system

Abstract

We provide the system. [Solution] A means of collecting information from multiple sensor devices, A means for analyzing the information using a generative model and identifying anomalies, A means of formulating a response in accordance with a pre-established disaster prevention plan when an anomaly is identified, A means for controlling the residential management device based on the said response to lock and adjust lighting, A means of reporting the situation to a mobile device, A means to integrate information from monitoring equipment in public facilities to enable citizen safety monitoring, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to protect the safety of the family, there is a need to provide a system that can monitor the situation of the house remotely and can respond quickly when an abnormality occurs. Among them, there is a problem that it is particularly difficult to detect potential dangers in advance and automatically execute appropriate security measures when the elderly or children are at home. In addition, in the current security system, there is a problem that the burden on the user is large and a quick and efficient response in an emergency situation has not been realized.

Means for Solving the Problems

[0005] This invention provides a system that continuously collects data from multiple sensors within a home (such as WiFi, UWB, temperature, humidity, sound, and cameras) and analyzes it using a generative model. This allows the system to automatically plan optimal countermeasures based on a pre-configured security plan when an anomaly is detected, and to automatically execute security measures such as locking doors and operating lights through a home management device. Furthermore, it sends real-time notifications to the user and provides rapid information in emergencies. This system reduces the burden on the user and efficiently protects the safety of the family.

[0006] A "sensor device" is a device that measures physical and environmental variables, converts them into digital signals, and provides data.

[0007] A "generative model" is a program that uses machine learning to automatically identify patterns and anomalies from data.

[0008] "Anomaly" is a definition used when detecting unusual behavior or situations, and in security systems, it includes unauthorized intrusion and unusual sounds.

[0009] A "crime prevention plan" is a predefined plan outlining a series of crime prevention measures to be taken when an anomaly is detected.

[0010] A "home management device" is a controller or hub used to control various devices within the home (e.g., smart locks, lighting, cameras).

[0011] A "user device" is a terminal device used by a user to receive information or perform operations.

[0012] "Notification" refers to a means of communicating information to the user regarding the circumstances of an anomaly detection and the security measures taken. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), etc.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a security system for protecting family safety using multiple sensor devices within the home. In this system, data acquired from the sensor devices is analyzed on a server, and if an anomaly is detected, appropriate security measures are automatically planned and executed.

[0035] First, the device monitors the conditions inside the home via Wi-Fi, UWB, and sensors such as temperature, humidity, audio, and cameras. Each sensor has communication capabilities to periodically acquire data and send it to a server. This data includes patterns of movement within the home, environmental changes, and audio information.

[0036] The server analyzes incoming data based on a generative model. The generative model learns unusual behaviors and sound patterns, and detects anomalies by comparing them. After detecting an anomaly, the server plans countermeasures based on a pre-configured security plan. This plan includes specific actions such as locking smart locks on doors, turning on lights, activating alarms, and sending notifications to users.

[0037] If an anomaly is detected, the device will take action according to the security plan via the home management system. For example, if movement is detected in a room that should be empty, all lights in that room will immediately turn on and an alarm will sound. Furthermore, when an anomaly is detected, a notification will be sent to the user's smartphone, displaying detailed information about the situation and the countermeasures taken by the system.

[0038] This system allows users to monitor the situation at their home even from a physical distance and to quickly give instructions to family members at home as needed. For example, if an intrusion occurs while the user is away, the user's terminal will receive a notification from the system, allowing them to immediately view the footage and issue an alarm to neighbors. In this way, the system provides peace of mind to users by demonstrating a high level of security.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device activates various sensor devices installed in the home and continuously collects data. These include WiFi sensors, UWB sensors, temperature and humidity sensors, sound sensors, and camera sensors. Each sensor records data at specific intervals, and the device temporarily stores the acquired data.

[0042] Step 2:

[0043] The terminal sends data collected at regular intervals to the server. The transmitted data includes readings from each sensor and a timestamp. The terminal monitors the data communication status and confirms that the transmission was successful.

[0044] Step 3:

[0045] Based on the data received by the server, the data is input into a generative model for analysis. The generative model learns about normal conditions based on past data and calculates an anomaly score by comparing it with the new data. If this score exceeds a threshold, the server determines that an anomaly has occurred.

[0046] Step 4:

[0047] When an anomaly is detected, the server selects the most appropriate response from a pre-configured security plan and formulates a plan. This plan includes various countermeasures, and the server selects the most effective one based on its judgment.

[0048] Step 5:

[0049] The server sends the selected countermeasure as an instruction to the terminal. Based on the received instruction, the terminal controls the home security device and executes the actual security measures. For example, it may lock the smart lock and turn on the lights throughout the house.

[0050] Step 6:

[0051] The terminal sends a notification to the user's device regarding the detection of an anomaly and the implementation of countermeasures. This notification includes the nature of the anomaly, the measures taken, and the current situation in the home. Based on the notification, the user can check detailed information from the application.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] To ensure safety within the home, it is necessary to efficiently analyze data from multiple sensor devices and quickly and accurately detect potential anomalies. Furthermore, it is essential that appropriate responses be taken promptly in the event of an anomaly, and it is desirable that users in remote locations be able to monitor the situation in real time. The challenge lies in realizing such a comprehensive security system.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for collecting information from multiple measuring devices, means for analyzing the information using a generative model and detecting anomalies, and means for allowing users to remotely check the situation through a video device when an anomaly is detected. This enables advanced security management within the home and allows for a quick and appropriate response to potential dangers.

[0057] A "measuring device" is a device used to measure environmental and physical conditions and collect that data as information.

[0058] "Information" refers to data acquired from measuring devices and insights gained during the analysis process.

[0059] A "generative model" is an algorithm trained using machine learning or artificial intelligence techniques to identify normal patterns and anomalies.

[0060] "Anomaly" is a term that refers to behavior or a state that deviates from normal operating conditions or expected patterns.

[0061] A "crime prevention plan" refers to a series of measures and operational procedures that are implemented in response to the detection of anomalies.

[0062] A "barrier" is a device used to restrict access, and includes, for example, door locking devices.

[0063] "Light source" refers to lighting fixtures and other light-emitting equipment within a home.

[0064] A "user device" is a device owned by the user that allows them to receive notifications from the system or check their home status.

[0065] A "video device" is equipment used to acquire real-time visual information using cameras or similar devices.

[0066] This invention relates to a security system for ensuring safety within the home. This system collects environmental information using multiple measuring devices and analyzes it based on a generative model to detect unusual behavior and automatically take necessary security measures.

[0067] The server collects information from measuring devices such as WiFi, UWB, temperature, humidity, audio, and cameras, and analyzes this information using a generative AI model. The generative AI model has learned normal operating patterns and audio data, and detects anomalies by comparing them with this data. If an anomaly is detected, the server plans and executes appropriate countermeasures based on a pre-configured security plan.

[0068] Specific countermeasures include the server controlling home security devices within the home, such as locking circuit breakers and operating light sources. Furthermore, if an anomaly is detected, a notification is sent to the user's device, allowing the user to remotely monitor the situation inside the home in real time via video equipment.

[0069] The device receives a notification from the server if it detects an intrusion while the user is away, and quickly reviews the footage. The system also includes an example of a prompt for the AI ​​model that generates messages for anomaly detection: "Identify normal activity patterns in the home and suggest security measures to be taken in the event of an intrusion."

[0070] Through these features, users can monitor the safety of their homes and implement advanced security measures even from a physically distant location. Furthermore, the goal of this system is to provide users with a safe environment in which they can live with peace of mind.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The terminal continuously collects environmental information using various in-home measuring devices (WiFi, UWB, temperature, humidity, audio, camera, etc.). Input data from these measuring devices includes, for example, temperature, humidity, sound intensity, and video data. This information is periodically transmitted to the server. The output is raw data sent to the server.

[0074] Step 2:

[0075] The server receives information transmitted from the terminal and performs data analysis using a generative AI model. The input here is a wide variety of environmental data sent from the terminal. The generative AI model analyzes this data using normal patterns and speech to determine whether or not an anomaly is present. The output is a result indicating whether or not an anomaly was detected.

[0076] Step 3:

[0077] If the server detects an anomaly based on the analysis results, it plans appropriate countermeasures according to a pre-configured security plan. The input is the analysis results of a generated AI model. Data processing includes comparing the analysis results with security plan data. The output is a list of specific countermeasures.

[0078] Step 4:

[0079] The server controls the home management devices in the home based on planned actions. The input to this step is the pre-planned action. For example, the server might lock a door shutoff or operate a light source to turn on the lights. The output is a control signal sent to the home management device, which then performs the actual operation.

[0080] Step 5:

[0081] The server sends a notification to the user's device so that the user can remotely check for the occurrence of an anomaly and the countermeasures taken. This step takes the results of the security plan's implementation as input. The server notifies the user's device of the situation and, if necessary, enables remote video verification. The notification is displayed on the user's device as output.

