, it can also highlight the location. Furthermore, if the dialogue unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0133] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated user emotions. For example, the dialogue unit estimates the user's emotions and adjusts the tone of the dialogue based on the estimated user emotions. For example, if the user is feeling anxious, the dialogue unit may use a calm tone. If the user is relaxed, the dialogue unit may use a bright tone. If the user is excited, the dialogue unit may use a calm tone. This allows the dialogue tone to be adjusted according to the user's emotions, thereby providing an optimal dialogue tone. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the dialogue unit may be performed using an AI, or may be performed without an AI. For example, the dialogue unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the dialogue tone.

[0134] The dialogue unit can prioritize highly relevant dialogues during dialogue, taking into account the user's geographical location information. For example, the dialogue unit can prioritize highly relevant dialogues during dialogue, taking into account the user's geographical location information. For example, if the user is close to their home, the dialogue unit can prioritize dialogues about the entrance or windows. Furthermore, if the user is far away, the dialogue unit can also prioritize dialogues about the entire home. Furthermore, if the user is in a specific location, the dialogue unit can also prioritize dialogues related to that location. In this way, by taking the user's geographical location information into account, highly relevant dialogues can be quickly carried out. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input geographical location information data to the generation AI and cause the generation AI to prioritize execution of highly relevant dialogues.

[0135] The dialogue unit can analyze the user's social media activity during the dialogue and carry out a related dialogue. For example, the dialogue unit can analyze the user's social media activity during the dialogue and carry out a related dialogue. For example, the dialogue unit can carry out a dialogue related to a place where the user checked in on social media. The dialogue unit can also analyze the content of the user's social media posts and carry out a related dialogue. The dialogue unit can also carry out a related dialogue by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a related dialogue. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input social media activity data to a generation AI and have the generation AI carry out a related dialogue.

[0136] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. The dialogue unit, for example, customizes the dialogue method by reflecting the user's past feedback during dialogue. For example, the dialogue unit customizes the dialogue method based on the user's preferred dialogue methods in the past. The dialogue unit can also provide the optimal dialogue method for a specific time period based on the user's past feedback. The dialogue unit can also provide the optimal dialogue method for a specific event based on the user's past feedback. In this way, the optimal dialogue method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input past feedback data into a generation AI and cause the generation AI to customize the dialogue method.

[0137] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can apply strict evaluation criteria when the user is feeling anxious. The evaluation unit can also apply normal evaluation criteria when the user is relaxed. The evaluation unit can also apply strict evaluation criteria when the user is excited. This allows the evaluation criteria to be adjusted according to the user's emotions, thereby providing optimal evaluation criteria. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0138] The evaluation unit can determine the priority of evaluations based on specific time periods and locations during evaluation. For example, the evaluation unit can determine the priority of evaluations based on specific time periods and locations during evaluation. For example, the evaluation unit can prioritize evaluations of entrances and windows at night. The evaluation unit can also prioritize evaluations of the living room during the daytime on weekdays. The evaluation unit can also prioritize evaluations of the entire home when traveling. In this way, by prioritizing evaluations based on specific time periods and locations, important evaluations can be performed quickly. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on time periods and locations into the generation AI and have the generation AI determine the priority of evaluations.

[0139] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating. For example, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating. For example, the evaluation unit prioritizes evaluation of places that the user has frequently rated in the past. The evaluation unit can also prioritize evaluation of places that the user has rated during a specific time period based on the places that the user rated during the same time period. The evaluation unit can also select places to prioritize evaluation during a specific event from the user's past evaluation history. In this way, the optimal evaluation method can be provided by referring to the user's past evaluation history. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation history data into a generation AI and cause the generation AI to select the optimal evaluation method.

[0140] The evaluation unit can apply an anomaly detection algorithm to highlight an abnormal location during evaluation. The evaluation unit can, for example, apply an anomaly detection algorithm to highlight an abnormal location during evaluation. For example, if the evaluation unit detects an abnormal sound, it highlights the location. Furthermore, if the evaluation unit detects an abnormal movement, it can also highlight the location. Furthermore, if the evaluation unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the anomaly detection data to the generation AI and cause the generation AI to highlight the abnormal location.