[0082] This allows users to respond quickly in the event of an anomaly, ensuring the safety of their homes.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In modern urban environments, public safety is a critical issue. In particular, it is essential to detect and address any abnormal incidents that threaten safety at an early stage. However, current security systems are often limited to individual homes and facilities, lacking integrated safety management across broader areas. Against this backdrop, there is a need for a means of monitoring and managing public safety in a unified and efficient manner across a wide area.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for collecting information from multiple sensor devices, means for analyzing the information using a generative model to identify anomalies, and means for integrating information from monitoring equipment in public facilities in the event of an anomaly to enable monitoring of citizen safety. This enables efficient monitoring of safety throughout the city and allows for a rapid response.

[0088] A "sensor device" is a device used to acquire information from the environment and has the function of collecting various data such as temperature, humidity, sound, and images.

[0089] A "generative model" is an algorithm or method used to analyze data acquired from sensors, which learns unusual patterns and enables the detection of anomalies.

[0090] "Means for identifying anomalies" refer to systems that analyze collected data and have the function of detecting behaviors or states that deviate from normal patterns.

[0091] A "disaster prevention plan" is a document that pre-determines specific countermeasures to be taken when an abnormality is detected, and includes things like locking doors and operating lights.

[0092] A "residential area management device" is a device installed in a home or facility to comprehensively manage various sensors and system operations.

[0093] A "personal information terminal" is an electronic device that a user can carry with them and use to report situations and check information.

[0094] "Public facility monitoring equipment" refers to devices installed in public areas within cities to monitor the surrounding environment in real time.

[0095] To implement this invention, it is necessary to construct a system that integrates sensor equipment, a residential management device, and a portable information terminal. The sensor equipment acquires information such as temperature, humidity, sound, and images, and transmits the data to a server. The server analyzes the collected data using a generation AI model and identifies anomalies. If an anomaly is detected, the server controls the residential management device based on the disaster prevention plan, performing actions such as locking doors and operating lights.

[0096] The server also reports detected anomalies to mobile devices, allowing users to stay informed in real time. By integrating information from monitoring equipment in public facilities, this system can monitor public safety throughout the city.

[0097] This system processes vast amounts of information acquired from sensor devices in the cloud as part of the data processing and calculation process, and identifies anomalies based on generative models. This makes it possible to efficiently manage the safety of the entire city.

[0098] As a concrete example, if a suspicious person is detected in a public park late at night, the system can immediately turn on all the lights in the park and notify the nearest security agency of the anomaly. In this case, based on the anomaly analyzed using a generative AI model, a prompt message such as "Suspicious activity has been detected in the park. Please promptly provide details of the incident and recommended countermeasures." can be used.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server receives data from multiple sensor devices. This data includes information such as temperature, humidity, sound, and images. The server acquires this data in real time and prepares it for analysis. The input is raw data from the sensor devices, and the output is an integrated dataset.

[0102] Step 2:

[0103] The server analyzes the received data based on a generative AI model. The generative AI model learns normal behavioral patterns and performs comparative analysis to identify anomalies in the data. The input is an integrated dataset, and the output is whether or not anomalies were detected and their details.

[0104] Step 3:

[0105] If an anomaly is detected, the server will formulate a response based on the disaster prevention plan. It will determine specific actions such as locking doors, operating lights, or issuing warning sounds. The input in this step is the anomaly detection information, and the output is the specific countermeasure to be implemented.

[0106] Step 4:

[0107] The terminal executes planned countermeasures through the residential management device. Specifically, this includes actions such as locking smart locks, turning on indoor lights, and sounding alarms. The input is the formulated countermeasures, and the output is the result of the execution of physical and electronic controls.

[0108] Step 5:

[0109] The server notifies the user's mobile device of the detected anomaly and the countermeasures taken. The user can check the details of the situation and the actions taken in real time. The input here is information about the anomaly and the countermeasures taken, and the output is notification data for user reporting.

[0110] Step 6:

[0111] The server integrates information from monitoring equipment at public facilities and continues to monitor the safety of the entire city. If an anomaly spreads at the city level, it considers and implements further appropriate countermeasures. The input for this step is additional data from public facilities, and the output is an assessment of the safety of the entire city.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention is a security system that combines multiple in-home sensor devices with an emotion engine. This system uses data obtained from in-home sensors to detect anomalies and infers the user's emotional state, thereby providing more personalized security measures and a comfortable environment.

[0114] First, the device collects data in real time through sensor devices installed in the home. This data includes human movement detected by WiFi sensors, conversations and ambient sounds detected by voice sensors, and video information from camera sensors. This data is temporarily stored on the device and sent to the server at regular intervals.

[0115] The server analyzes the received data using a generative model to detect anomalies. This allows for the early detection of potential threats within the home. If an anomaly is detected, the server plans and implements the optimal countermeasures based on a pre-configured security plan.

[0116] Furthermore, this system incorporates an emotion engine. The server analyzes the user's emotional state based on voice and behavioral data obtained from sensors. The emotion engine determines emotional states such as stress, relaxation, and alertness, and generates instructions to control the home management device accordingly.

[0117] For example, if the emotion engine determines that the user is feeling fatigued, the server sends instructions to the home management device to adjust the lighting to a warmer color and play relaxation music. Conversely, if the system determines that the user is under high stress, it will optimize the environment according to the user's emotions, such as displaying images of refreshing scenery on the camera display.

[0118] In this way, the collaboration between the emotion engine and the generative model allows users to ensure daily peace of mind while simultaneously receiving personalized support tailored to their emotional state. As a result, family safety and quality of life are improved.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device collects data from sensor devices installed in the home. This includes motion detection using WiFi sensors, acquisition of audio data from audio sensors, and recording of video from camera sensors. This data is stored in a buffer at regular intervals.

[0122] Step 2:

[0123] The terminal sends the data in the buffer to the server. The transmission includes data packets containing information from various sensors, organized in chronological order. Data transmission occurs only when stable communication is ensured.

[0124] Step 3:

[0125] The server analyzes the received data. Using a generative model, it compares it to normal behavioral patterns to detect anomalies. An anomaly score is calculated during this process, and if it exceeds a set threshold, an anomaly is detected.

[0126] Step 4:

[0127] When an anomaly is detected, the server consults a pre-prepared security plan and devises a response. This plan includes how to control which home security devices, selecting the quickest and most effective method.

[0128] Step 5:

[0129] The server uses an emotion engine to analyze the user's emotions from voice data and behavioral patterns. The emotion engine determines the user's stress level and relaxation state, and uses the results to generate instructions for environmental control.

[0130] Step 6:

[0131] The device controls the home management system based on instructions from the server. Specific operations include locking smart locks, changing lighting colors, and selecting music, all of which are performed automatically according to the user's emotional state.

[0132] Step 7:

[0133] The terminal notifies the user's device of the status of anomaly detection and response, as well as the results of the user's sentiment analysis. Through this notification, the user can check the current situation in their home and the details of security measures.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] It is difficult to provide a comfortable environment based on the emotional state of residents while responding quickly to sudden emergencies and security risks within the home. Furthermore, current systems cannot optimize the environment in accordance with emotions, resulting in the challenge of not being able to achieve both resident comfort and safety.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes means for collecting information from multiple detection devices, means for analyzing the information using a generation algorithm to detect anomalies, and means for inferring emotional states based on the analyzed information. This enables early detection of abnormal situations as well as appropriate environmental control in response to emotions.

[0139] A "detection device" is a device that uses sensor technology to collect information from the environment and has the function of acquiring data such as human movement, sound, and images.

[0140] A "generative algorithm" is a computational method for finding specific patterns or trends by analyzing a large amount of input information, and it is a mechanism for gaining new insights using AI technology.

[0141] An "anomaly" refers to an event or behavior that exceeds the normal range, and includes potential dangers and security risks detected by the system.

[0142] "Emotional state" refers to an individual's psychological or emotional condition and includes states such as stress and relaxation, which are determined through analysis based on voice and behavioral data.

[0143] An "environmental control device" is a device that controls household appliances such as lighting and sound equipment to adjust the conditions of a living space, and has the function of improving user comfort.

[0144] A "user device" is a device that a user can directly operate and use to receive notifications of abnormalities or status changes, and includes mobile terminals and tablets.

[0145] This system aims to improve safety and comfort within the home and is implemented with a configuration that includes multiple detection devices, generation algorithms, environmental control devices, and user devices.

[0146] The device collects environmental information in various formats using detection devices installed in the home, such as WiFi sensors, voice sensors, and camera sensors. The WiFi sensor acquires data on people's movements in the room, the voice sensor records surrounding sounds and conversations, and the camera sensor captures visual information. This data is temporarily stored on the device and sent to the server at regular intervals.

[0147] The server analyzes the received data using a generation algorithm. AI model analysis is used to find useful patterns in large amounts of data and to detect anomalies that threaten safety within the home at an early stage. If an anomaly is detected, necessary countermeasures are devised based on a pre-configured safety plan.

[0148] Furthermore, the server employs an emotion engine for sentiment analysis, inferring the user's emotional state from voice and movement data. Based on this analysis, instructions are sent to the environmental control system, which then appropriately optimizes the home environment through, for example, lighting adjustments and sound effects. Specifically, if the user needs to relax, warm-colored lighting and relaxation music are used, while if they are feeling stressed, refreshing images are played.

[0149] Users have a user device that notifies them of detected anomalies and environmental changes, and can check the situation in their home in real time via their smartphone or tablet. Examples of prompt messages include, "Based on sensor data, please consider countermeasures when a person is experiencing high stress. Please suggest ways to optimize the environment," and "Please give specific examples of the home environment that should be provided when the user is relaxed."

[0150] This system allows users to improve safety within their homes while also obtaining a comfortable living environment that responds to their emotions.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The device collects input data from Wi-Fi sensors, voice sensors, and camera sensors installed in the home. The Wi-Fi sensor detects the movement of residents, the voice sensor records conversations and ambient sounds, and the camera sensor captures visual information. This data is temporarily stored in the device's memory. The output of this step is a set of sensor data recorded in chronological order.

[0154] Step 2:

[0155] The terminal transmits the accumulated sensor data to the server at predetermined intervals. For security reasons, the data is encrypted before being transmitted to the server via the internet. The input to this step is the sensor data stored in the terminal, and the output is the transmission of encrypted data to the server.

[0156] Step 3:

[0157] The server analyzes the received sensor data using a generating AI model. The algorithm compares the data to normal conditions and detects abnormal patterns. For example, if abnormal movement or noise is detected, the server determines it to be an anomaly. The input for this step is the transmitted sensor data, and the output is the anomaly detection result.

[0158] Step 4:

[0159] If an anomaly is detected, the server plans and implements the optimal response according to a pre-configured safety plan. For example, if suspicious activity is detected, it records footage from security cameras and activates an alarm. The input to this step is the anomaly detection result, and the output is the implementation of specific safety measures.

[0160] Step 5:

[0161] The server uses an emotion engine to infer the user's emotional state based on sensor data. It analyzes the tone and behavioral patterns of voice data to determine emotions such as stress and relaxation. The input for this step is sensor data, and the output is an evaluation of the user's emotional state.

[0162] Step 6:

[0163] Based on the emotional state, the server sends specific instructions to the environmental control unit to control the home environment. For example, if relaxation is needed, the lighting is adjusted to warmer colors and relaxation music is played. The input for this step is the result of the emotional analysis, and the output is the optimization of the home environment.

[0164] Step 7:

[0165] Users receive notifications about detected anomalies and changes to the environment. They can then access detailed information and take additional action as needed via a smartphone app or tablet. The input in this step is a notification from the server, and the output is information provided to the user.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In modern smart cities, ensuring the safety of residents while providing a comfortable living environment is crucial. However, existing security and environmental control systems struggle to respond flexibly based on residents' emotional states, and their ability to quickly and appropriately address anomalies is insufficient. To solve this problem, the introduction of more advanced systems incorporating emotion analysis is required.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes a function to collect data from multiple information gathering means, a function to process that data using a generation algorithm and identify anomalies, and a function to construct countermeasures according to a pre-set safety plan when an anomaly is identified. This enables the rapid implementation of countermeasures when an anomaly occurs and the optimization of the environment based on the emotional state of residents.

[0171] "Information gathering means" refers to sensors and devices used to acquire diverse data.

[0172] A "generative algorithm" is a program that analyzes collected data to identify specific patterns or anomalies.

[0173] The "function for identifying anomalies" is a process for identifying phenomena that differ from normal data or situations.

[0174] A "safety measures plan" is a set of pre-defined action plans for responding to abnormal situations.

[0175] The "function of developing countermeasures" refers to the process of planning and preparing, in a feasible form, the actions to be taken when an anomaly is identified.

[0176] A "control device" is a machine or system used to control equipment in a home or public space.

[0177] A "user device" is a terminal or device used to provide information to a user and receive operating instructions.

[0178] "Emotional analysis" is the process of inferring a user's emotional state from collected data and analyzing the results.

[0179] The "environment adjustment function" refers to the function of appropriately changing the lighting, music, and other elements of a space based on acquired emotional data.

[0180] The system implementing this invention uses a mechanism to collect data from various sensors and information gathering devices installed throughout a city or within individual homes. A server takes in the data collected from these sensors in real time and processes it using a generation algorithm to identify anomalies. If an anomaly is identified, the server quickly develops countermeasures based on a pre-configured safety plan and sends instructions to the management device. As a result, the management device in the city or home performs actions such as controlling entrances and exits or adjusting light sources.

[0181] The server also performs emotion analysis simultaneously, inferring the user's emotional state from the collected data. Based on this analysis, it can send instructions to the management device to optimize the environment in public spaces or homes. For example, for a user analyzed as feeling stressed, the server could play relaxation music or change the lighting to a warmer tone.

[0182] As a concrete example, by monitoring park usage through IoT sensors, if there are many visitors on gloomy weather days, the environment can be improved by making the lights brighter or playing cheerful music. The main hardware used includes IoT sensors from various manufacturers and AWS® EC2 instances as servers. On the software side, a program written in Python is created, and Tensorflow® is used for the generated AI model.

[0183] An example of a prompt to be input into the generating AI model would be: "In order to create an urban environment that citizens find comfortable, please use data collected from sensors to appropriately determine emotional states and work towards creating a safe and happy space."

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server collects data from information gathering devices installed in homes and public spaces. Inputs include raw data obtained from motion sensors, sound sensors, cameras, and other sources. This data is temporarily stored, formatted, and converted into an analyzable format.

[0187] Step 2:

[0188] The server inputs the formatted data into a generation AI model, which then analyzes the data. This analysis identifies the presence or absence of anomalies. The input is pre-processed data, and the output is the presence or absence of anomalies and the type of anomaly. Using the analysis results, if anomalies are found, the system identifies situations requiring further action.

[0189] Step 3:

[0190] When the server detects an anomaly, it develops a countermeasure based on a pre-configured safety plan. The input is the result of the anomaly identification, and the output is a detailed protocol for the countermeasure. The server sends this protocol to the management device, preparing to take physical control.

[0191] Step 4:

[0192] The control device performs actions such as locking doors and adjusting lighting based on instructions from the server. The input is the corresponding protocol received from the server, and the output is the actual control action. This improves security in homes or public spaces.

[0193] Step 5:

[0194] The server performs emotion analysis based on the collected sensor data. The input is formatted sensor data, and the output is the user's emotional state (e.g., relaxed, stressed). The server sends environment optimization instructions to the management device according to the emotional state.

[0195] Step 6:

[0196] The management device adjusts the environment according to the user's emotional state, following environment optimization instructions received from the server. The input is the emotional optimization instructions from the server, and the output is a change in the environment (e.g., music playback, change in lighting color).

[0197] Step 7:

[0198] The server sends notifications to the user's device regarding the situation and countermeasures. The input is the results of anomaly and sentiment analysis and the countermeasures, and the output is the content of the notification to the user. This allows the user to understand information about their environment and safety status in real time.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention provides a security system for protecting family safety using multiple sensor devices within the home. In this system, data acquired from the sensor devices is analyzed on a server, and if an anomaly is detected, appropriate security measures are automatically planned and executed.

[0216] First, the device monitors the conditions inside the home via Wi-Fi, UWB, and sensors such as temperature, humidity, audio, and cameras. Each sensor has communication capabilities to periodically acquire data and send it to a server. This data includes patterns of movement within the home, environmental changes, and audio information.

[0217] The server analyzes incoming data based on a generative model. The generative model learns unusual behaviors and sound patterns, and detects anomalies by comparing them. After detecting an anomaly, the server plans countermeasures based on a pre-configured security plan. This plan includes specific actions such as locking smart locks on doors, turning on lights, activating alarms, and sending notifications to users.

[0218] If an anomaly is detected, the device will take action according to the security plan via the home management system. For example, if movement is detected in a room that should be empty, all lights in that room will immediately turn on and an alarm will sound. Furthermore, when an anomaly is detected, a notification will be sent to the user's smartphone, displaying detailed information about the situation and the countermeasures taken by the system.

[0219] This system allows users to monitor the situation at their home even from a physical distance and to quickly give instructions to family members at home as needed. For example, if an intrusion occurs while the user is away, the user's terminal will receive a notification from the system, allowing them to immediately view the footage and issue an alarm to neighbors. In this way, the system provides peace of mind to users by demonstrating a high level of security.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The device activates various sensor devices installed in the home and continuously collects data. These include WiFi sensors, UWB sensors, temperature and humidity sensors, sound sensors, and camera sensors. Each sensor records data at specific intervals, and the device temporarily stores the acquired data.

[0223] Step 2:

[0224] The terminal sends data collected at regular intervals to the server. The transmitted data includes readings from each sensor and a timestamp. The terminal monitors the data communication status and confirms that the transmission was successful.

[0225] Step 3:

[0226] Based on the data received by the server, the data is input into a generative model for analysis. The generative model learns about normal conditions based on past data and calculates an anomaly score by comparing it with the new data. If this score exceeds a threshold, the server determines that an anomaly has occurred.

[0227] Step 4:

[0228] When an anomaly is detected, the server selects the most appropriate response from a pre-configured security plan and formulates a plan. This plan includes various countermeasures, and the server selects the most effective one based on its judgment.

[0229] Step 5:

[0230] The server sends the selected countermeasure as an instruction to the terminal. Based on the received instruction, the terminal controls the home security device and executes the actual security measures. For example, it may lock the smart lock and turn on the lights throughout the house.