[0141] The evaluation unit can estimate the user's emotion and adjust the content of the evaluation based on the estimated user's emotion. For example, the evaluation unit can estimate the user's emotion and adjust the content of the evaluation based on the estimated user's emotion. For example, the evaluation unit can provide detailed evaluation content when the user is feeling anxious. The evaluation unit can also provide normal evaluation content when the user is relaxed. The evaluation unit can also provide detailed evaluation content when the user is excited. This allows the optimal evaluation content to be provided by adjusting the content of the evaluation according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the evaluation.

[0142] The evaluation unit can prioritize highly relevant evaluations during evaluation by taking into account the user's geographical location information. For example, the evaluation unit can prioritize highly relevant evaluations during evaluation by taking into account the user's geographical location information. For example, if the user is close to their home, the evaluation unit can prioritize evaluations of the entrance and windows. Furthermore, if the user is far away, the evaluation unit can prioritize evaluations of the entire home. Furthermore, if the user is in a specific location, the evaluation unit can prioritize evaluations related to that location. In this way, by taking the user's geographical location information into account, highly relevant evaluations can be made quickly. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant evaluations.

[0143] The evaluation unit can analyze the user's social media activity and provide a related evaluation during the evaluation. For example, the evaluation unit can analyze the user's social media activity and provide a related evaluation during the evaluation. For example, the evaluation unit can provide an evaluation related to places where the user checked in on social media. The evaluation unit can also analyze the content of the user's social media posts and provide a related evaluation. The evaluation unit can also provide a related evaluation by referring to the activities of the user's friends on social media. In this way, the related evaluation can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input social media activity data into a generation AI and have the generation AI perform a related evaluation.

[0144] The evaluation unit can customize the evaluation method by reflecting the user's past feedback at the time of evaluation. The evaluation unit, for example, customizes the evaluation method by reflecting the user's past feedback at the time of evaluation. For example, the evaluation unit customizes the evaluation method based on the user's past preferred evaluation method. The evaluation unit can also provide an optimal evaluation method for a specific time period based on the user's past feedback. The evaluation unit can also provide an optimal evaluation method for a specific event based on the user's past feedback. In this way, the optimal evaluation method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input past feedback data into the generation AI and cause the generation AI to customize the evaluation method.

[0145] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can provide detailed content of the suggestions. If the user is relaxed, the suggestion unit can also provide normal content of the suggestions. If the user is excited, the suggestion unit can also provide detailed content of the suggestions. This allows the suggestion unit to adjust the content of the suggestions according to the user's emotions and provide optimal content of the suggestions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the suggestions.

[0146] The suggestion unit can determine the priority of suggestions based on a specific time period or location when making suggestions. For example, the suggestion unit can prioritize suggestions for the entrance or windows at night. The suggestion unit can also prioritize suggestions for the living room during the daytime on weekdays. The suggestion unit can also prioritize suggestions for the entire home when traveling. In this way, by prioritizing suggestions based on a specific time period or location, important suggestions can be made quickly. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on time periods and locations into the generation AI and have the generation AI determine the priority of suggestions.

[0147] The suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making a suggestion. For example, the suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making a suggestion. For example, the suggestion unit prioritizes suggesting locations for which the user has frequently received suggestions in the past. The suggestion unit can also prioritize suggestions for locations for which the user has received suggestions during a specific time period based on the locations for which the user has received suggestions during the same time period. The suggestion unit can also select locations to prioritize suggestions for during a specific event from the user's past suggestion history. In this way, the optimal suggestion method can be provided by referring to the user's past suggestion history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past suggestion history data into a generation AI and cause the generation AI to select the optimal suggestion method.

[0148] The suggestion unit can apply an anomaly detection algorithm to highlight an abnormal location when making a suggestion. The suggestion unit, for example, applies an anomaly detection algorithm to highlight an abnormal location when making a suggestion. For example, if the suggestion unit detects an abnormal sound, it highlights the location. Furthermore, if the suggestion unit detects an abnormal movement, it can also highlight the location. Furthermore, if the suggestion unit detects an abnormal temperature change, it can also highlight the location. In this way, by applying the anomaly detection algorithm, an abnormal location can be quickly identified. The anomaly detection algorithm is realized, for example, by a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input anomaly detection data to a generation AI and cause the generation AI to highlight an abnormal location.

[0149] The suggestion unit can estimate the user's emotion and adjust the tone of the suggestion based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the tone of the suggestion based on the estimated user's emotion. For example, if the user is feeling anxious, the suggestion unit can make the suggestion in a calm tone. If the user is relaxed, the suggestion unit can make the suggestion in a bright tone. If the user is excited, the suggestion unit can make the suggestion in a calm tone. This allows the suggestion tone to be adjusted according to the user's emotion, thereby providing an optimal suggestion tone. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the suggestion tone.

[0150] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when the user is close to their home, the suggestion unit can prioritize suggestions for the entrance or windows. Furthermore, when the user is far away, the suggestion unit can prioritize suggestions for the entire home. Furthermore, when the user is in a specific location, the suggestion unit can prioritize suggestions related to that location. In this way, highly relevant suggestions can be made quickly by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input geographical location information data to the generation AI and cause the generation AI to prioritize highly relevant suggestions.

[0151] The suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can make suggestions related to places the user has checked in to on social media. The suggestion unit can also analyze the content of the user's social media posts and make related suggestions. The suggestion unit can also make related suggestions by referring to the activities of the user's friends on social media. In this way, related suggestions can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input social media activity data into a generation AI and have the generation AI execute related suggestions.

[0152] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method based on suggestion methods that the user has previously preferred. The suggestion unit can also provide a suggestion method that is optimal for a specific time period based on the user's past feedback. The suggestion unit can also provide a suggestion method that is optimal for a specific event based on the user's past feedback. In this way, the optimal suggestion method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past feedback data into a generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements, including the confirmation unit, detection unit, notification unit, alert unit, dialogue unit, evaluation unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the confirmation unit can check security camera footage in real time using the camera 42 of the smart device 14. The detection unit detects fires and gas leaks in cooperation with the sensors of the smart device 14. The notification unit automatically notifies the police or fire department in an emergency using the specific processing unit 290 of the data processing device 12. The alert unit learns the faces of family members and authorized individuals using the control unit 46A of the smart device 14 and issues an alert to the user if a suspicious person enters the residence. The dialogue unit conducts virtual dialogue through the speaker of the smart device 14. The evaluation unit evaluates the security of the residence using the specific processing unit 290 of the data processing device 12 and suggests crime prevention measures. The suggestion unit acquires the latest local information using the specific processing unit 290 of the data processing device 12 and suggests optimal disaster countermeasures and evacuation routes. === Hard Collateral 1-2 === Each of the multiple elements, including the confirmation unit, detection unit, notification unit, alert unit, dialogue unit, evaluation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the confirmation unit can check security camera footage in real time using the camera 42 of the smart glasses 214. The detection unit detects fires and gas leaks in cooperation with the sensors of the smart glasses 214. The notification unit automatically notifies the police or fire department in an emergency using the specific processing unit 290 of the data processing device 12. The alert unit learns the faces of family members and authorized individuals using the control unit 46A of the smart glasses 214 and issues an alert to the user if a suspicious person enters the residence. The dialogue unit conducts virtual dialogue through the speaker of the smart glasses 214. The evaluation unit evaluates the security of the residence using the specific processing unit 290 of the data processing device 12 and suggests crime prevention measures. The suggestion unit acquires the latest local information using the specific processing unit 290 of the data processing device 12 and suggests optimal disaster countermeasures and evacuation routes. === Hard Collateral 1-3 === Each of the multiple elements, including the confirmation unit, detection unit, notification unit, alert unit, dialogue unit, evaluation unit, and suggestion unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the confirmation unit can check security camera footage in real time using the camera 42 of the headset terminal 314. The detection unit detects fires and gas leaks in cooperation with the sensors of the headset terminal 314. The notification unit automatically notifies the police or fire department in an emergency using the specific processing unit 290 of the data processing device 12. The alert unit learns the faces of family members and authorized individuals using the control unit 46A of the headset terminal 314 and issues an alert to the user if a suspicious person enters the residence. The dialogue unit conducts virtual dialogue through the speaker of the headset terminal 314. The evaluation unit evaluates the security of the residence using the specific processing unit 290 of the data processing device 12 and suggests crime prevention measures. The suggestion unit acquires the latest local information using the specific processing unit 290 of the data processing device 12 and suggests optimal disaster countermeasures and evacuation routes. === Hard Collateral 1-4 === Each of the multiple elements, including the confirmation unit, detection unit, notification unit, alert unit, dialogue unit, evaluation unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the confirmation unit can check security camera footage in real time using the camera 42 of the robot 414. The detection unit detects fires and gas leaks in cooperation with the robot 414's sensors. The notification unit automatically notifies the police or fire department in an emergency using the specific processing unit 290 of the data processing device 12. The alert unit learns the faces of family members and authorized individuals using the control unit 46A of the robot 414 and issues an alert to the user if a suspicious person enters the residence. The dialogue unit conducts virtual dialogue through the speaker of the robot 414. The evaluation unit evaluates the security of the residence using the specific processing unit 290 of the data processing device 12 and suggests crime prevention measures. The suggestion unit obtains the latest local information using the specific processing unit 290 of the data processing device 12 and suggests optimal disaster countermeasures and evacuation routes.