[0231] Step 6:

[0232] The terminal sends a notification to the user's device regarding the detection of an anomaly and the implementation of countermeasures. This notification includes the nature of the anomaly, the measures taken, and the current situation in the home. Based on the notification, the user can check detailed information from the application.

[0233] (Example 1)

[0234] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0235] To ensure safety within the home, it is necessary to efficiently analyze data from multiple sensor devices and quickly and accurately detect potential anomalies. Furthermore, it is essential that appropriate responses be taken promptly in the event of an anomaly, and it is desirable that users in remote locations be able to monitor the situation in real time. The challenge lies in realizing such a comprehensive security system.

[0236] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0237] In this invention, the server includes means for collecting information from multiple measuring devices, means for analyzing the information using a generative model and detecting anomalies, and means for allowing users to remotely check the situation through a video device when an anomaly is detected. This enables advanced security management within the home and allows for a quick and appropriate response to potential dangers.

[0238] A "measuring device" is a device used to measure environmental and physical conditions and collect that data as information.

[0239] "Information" refers to data acquired from measuring devices and insights gained during the analysis process.

[0240] A "generative model" is an algorithm trained using machine learning or artificial intelligence techniques to identify normal patterns and anomalies.

[0241] "Anomaly" is a term that refers to behavior or a state that deviates from normal operating conditions or expected patterns.

[0242] A "crime prevention plan" refers to a series of measures and operational procedures that are implemented in response to the detection of anomalies.

[0243] A "barrier" is a device used to restrict access, and includes, for example, door locking devices.

[0244] "Light source" refers to lighting fixtures and other light-emitting equipment within a home.

[0245] A "user device" is a device owned by the user that allows them to receive notifications from the system or check their home status.

[0246] A "video device" is equipment used to acquire real-time visual information using cameras or similar devices.

[0247] This invention relates to a security system for ensuring safety within the home. This system collects environmental information using multiple measuring devices and analyzes it based on a generative model to detect unusual behavior and automatically take necessary security measures.

[0248] The server collects information from measuring devices such as WiFi, UWB, temperature, humidity, audio, and cameras, and analyzes this information using a generative AI model. The generative AI model has learned normal operating patterns and audio data, and detects anomalies by comparing them with this data. If an anomaly is detected, the server plans and executes appropriate countermeasures based on a pre-configured security plan.

[0249] Specific countermeasures include the server controlling home security devices within the home, such as locking circuit breakers and operating light sources. Furthermore, if an anomaly is detected, a notification is sent to the user's device, allowing the user to remotely monitor the situation inside the home in real time via video equipment.

[0250] The device receives a notification from the server if it detects an intrusion while the user is away, and quickly reviews the footage. The system also includes an example of a prompt for the AI ​​model that generates messages for anomaly detection: "Identify normal activity patterns in the home and suggest security measures to be taken in the event of an intrusion."

[0251] Through these features, users can monitor the safety of their homes and implement advanced security measures even from a physically distant location. Furthermore, the goal of this system is to provide users with a safe environment in which they can live with peace of mind.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The terminal continuously collects environmental information using various in-home measuring devices (WiFi, UWB, temperature, humidity, audio, camera, etc.). Input data from these measuring devices includes, for example, temperature, humidity, sound intensity, and video data. This information is periodically transmitted to the server. The output is raw data sent to the server.

[0255] Step 2:

[0256] The server receives information transmitted from the terminal and performs data analysis using a generative AI model. The input here is a wide variety of environmental data sent from the terminal. The generative AI model analyzes this data using normal patterns and speech to determine whether or not an anomaly is present. The output is a result indicating whether or not an anomaly was detected.

[0257] Step 3:

[0258] If the server detects an anomaly based on the analysis results, it plans appropriate countermeasures according to a pre-configured security plan. The input is the analysis results of a generated AI model. Data processing includes comparing the analysis results with security plan data. The output is a list of specific countermeasures.

[0259] Step 4:

[0260] The server controls the home management devices in the home based on planned actions. The input to this step is the pre-planned action. For example, the server might lock a door shutoff or operate a light source to turn on the lights. The output is a control signal sent to the home management device, which then performs the actual operation.

[0261] Step 5:

[0262] The server sends a notification to the user's device so that the user can remotely check for the occurrence of an anomaly and the countermeasures taken. This step takes the results of the security plan's implementation as input. The server notifies the user's device of the situation and, if necessary, enables remote video verification. The notification is displayed on the user's device as output.

[0263] This allows users to respond quickly in the event of an anomaly, ensuring the safety of their homes.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In modern urban environments, public safety is a critical issue. In particular, it is essential to detect and address any abnormal incidents that threaten safety at an early stage. However, current security systems are often limited to individual homes and facilities, lacking integrated safety management across broader areas. Against this backdrop, there is a need for a means of monitoring and managing public safety in a unified and efficient manner across a wide area.

[0267] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0268] In this invention, the server includes means for collecting information from multiple sensor devices, means for analyzing the information using a generative model to identify anomalies, and means for integrating information from monitoring equipment in public facilities in the event of an anomaly to enable monitoring of citizen safety. This enables efficient monitoring of safety throughout the city and allows for a rapid response.

[0269] A "sensor device" is a device used to acquire information from the environment and has the function of collecting various data such as temperature, humidity, sound, and images.

[0270] A "generative model" is an algorithm or method used to analyze data acquired from sensors, which learns unusual patterns and enables the detection of anomalies.

[0271] "Means for identifying anomalies" refer to systems that analyze collected data and have the function of detecting behaviors or states that deviate from normal patterns.

[0272] A "disaster prevention plan" is a document that pre-determines specific countermeasures to be taken when an abnormality is detected, and includes things like locking doors and operating lights.

[0273] A "residential area management device" is a device installed in a home or facility to comprehensively manage various sensors and system operations.

[0274] A "personal information terminal" is an electronic device that a user can carry with them and use to report situations and check information.

[0275] "Public facility monitoring equipment" refers to devices installed in public areas within cities to monitor the surrounding environment in real time.

[0276] To implement this invention, it is necessary to construct a system that integrates sensor equipment, a residential management device, and a portable information terminal. The sensor equipment acquires information such as temperature, humidity, sound, and images, and transmits the data to a server. The server analyzes the collected data using a generation AI model and identifies anomalies. If an anomaly is detected, the server controls the residential management device based on the disaster prevention plan, performing actions such as locking doors and operating lights.

[0277] The server also reports detected anomalies to mobile devices, allowing users to stay informed in real time. By integrating information from monitoring equipment in public facilities, this system can monitor public safety throughout the city.

[0278] This system processes vast amounts of information acquired from sensor devices in the cloud as part of the data processing and calculation process, and identifies anomalies based on generative models. This makes it possible to efficiently manage the safety of the entire city.

[0279] As a concrete example, if a suspicious person is detected in a public park late at night, the system can immediately turn on all the lights in the park and notify the nearest security agency of the anomaly. In this case, based on the anomaly analyzed using a generative AI model, a prompt message such as "Suspicious activity has been detected in the park. Please promptly provide details of the incident and recommended countermeasures." can be used.

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The server receives data from multiple sensor devices. This data includes information such as temperature, humidity, sound, and images. The server acquires this data in real time and prepares it for analysis. The input is raw data from the sensor devices, and the output is an integrated dataset.

[0283] Step 2:

[0284] The server analyzes the received data based on the generative AI model. The generative AI model has learned normal behavior patterns and performs comparison and analysis to identify anomalies in the data. The input is the integrated dataset, and the output is the presence or absence of anomaly detection and its detailed information.

[0285] Step 3:

[0286] If an anomaly is detected, the server formulates a response based on the disaster prevention plan. It determines specific actions such as locking, lighting operations, and emitting warning sounds. The input at this step is the anomaly detection information, and the output is the specific response measures to be executed.

[0287] Step 4:

[0288] The terminal executes the planned response measures through the residential area management device. Specifically, it performs actions such as locking the smart lock, turning on the indoor lighting, and sounding the alarm. The input is the formulated response measures, and the output is the execution result of physical and electronic controls.

[0289] Step 5:

[0290] The server notifies the user's mobile information terminal of the anomaly detection and response measures. The user can confirm the details of the situation and the actions taken in real time. The input here is the information regarding the anomaly and response, and the output is the notification data for user reporting.

[0291] Step 6:

[0292] The server integrates the information from the monitoring devices of public facilities and continues to monitor the safety of the entire city. If an anomaly spreads at the city level, it further considers and implements appropriate response measures. The input for this step is the additional data from public facilities, and the output is the evaluation regarding the safety of the entire city.

[0293] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0294] This invention is a security system that combines multiple in-home sensor devices with an emotion engine. This system uses data obtained from in-home sensors to detect anomalies and infers the user's emotional state, thereby providing more personalized security measures and a comfortable environment.

[0295] First, the device collects data in real time through sensor devices installed in the home. This data includes human movement detected by WiFi sensors, conversations and ambient sounds detected by voice sensors, and video information from camera sensors. This data is temporarily stored on the device and sent to the server at regular intervals.

[0296] The server analyzes the received data using a generative model to detect anomalies. This allows for the early detection of potential threats within the home. If an anomaly is detected, the server plans and implements the optimal countermeasures based on a pre-configured security plan.