[0153] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0154] The security system can also be equipped with a voice recognition unit. The voice recognition unit can analyze sounds within the residence in real time and detect abnormal sounds. For example, the voice recognition unit can detect the sound of glass breaking or a door being forcibly opened and closed and notify the user. The voice recognition unit can also recognize the resident's voice and operate the system based on specific voice commands. For example, if a resident shouts "emergency," the system can automatically notify the police. This means that adding voice recognition functionality can further strengthen security.

[0155] The confirmation unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, if the user is nervous, the confirmation unit can provide a simple, highly visible display method. If the user is relaxed, the confirmation unit can also provide a display method that includes detailed information. If the user is in a hurry, the confirmation unit can also provide a display method that focuses on the main points. In this way, by adjusting the video display method according to the user's emotions, it is possible to display the video optimally for the user.

[0156] The detection unit can further include an environmental sensor. The environmental sensor can acquire environmental data such as temperature, humidity, and carbon dioxide concentration in real time and detect abnormalities. For example, if the detection unit detects an abnormal rise in temperature, it can notify the user of the possibility of a fire. The detection unit can also notify the user to ventilate if the carbon dioxide concentration becomes high. In this way, adding an environmental sensor can further improve safety within the home.

[0157] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, the notification unit can issue an immediate notification if the user is feeling anxious. The notification unit can also use a normal notification timing if the user is relaxed. The notification unit can also issue an immediate notification if the user is excited. This allows the notification timing to be adjusted according to the user's emotions, making it possible to issue a notification at the optimal timing.

[0158] The alert unit can further include a vibration sensor. The vibration sensor can detect vibrations within the home and issue an alert if an abnormal vibration is detected. For example, the alert unit can detect vibrations caused by breaking a window and notify the user. The alert unit can also detect vibrations caused by forcibly opening or closing a door and issue a warning. Thus, adding a vibration sensor can strengthen security against physical intrusion.

[0159] The dialogue unit can estimate the user's emotions and adjust the dialogue content based on the estimated user's emotions. For example, if the user is feeling anxious, the dialogue unit can provide dialogue content that gives a sense of security. Furthermore, if the user is relaxed, the dialogue unit can also provide normal dialogue content. Furthermore, if the user is excited, the dialogue unit can also provide dialogue content that encourages the user to stay calm. In this way, the dialogue content can be adjusted according to the user's emotions, thereby providing optimal dialogue content.

[0160] The evaluation unit can also refer to past crime data to perform security evaluations. For example, the evaluation unit can analyze past crime data for a region and propose enhanced crime prevention measures for areas where specific crimes occur frequently. The evaluation unit can also perform risk evaluations for specific time periods based on past crime data. This makes it possible to utilize past crime data to perform more accurate security evaluations and propose crime prevention measures.

[0161] The suggestion unit can estimate the user's emotion and adjust the content of the suggestion based on the estimated user's emotion. For example, if the user feels anxious, the suggestion unit can provide detailed content of the suggestion. If the user feels relaxed, the suggestion unit can also provide normal content of the suggestion. If the user feels excited, the suggestion unit can also provide detailed content of the suggestion. In this way, the suggestion content can be adjusted according to the user's emotion, thereby providing optimal content of the suggestion.