[0297] Furthermore, this system incorporates an emotion engine. The server analyzes the user's emotional state based on voice and behavioral data obtained from sensors. The emotion engine determines emotional states such as stress, relaxation, and alertness, and generates instructions to control the home management device accordingly.

[0298] For example, if the emotion engine determines that the user is feeling fatigued, the server sends instructions to the home management device to adjust the lighting to a warmer color and play relaxation music. Conversely, if the system determines that the user is under high stress, it will optimize the environment according to the user's emotions, such as displaying images of refreshing scenery on the camera display.

[0299] In this way, through the cooperation of the emotion engine and the generation model, users can ensure their daily peace of mind and receive personalized support according to their emotional states. As a result, the safety of the family and the improvement of the quality of life can be achieved.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The terminal collects data from sensor devices installed in the home. This includes motion detection by WiFi sensors, acquisition of voice data from voice sensors, and recording of video by camera sensors. These data are stored in the buffer at regular intervals.

[0303] Step 2:

[0304] The terminal transmits the data in the buffer to the server. The transmission includes information on various sensors obtained as data packets and is sorted in chronological order. The data transmission is performed when stable communication is ensured.

[0305] Step 3:

[0306] The server analyzes the received data. Using the generation model, it compares with the normal behavior pattern and detects abnormalities. An abnormality score is calculated in this process, and when it exceeds the set threshold, it is determined that an abnormality has been detected.

[0307] Step 4:

[0308] When an abnormality is detected, the server refers to a pre-prepared security plan and plans countermeasures. The plan includes how to control which home management devices, and the most rapid and effective method is selected.

[0309] Step 5:

[0310] The server uses an emotion engine to analyze the user's emotions from voice data and behavioral patterns. The emotion engine determines the user's stress level and relaxation state, and uses the results to generate instructions for environmental control.

[0311] Step 6:

[0312] The device controls the home management system based on instructions from the server. Specific operations include locking smart locks, changing lighting colors, and selecting music, all of which are performed automatically according to the user's emotional state.

[0313] Step 7:

[0314] The terminal notifies the user's device of the status of anomaly detection and response, as well as the results of the user's sentiment analysis. Through this notification, the user can check the current situation in their home and the details of security measures.

[0315] (Example 2)

[0316] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0317] It is difficult to provide a comfortable environment based on the emotional state of residents while responding quickly to sudden emergencies and security risks within the home. Furthermore, current systems cannot optimize the environment in accordance with emotions, resulting in the challenge of not being able to achieve both resident comfort and safety.

[0318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0319] In this invention, the server includes means for collecting information from multiple detection devices, means for analyzing the information using a generation algorithm to detect anomalies, and means for inferring emotional states based on the analyzed information. This enables early detection of abnormal situations as well as appropriate environmental control in response to emotions.

[0320] A "detection device" is a device that uses sensor technology to collect information from the environment and has the function of acquiring data such as human movement, sound, and images.

[0321] A "generative algorithm" is a computational method for finding specific patterns or trends by analyzing a large amount of input information, and it is a mechanism for gaining new insights using AI technology.

[0322] An "anomaly" refers to an event or behavior that exceeds the normal range, and includes potential dangers and security risks detected by the system.

[0323] "Emotional state" refers to an individual's psychological or emotional condition and includes states such as stress and relaxation, which are determined through analysis based on voice and behavioral data.

[0324] An "environmental control device" is a device that controls household appliances such as lighting and sound equipment to adjust the conditions of a living space, and has the function of improving user comfort.

[0325] A "user device" is a device that a user can directly operate and use to receive notifications of abnormalities or status changes, and includes mobile terminals and tablets.

[0326] This system aims to improve safety and comfort within the home and is implemented with a configuration that includes multiple detection devices, generation algorithms, environmental control devices, and user devices.

[0327] The device collects environmental information in various formats using detection devices installed in the home, such as WiFi sensors, voice sensors, and camera sensors. The WiFi sensor acquires data on people's movements in the room, the voice sensor records surrounding sounds and conversations, and the camera sensor captures visual information. This data is temporarily stored on the device and sent to the server at regular intervals.

[0328] The server analyzes the received data using a generation algorithm. AI model analysis is used to find useful patterns in large amounts of data and to detect anomalies that threaten safety within the home at an early stage. If an anomaly is detected, necessary countermeasures are devised based on a pre-configured safety plan.

[0329] Furthermore, the server employs an emotion engine for sentiment analysis, inferring the user's emotional state from voice and movement data. Based on this analysis, instructions are sent to the environmental control system, which then appropriately optimizes the home environment through, for example, lighting adjustments and sound effects. Specifically, if the user needs to relax, warm-colored lighting and relaxation music are used, while if they are feeling stressed, refreshing images are played.

[0330] Users have a user device that notifies them of detected anomalies and environmental changes, and can check the situation in their home in real time via their smartphone or tablet. Examples of prompt messages include, "Based on sensor data, please consider countermeasures when a person is experiencing high stress. Please suggest ways to optimize the environment," and "Please give specific examples of the home environment that should be provided when the user is relaxed."

[0331] This system allows users to improve safety within their homes while also obtaining a comfortable living environment that responds to their emotions.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The device collects input data from Wi-Fi sensors, voice sensors, and camera sensors installed in the home. The Wi-Fi sensor detects the movement of residents, the voice sensor records conversations and ambient sounds, and the camera sensor captures visual information. This data is temporarily stored in the device's memory. The output of this step is a set of sensor data recorded in chronological order.

[0335] Step 2:

[0336] The terminal transmits the accumulated sensor data to the server at predetermined intervals. For security reasons, the data is encrypted before being transmitted to the server via the internet. The input to this step is the sensor data stored in the terminal, and the output is the transmission of encrypted data to the server.

[0337] Step 3:

[0338] The server analyzes the received sensor data using a generating AI model. The algorithm compares the data to normal conditions and detects abnormal patterns. For example, if abnormal movement or noise is detected, the server determines it to be an anomaly. The input for this step is the transmitted sensor data, and the output is the anomaly detection result.

[0339] Step 4:

[0340] If an anomaly is detected, the server plans and implements the optimal response according to a pre-configured safety plan. For example, if suspicious activity is detected, it records footage from security cameras and activates an alarm. The input to this step is the anomaly detection result, and the output is the implementation of specific safety measures.

[0341] Step 5:

[0342] The server uses an emotion engine to infer the user's emotional state based on sensor data. It analyzes the tone and behavioral patterns of voice data to determine emotions such as stress and relaxation. The input for this step is sensor data, and the output is an evaluation of the user's emotional state.

[0343] Step 6:

[0344] Based on the emotional state, the server sends specific instructions to the environmental control unit to control the home environment. For example, if relaxation is needed, the lighting is adjusted to warmer colors and relaxation music is played. The input for this step is the result of the emotional analysis, and the output is the optimization of the home environment.

[0345] Step 7:

[0346] Users receive notifications about detected anomalies and changes to the environment. They can then access detailed information and take additional action as needed via a smartphone app or tablet. The input in this step is a notification from the server, and the output is information provided to the user.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0349] In modern smart cities, ensuring the safety of residents while providing a comfortable living environment is crucial. However, existing security and environmental control systems struggle to respond flexibly based on residents' emotional states, and their ability to quickly and appropriately address anomalies is insufficient. To solve this problem, the introduction of more advanced systems incorporating emotion analysis is required.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes a function to collect data from multiple information gathering means, a function to process that data using a generation algorithm and identify anomalies, and a function to construct countermeasures according to a pre-set safety plan when an anomaly is identified. This enables the rapid implementation of countermeasures when an anomaly occurs and the optimization of the environment based on the emotional state of residents.

[0352] "Information gathering means" refers to sensors and devices used to acquire diverse data.

[0353] A "generative algorithm" is a program that analyzes collected data to identify specific patterns or anomalies.

[0354] The "function for identifying anomalies" is a process for identifying phenomena that differ from normal data or situations.

[0355] A "safety measures plan" is a set of pre-defined action plans for responding to abnormal situations.

[0356] The "function of developing countermeasures" refers to the process of planning and preparing, in a feasible form, the actions to be taken when an anomaly is identified.

[0357] A "control device" is a machine or system used to control equipment in a home or public space.

[0358] A "user device" is a terminal or device used to provide information to a user and receive operating instructions.

[0359] "Emotional analysis" is the process of inferring a user's emotional state from collected data and analyzing the results.

[0360] The "environment adjustment function" refers to the function of appropriately changing the lighting, music, and other elements of a space based on acquired emotional data.

[0361] The system implementing this invention uses a mechanism to collect data from various sensors and information gathering devices installed throughout a city or within individual homes. A server takes in the data collected from these sensors in real time and processes it using a generation algorithm to identify anomalies. If an anomaly is identified, the server quickly develops countermeasures based on a pre-configured safety plan and sends instructions to the management device. As a result, the management device in the city or home performs actions such as controlling entrances and exits or adjusting light sources.

[0362] The server also performs emotion analysis simultaneously, inferring the user's emotional state from the collected data. Based on this analysis, it can send instructions to the management device to optimize the environment in public spaces or homes. For example, for a user analyzed as feeling stressed, the server could play relaxation music or change the lighting to a warmer tone.

[0363] As a concrete example, by monitoring park usage through IoT sensors, if there are many visitors on gloomy weather days, the environment can be improved by making the lights brighter or playing cheerful music. The main hardware used includes IoT sensors from various manufacturers and AWS EC2 instances as servers. On the software side, programs are written in Python, and TensorFlow is used for the generated AI models.

[0364] An example of a prompt to be input into the generating AI model would be: "In order to create an urban environment that citizens find comfortable, please use data collected from sensors to appropriately determine emotional states and work towards creating a safe and happy space."

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server collects data from information gathering devices installed in homes and public spaces. Inputs include raw data obtained from motion sensors, sound sensors, cameras, and other sources. This data is temporarily stored, formatted, and converted into an analyzable format.

[0368] Step 2:

[0369] The server inputs the formatted data into a generation AI model, which then analyzes the data. This analysis identifies the presence or absence of anomalies. The input is pre-processed data, and the output is the presence or absence of anomalies and the type of anomaly. Using the analysis results, if anomalies are found, the system identifies situations requiring further action.

[0370] Step 3:

[0371] When the server detects an anomaly, it develops a countermeasure based on a pre-configured safety plan. The input is the result of the anomaly identification, and the output is a detailed protocol for the countermeasure. The server sends this protocol to the management device, preparing to take physical control.

[0372] Step 4:

[0373] The control device performs actions such as locking doors and adjusting lighting based on instructions from the server. The input is the corresponding protocol received from the server, and the output is the actual control action. This improves security in homes or public spaces.

[0374] Step 5:

[0375] The server performs emotion analysis based on the collected sensor data. The input is formatted sensor data, and the output is the user's emotional state (e.g., relaxed, stressed). The server sends environment optimization instructions to the management device according to the emotional state.

[0376] Step 6:

[0377] The management device adjusts the environment according to the user's emotional state, following environment optimization instructions received from the server. The input is the emotional optimization instructions from the server, and the output is a change in the environment (e.g., music playback, change in lighting color).

[0378] Step 7:

[0379] The server sends notifications to the user's device regarding the situation and countermeasures. The input is the results of anomaly and sentiment analysis and the countermeasures, and the output is the content of the notification to the user. This allows the user to understand information about their environment and safety status in real time.

[0380] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0387] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0389] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0391] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0392] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] This invention provides a security system for protecting family safety using multiple sensor devices within the home. In this system, data acquired from the sensor devices is analyzed on a server, and if an anomaly is detected, appropriate security measures are automatically planned and executed.

[0397] First, the device monitors the conditions inside the home via Wi-Fi, UWB, and sensors such as temperature, humidity, audio, and cameras. Each sensor has communication capabilities to periodically acquire data and send it to a server. This data includes patterns of movement within the home, environmental changes, and audio information.

[0398] The server analyzes incoming data based on a generative model. The generative model learns unusual behaviors and sound patterns, and detects anomalies by comparing them. After detecting an anomaly, the server plans countermeasures based on a pre-configured security plan. This plan includes specific actions such as locking smart locks on doors, turning on lights, activating alarms, and sending notifications to users.

[0399] If an anomaly is detected, the device will take action according to the security plan via the home management system. For example, if movement is detected in a room that should be empty, all lights in that room will immediately turn on and an alarm will sound. Furthermore, when an anomaly is detected, a notification will be sent to the user's smartphone, displaying detailed information about the situation and the countermeasures taken by the system.

[0400] This system allows users to monitor the situation at their home even from a physical distance and to quickly give instructions to family members at home as needed. For example, if an intrusion occurs while the user is away, the user's terminal will receive a notification from the system, allowing them to immediately view the footage and issue an alarm to neighbors. In this way, the system provides peace of mind to users by demonstrating a high level of security.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The device activates various sensor devices installed in the home and continuously collects data. These include WiFi sensors, UWB sensors, temperature and humidity sensors, sound sensors, and camera sensors. Each sensor records data at specific intervals, and the device temporarily stores the acquired data.

[0404] Step 2:

[0405] The terminal sends data collected at regular intervals to the server. The transmitted data includes readings from each sensor and a timestamp. The terminal monitors the data communication status and confirms that the transmission was successful.

[0406] Step 3:

[0407] Based on the data received by the server, the data is input into a generative model for analysis. The generative model learns about normal conditions based on past data and calculates an anomaly score by comparing it with the new data. If this score exceeds a threshold, the server determines that an anomaly has occurred.

[0408] Step 4:

[0409] When an anomaly is detected, the server selects the most appropriate response from a pre-configured security plan and formulates a plan. This plan includes various countermeasures, and the server selects the most effective one based on its judgment.

[0410] Step 5:

[0411] The server sends the selected countermeasure as an instruction to the terminal. Based on the received instruction, the terminal controls the home security device and executes the actual security measures. For example, it may lock the smart lock and turn on the lights throughout the house.

[0412] Step 6:

[0413] The terminal sends a notification to the user's device regarding the detection of an anomaly and the implementation of countermeasures. This notification includes the nature of the anomaly, the measures taken, and the current situation in the home. Based on the notification, the user can check detailed information from the application.

[0414] (Example 1)

[0415] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0416] To ensure safety within the home, it is necessary to efficiently analyze data from multiple sensor devices and quickly and accurately detect potential anomalies. Furthermore, it is essential that appropriate responses be taken promptly in the event of an anomaly, and it is desirable that users in remote locations be able to monitor the situation in real time. The challenge lies in realizing such a comprehensive security system.

[0417] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0418] In this invention, the server includes means for collecting information from multiple measuring devices, means for analyzing the information using a generative model and detecting anomalies, and means for allowing users to remotely check the situation through a video device when an anomaly is detected. This enables advanced security management within the home and allows for a quick and appropriate response to potential dangers.

[0419] A "measuring device" is a device used to measure environmental and physical conditions and collect that data as information.

[0420] "Information" refers to data acquired from measuring devices and insights gained during the analysis process.

[0421] A "generative model" is an algorithm trained using machine learning or artificial intelligence techniques to identify normal patterns and anomalies.

[0422] "Anomaly" is a term that refers to behavior or a state that deviates from normal operating conditions or expected patterns.

[0423] A "crime prevention plan" refers to a series of measures and operational procedures that are implemented in response to the detection of anomalies.

[0424] A "barrier" is a device used to restrict access, and includes, for example, door locking devices.

[0425] "Light source" refers to lighting fixtures and other light-emitting equipment within a home.

[0426] A "user device" is a device owned by the user that allows them to receive notifications from the system or check their home status.

[0427] A "video device" is equipment used to acquire real-time visual information using cameras or similar devices.

[0428] This invention relates to a security system for ensuring safety within the home. This system collects environmental information using multiple measuring devices and analyzes it based on a generative model to detect unusual behavior and automatically take necessary security measures.

[0429] The server collects information from measuring devices such as WiFi, UWB, temperature, humidity, audio, and cameras, and analyzes this information using a generative AI model. The generative AI model has learned normal operating patterns and audio data, and detects anomalies by comparing them with this data. If an anomaly is detected, the server plans and executes appropriate countermeasures based on a pre-configured security plan.

[0430] Specific countermeasures include the server controlling home security devices within the home, such as locking circuit breakers and operating light sources. Furthermore, if an anomaly is detected, a notification is sent to the user's device, allowing the user to remotely monitor the situation inside the home in real time via video equipment.

[0431] The device receives a notification from the server if it detects an intrusion while the user is away, and quickly reviews the footage. The system also includes an example of a prompt for the AI ​​model that generates messages for anomaly detection: "Identify normal activity patterns in the home and suggest security measures to be taken in the event of an intrusion."

[0432] Through these features, users can monitor the safety of their homes and implement advanced security measures even from a physically distant location. Furthermore, the goal of this system is to provide users with a safe environment in which they can live with peace of mind.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The terminal continuously collects environmental information using various in-home measuring devices (WiFi, UWB, temperature, humidity, audio, camera, etc.). Input data from these measuring devices includes, for example, temperature, humidity, sound intensity, and video data. This information is periodically transmitted to the server. The output is raw data sent to the server.

[0436] Step 2:

[0437] The server receives information transmitted from the terminal and performs data analysis using a generative AI model. The input here is a wide variety of environmental data sent from the terminal. The generative AI model analyzes this data using normal patterns and speech to determine whether or not an anomaly is present. The output is a result indicating whether or not an anomaly was detected.

[0438] Step 3:

[0439] If the server detects an anomaly based on the analysis results, it plans appropriate countermeasures according to a pre-configured security plan. The input is the analysis results of a generated AI model. Data processing includes comparing the analysis results with security plan data. The output is a list of specific countermeasures.

[0440] Step 4:

[0441] The server controls the home management devices in the home based on planned actions. The input to this step is the pre-planned action. For example, the server might lock a door shutoff or operate a light source to turn on the lights. The output is a control signal sent to the home management device, which then performs the actual operation.

[0442] Step 5:

[0443] The server sends a notification to the user's device so that the user can remotely check for the occurrence of an anomaly and the countermeasures taken. This step takes the results of the security plan's implementation as input. The server notifies the user's device of the situation and, if necessary, enables remote video verification. The notification is displayed on the user's device as output.

[0444] This allows users to respond quickly in the event of an anomaly, ensuring the safety of their homes.