[0162] The confirmation unit may further include a voice assistant function. The voice assistant function can display security camera footage or switch to a specific camera in response to a user's voice command. For example, if the user says, "Show the camera at the front door," the confirmation unit may preferentially display footage from the front door. Also, if the user says, "Show all cameras," the confirmation unit may display footage from all cameras at once. This allows for improved user convenience by adding the voice assistant function.

[0163] The notification unit can also have a function for managing a list of emergency contacts. For example, the notification unit can automatically notify emergency contacts registered by the user in advance in the event of an emergency. The notification unit can also set priorities for emergency contacts and prioritize notifications to the most important contacts. This allows for a quick and appropriate response in the event of an emergency.

[0164] The processing flow of the second embodiment will be briefly explained below.

[0165] Step 1: The checking unit checks the security camera footage in real time. For example, the checking unit can check the security camera footage in real time from a smartphone. The checking unit can also set the video resolution and checking frequency. For example, the checking unit can provide high-resolution video and allow the user to enlarge the video as needed. Step 2: The detection unit detects fires and gas leaks based on the video confirmed by the confirmation unit. For example, the detection unit detects fires and gas leaks in cooperation with a security sensor. The detection unit can set the detection method and type of sensor for fires and gas leaks. For example, the detection unit detects fires and gas leaks using a smoke sensor or gas sensor. Step 3: The notification unit notifies the police or fire department based on the emergency detected by the detection unit. For example, the notification unit automatically notifies the police or fire department in the event of an emergency. The notification unit can set the timing of the notification and the information on the notification destination. For example, the notification unit automatically notifies the fire department in the event of a fire. Step 4: The alert unit recognizes the faces of family members and authorized individuals and issues an alert if a suspicious individual enters the residence. For example, the alert unit learns the faces of family members and authorized individuals and issues an alert to the user if a suspicious individual enters the residence. The alert unit can set the facial recognition algorithm and recognition accuracy. For example, the alert unit detects suspicious individuals using a highly accurate facial recognition algorithm. Step 5: The dialogue unit conducts a voice dialogue if a suspicious individual breaks into the building while the resident is away. For example, the dialogue unit conducts a virtual dialogue through a speaker. The dialogue unit can set the content and tone of the dialogue. For example, if a suspicious individual breaks into the entrance, the dialogue unit can play a message such as "This area is guarded. Please leave immediately."

[0166] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0167] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0171] 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.

[0172] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0187] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0188] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0203] 7, a 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.

[0204] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0205] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0206] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0208] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0209] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0210] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0211] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0213] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0214] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0215] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0216] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0217] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0218] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0219] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0220] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0221] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0222] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0223] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0224] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0225] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0226] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0227] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0228] 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.

[0229] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0230] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0231] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0232] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0233] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0234] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0235] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0236] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0237] [Explanation of symbols]

[0238] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A confirmation section for checking security camera footage in real time; a detection unit that detects a fire or a gas leak based on the video image confirmed by the confirmation unit; a reporting unit that reports to the police or a fire department based on the emergency situation detected by the detection unit; An alert unit that recognizes the faces of family members and authorized people and issues an alert if a suspicious person enters the residence; and a dialogue unit that conducts a voice dialogue when a suspicious person enters the building while the resident is away. A system characterized by:

2. The confirmation unit Check security camera footage in real time from your smartphone 2. The system of claim 1.

3. The detection unit Works in conjunction with security sensors to detect fires and gas leaks 2. The system of claim 1.

4. The reporting unit Automatically notify the police and fire department in an emergency 2. The system of claim 1.

5. The alert unit Recognizes the faces of family members and authorized individuals and sends an alert to the user if a suspicious person enters the home.

2. The system of claim 1.

6. The dialogue unit Conducts a voice dialogue if a suspicious person breaks in while the resident is away 2. The system of claim 1.

7. Equipped with an evaluation department that evaluates residential security and proposes appropriate crime prevention measures 2. The system of claim 1.

8. Equipped with a proposal department that obtains the latest local information and proposes disaster countermeasures and evacuation routes 2. The system of claim 1.

Images