[0445] (Application Example 1)

[0446] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] In modern urban environments, public safety is a critical issue. In particular, it is essential to detect and address any abnormal incidents that threaten safety at an early stage. However, current security systems are often limited to individual homes and facilities, lacking integrated safety management across broader areas. Against this backdrop, there is a need for a means of monitoring and managing public safety in a unified and efficient manner across a wide area.

[0448] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0449] In this invention, the server includes means for collecting information from multiple sensor devices, means for analyzing the information using a generative model to identify anomalies, and means for integrating information from monitoring equipment in public facilities in the event of an anomaly to enable monitoring of citizen safety. This enables efficient monitoring of safety throughout the city and allows for a rapid response.

[0450] A "sensor device" is a device used to acquire information from the environment and has the function of collecting various data such as temperature, humidity, sound, and images.

[0451] A "generative model" is an algorithm or method used to analyze data acquired from sensors, which learns unusual patterns and enables the detection of anomalies.

[0452] "Means for identifying anomalies" refer to systems that analyze collected data and have the function of detecting behaviors or states that deviate from normal patterns.

[0453] A "disaster prevention plan" is a document that pre-determines specific countermeasures to be taken when an abnormality is detected, and includes things like locking doors and operating lights.

[0454] A "residential area management device" is a device installed in a home or facility to comprehensively manage various sensors and system operations.

[0455] A "personal information terminal" is an electronic device that a user can carry with them and use to report situations and check information.

[0456] "Public facility monitoring equipment" refers to devices installed in public areas within cities to monitor the surrounding environment in real time.

[0457] To implement this invention, it is necessary to construct a system that integrates sensor equipment, a residential management device, and a portable information terminal. The sensor equipment acquires information such as temperature, humidity, sound, and images, and transmits the data to a server. The server analyzes the collected data using a generation AI model and identifies anomalies. If an anomaly is detected, the server controls the residential management device based on the disaster prevention plan, performing actions such as locking doors and operating lights.

[0458] The server also reports detected anomalies to mobile devices, allowing users to stay informed in real time. By integrating information from monitoring equipment in public facilities, this system can monitor public safety throughout the city.

[0459] This system processes vast amounts of information acquired from sensor devices in the cloud as part of the data processing and calculation process, and identifies anomalies based on generative models. This makes it possible to efficiently manage the safety of the entire city.

[0460] As a concrete example, if a suspicious person is detected in a public park late at night, the system can immediately turn on all the lights in the park and notify the nearest security agency of the anomaly. In this case, based on the anomaly analyzed using a generative AI model, a prompt message such as "Suspicious activity has been detected in the park. Please promptly provide details of the incident and recommended countermeasures." can be used.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] The server receives data from multiple sensor devices. This data includes information such as temperature, humidity, sound, and images. The server acquires this data in real time and prepares it for analysis. The input is raw data from the sensor devices, and the output is an integrated dataset.

[0464] Step 2:

[0465] The server analyzes the received data based on a generative AI model. The generative AI model learns normal behavioral patterns and performs comparative analysis to identify anomalies in the data. The input is an integrated dataset, and the output is whether or not anomalies were detected and their details.

[0466] Step 3:

[0467] If an anomaly is detected, the server will formulate a response based on the disaster prevention plan. It will determine specific actions such as locking doors, operating lights, or issuing warning sounds. The input in this step is the anomaly detection information, and the output is the specific countermeasure to be implemented.

[0468] Step 4:

[0469] The terminal executes planned countermeasures through the residential management device. Specifically, this includes actions such as locking smart locks, turning on indoor lights, and sounding alarms. The input is the formulated countermeasures, and the output is the result of the execution of physical and electronic controls.

[0470] Step 5:

[0471] The server notifies the user's mobile device of the detected anomaly and the countermeasures taken. The user can check the details of the situation and the actions taken in real time. The input here is information about the anomaly and the countermeasures taken, and the output is notification data for user reporting.

[0472] Step 6:

[0473] The server integrates information from monitoring equipment at public facilities and continues to monitor the safety of the entire city. If an anomaly spreads at the city level, it considers and implements further appropriate countermeasures. The input for this step is additional data from public facilities, and the output is an assessment of the safety of the entire city.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention is a security system that combines multiple in-home sensor devices with an emotion engine. This system uses data obtained from in-home sensors to detect anomalies and infers the user's emotional state, thereby providing more personalized security measures and a comfortable environment.

[0476] First, the device collects data in real time through sensor devices installed in the home. This data includes human movement detected by WiFi sensors, conversations and ambient sounds detected by voice sensors, and video information from camera sensors. This data is temporarily stored on the device and sent to the server at regular intervals.

[0477] The server analyzes the received data using a generative model to detect anomalies. This allows for the early detection of potential threats within the home. If an anomaly is detected, the server plans and implements the optimal countermeasures based on a pre-configured security plan.

[0478] Furthermore, this system incorporates an emotion engine. The server analyzes the user's emotional state based on voice and behavioral data obtained from sensors. The emotion engine determines emotional states such as stress, relaxation, and alertness, and generates instructions to control the home management device accordingly.

[0479] For example, if the emotion engine determines that the user is feeling fatigued, the server sends instructions to the home management device to adjust the lighting to a warmer color and play relaxation music. Conversely, if the system determines that the user is under high stress, it will optimize the environment according to the user's emotions, such as displaying images of refreshing scenery on the camera display.

[0480] In this way, the collaboration between the emotion engine and the generative model allows users to ensure daily peace of mind while simultaneously receiving personalized support tailored to their emotional state. As a result, family safety and quality of life are improved.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The device collects data from sensor devices installed in the home. This includes motion detection using WiFi sensors, acquisition of audio data from audio sensors, and recording of video from camera sensors. This data is stored in a buffer at regular intervals.

[0484] Step 2:

[0485] The terminal sends the data in the buffer to the server. The transmission includes data packets containing information from various sensors, organized in chronological order. Data transmission occurs only when stable communication is ensured.

[0486] Step 3:

[0487] The server analyzes the received data. Using a generative model, it compares it to normal behavioral patterns to detect anomalies. An anomaly score is calculated during this process, and if it exceeds a set threshold, an anomaly is detected.

[0488] Step 4:

[0489] When an anomaly is detected, the server consults a pre-prepared security plan and devises a response. This plan includes how to control which home security devices, selecting the quickest and most effective method.

[0490] Step 5:

[0491] The server uses an emotion engine to analyze the user's emotions from voice data and behavioral patterns. The emotion engine determines the user's stress level and relaxation state, and uses the results to generate instructions for environmental control.

[0492] Step 6:

[0493] The device controls the home management system based on instructions from the server. Specific operations include locking smart locks, changing lighting colors, and selecting music, all of which are performed automatically according to the user's emotional state.

[0494] Step 7:

[0495] The terminal notifies the user's device of the status of anomaly detection and response, as well as the results of the user's sentiment analysis. Through this notification, the user can check the current situation in their home and the details of security measures.

[0496] (Example 2)

[0497] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] It is difficult to provide a comfortable environment based on the emotional state of residents while responding quickly to sudden emergencies and security risks within the home. Furthermore, current systems cannot optimize the environment in accordance with emotions, resulting in the challenge of not being able to achieve both resident comfort and safety.

[0499] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0500] In this invention, the server includes means for collecting information from multiple detection devices, means for analyzing the information using a generation algorithm to detect anomalies, and means for inferring emotional states based on the analyzed information. This enables early detection of abnormal situations as well as appropriate environmental control in response to emotions.

[0501] A "detection device" is a device that uses sensor technology to collect information from the environment and has the function of acquiring data such as human movement, sound, and images.

[0502] A "generative algorithm" is a computational method for finding specific patterns or trends by analyzing a large amount of input information, and it is a mechanism for gaining new insights using AI technology.

[0503] An "anomaly" refers to an event or behavior that exceeds the normal range, and includes potential dangers and security risks detected by the system.

[0504] "Emotional state" refers to an individual's psychological or emotional condition and includes states such as stress and relaxation, which are determined through analysis based on voice and behavioral data.

[0505] An "environmental control device" is a device that controls household appliances such as lighting and sound equipment to adjust the conditions of a living space, and has the function of improving user comfort.

[0506] A "user device" is a device that a user can directly operate and use to receive notifications of abnormalities or status changes, and includes mobile terminals and tablets.

[0507] This system aims to improve safety and comfort within the home and is implemented with a configuration that includes multiple detection devices, generation algorithms, environmental control devices, and user devices.

[0508] The device collects environmental information in various formats using detection devices installed in the home, such as WiFi sensors, voice sensors, and camera sensors. The WiFi sensor acquires data on people's movements in the room, the voice sensor records surrounding sounds and conversations, and the camera sensor captures visual information. This data is temporarily stored on the device and sent to the server at regular intervals.

[0509] The server analyzes the received data using a generation algorithm. AI model analysis is used to find useful patterns in large amounts of data and to detect anomalies that threaten safety within the home at an early stage. If an anomaly is detected, necessary countermeasures are devised based on a pre-configured safety plan.

[0510] Furthermore, the server employs an emotion engine for sentiment analysis, inferring the user's emotional state from voice and movement data. Based on this analysis, instructions are sent to the environmental control system, which then appropriately optimizes the home environment through, for example, lighting adjustments and sound effects. Specifically, if the user needs to relax, warm-colored lighting and relaxation music are used, while if they are feeling stressed, refreshing images are played.

[0511] Users have a user device that notifies them of detected anomalies and environmental changes, and can check the situation in their home in real time via their smartphone or tablet. Examples of prompt messages include, "Based on sensor data, please consider countermeasures when a person is experiencing high stress. Please suggest ways to optimize the environment," and "Please give specific examples of the home environment that should be provided when the user is relaxed."

[0512] This system allows users to improve safety within their homes while also obtaining a comfortable living environment that responds to their emotions.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1:

[0515] The device collects input data from Wi-Fi sensors, voice sensors, and camera sensors installed in the home. The Wi-Fi sensor detects the movement of residents, the voice sensor records conversations and ambient sounds, and the camera sensor captures visual information. This data is temporarily stored in the device's memory. The output of this step is a set of sensor data recorded in chronological order.

[0516] Step 2:

[0517] The terminal transmits the accumulated sensor data to the server at predetermined intervals. For security reasons, the data is encrypted before being transmitted to the server via the internet. The input to this step is the sensor data stored in the terminal, and the output is the transmission of encrypted data to the server.

[0518] Step 3:

[0519] The server analyzes the received sensor data using a generating AI model. The algorithm compares the data to normal conditions and detects abnormal patterns. For example, if abnormal movement or noise is detected, the server determines it to be an anomaly. The input for this step is the transmitted sensor data, and the output is the anomaly detection result.

[0520] Step 4:

[0521] If an anomaly is detected, the server plans and implements the optimal response according to a pre-configured safety plan. For example, if suspicious activity is detected, it records footage from security cameras and activates an alarm. The input to this step is the anomaly detection result, and the output is the implementation of specific safety measures.

[0522] Step 5:

[0523] The server uses an emotion engine to infer the user's emotional state based on sensor data. It analyzes the tone and behavioral patterns of voice data to determine emotions such as stress and relaxation. The input for this step is sensor data, and the output is an evaluation of the user's emotional state.

[0524] Step 6:

[0525] Based on the emotional state, the server sends specific instructions to the environmental control unit to control the home environment. For example, if relaxation is needed, the lighting is adjusted to warmer colors and relaxation music is played. The input for this step is the result of the emotional analysis, and the output is the optimization of the home environment.

[0526] Step 7:

[0527] Users receive notifications about detected anomalies and changes to the environment. They can then access detailed information and take additional action as needed via a smartphone app or tablet. The input in this step is a notification from the server, and the output is information provided to the user.

[0528] (Application Example 2)

[0529] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] In modern smart cities, ensuring the safety of residents while providing a comfortable living environment is crucial. However, existing security and environmental control systems struggle to respond flexibly based on residents' emotional states, and their ability to quickly and appropriately address anomalies is insufficient. To solve this problem, the introduction of more advanced systems incorporating emotion analysis is required.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0532] In this invention, the server includes a function to collect data from multiple information gathering means, a function to process that data using a generation algorithm and identify anomalies, and a function to construct countermeasures according to a pre-set safety plan when an anomaly is identified. This enables the rapid implementation of countermeasures when an anomaly occurs and the optimization of the environment based on the emotional state of residents.

[0533] "Information gathering means" refers to sensors and devices used to acquire diverse data.

[0534] A "generative algorithm" is a program that analyzes collected data to identify specific patterns or anomalies.

[0535] The "function for identifying anomalies" is a process for identifying phenomena that differ from normal data or situations.

[0536] A "safety measures plan" is a set of pre-defined action plans for responding to abnormal situations.

[0537] The "function of developing countermeasures" refers to the process of planning and preparing, in a feasible form, the actions to be taken when an anomaly is identified.

[0538] A "control device" is a machine or system used to control equipment in a home or public space.

[0539] A "user device" is a terminal or device used to provide information to a user and receive operating instructions.

[0540] "Emotional analysis" is the process of inferring a user's emotional state from collected data and analyzing the results.

[0541] The "environment adjustment function" refers to the function of appropriately changing the lighting, music, and other elements of a space based on acquired emotional data.

[0542] The system implementing this invention uses a mechanism to collect data from various sensors and information gathering devices installed throughout a city or within individual homes. A server takes in the data collected from these sensors in real time and processes it using a generation algorithm to identify anomalies. If an anomaly is identified, the server quickly develops countermeasures based on a pre-configured safety plan and sends instructions to the management device. As a result, the management device in the city or home performs actions such as controlling entrances and exits or adjusting light sources.

[0543] The server also performs emotion analysis simultaneously, inferring the user's emotional state from the collected data. Based on this analysis, it can send instructions to the management device to optimize the environment in public spaces or homes. For example, for a user analyzed as feeling stressed, the server could play relaxation music or change the lighting to a warmer tone.

[0544] As a concrete example, by monitoring park usage through IoT sensors, if there are many visitors on gloomy weather days, the environment can be improved by making the lights brighter or playing cheerful music. The main hardware used includes IoT sensors from various manufacturers and AWS EC2 instances as servers. On the software side, programs are written in Python, and TensorFlow is used for the generated AI models.

[0545] An example of a prompt to be input into the generating AI model would be: "In order to create an urban environment that citizens find comfortable, please use data collected from sensors to appropriately determine emotional states and work towards creating a safe and happy space."

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The server collects data from information gathering devices installed in homes and public spaces. Inputs include raw data obtained from motion sensors, sound sensors, cameras, and other sources. This data is temporarily stored, formatted, and converted into an analyzable format.

[0549] Step 2:

[0550] The server inputs the formatted data into a generation AI model, which then analyzes the data. This analysis identifies the presence or absence of anomalies. The input is pre-processed data, and the output is the presence or absence of anomalies and the type of anomaly. Using the analysis results, if anomalies are found, the system identifies situations requiring further action.

[0551] Step 3:

[0552] When the server detects an anomaly, it develops a countermeasure based on a pre-configured safety plan. The input is the result of the anomaly identification, and the output is a detailed protocol for the countermeasure. The server sends this protocol to the management device, preparing to take physical control.

[0553] Step 4:

[0554] The control device performs actions such as locking doors and adjusting lighting based on instructions from the server. The input is the corresponding protocol received from the server, and the output is the actual control action. This improves security in homes or public spaces.

[0555] Step 5:

[0556] The server performs emotion analysis based on the collected sensor data. The input is formatted sensor data, and the output is the user's emotional state (e.g., relaxed, stressed). The server sends environment optimization instructions to the management device according to the emotional state.

[0557] Step 6:

[0558] The management device adjusts the environment according to the user's emotional state, following environment optimization instructions received from the server. The input is the emotional optimization instructions from the server, and the output is a change in the environment (e.g., music playback, change in lighting color).

[0559] Step 7:

[0560] The server sends notifications to the user's device regarding the situation and countermeasures. The input is the results of anomaly and sentiment analysis and the countermeasures, and the output is the content of the notification to the user. This allows the user to understand information about their environment and safety status in real time.

[0561] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0562] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0567] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0568] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0569] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0570] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0571] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0572] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0573] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0574] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0575] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0576] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0577] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0578] This invention provides a security system for protecting family safety using multiple sensor devices within the home. In this system, data acquired from the sensor devices is analyzed on a server, and if an anomaly is detected, appropriate security measures are automatically planned and executed.

[0579] First, the device monitors the conditions inside the home via Wi-Fi, UWB, and sensors such as temperature, humidity, audio, and cameras. Each sensor has communication capabilities to periodically acquire data and send it to a server. This data includes patterns of movement within the home, environmental changes, and audio information.

[0580] The server analyzes incoming data based on a generative model. The generative model learns unusual behaviors and sound patterns, and detects anomalies by comparing them. After detecting an anomaly, the server plans countermeasures based on a pre-configured security plan. This plan includes specific actions such as locking smart locks on doors, turning on lights, activating alarms, and sending notifications to users.

[0581] If an anomaly is detected, the device will take action according to the security plan via the home management system. For example, if movement is detected in a room that should be empty, all lights in that room will immediately turn on and an alarm will sound. Furthermore, when an anomaly is detected, a notification will be sent to the user's smartphone, displaying detailed information about the situation and the countermeasures taken by the system.

[0582] This system allows users to monitor the situation at their home even from a physical distance and to quickly give instructions to family members at home as needed. For example, if an intrusion occurs while the user is away, the user's terminal will receive a notification from the system, allowing them to immediately view the footage and issue an alarm to neighbors. In this way, the system provides peace of mind to users by demonstrating a high level of security.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The device activates various sensor devices installed in the home and continuously collects data. These include WiFi sensors, UWB sensors, temperature and humidity sensors, sound sensors, and camera sensors. Each sensor records data at specific intervals, and the device temporarily stores the acquired data.

[0586] Step 2:

[0587] The terminal sends data collected at regular intervals to the server. The transmitted data includes readings from each sensor and a timestamp. The terminal monitors the data communication status and confirms that the transmission was successful.

[0588] Step 3:

[0589] Based on the data received by the server, the data is input into a generative model for analysis. The generative model learns about normal conditions based on past data and calculates an anomaly score by comparing it with the new data. If this score exceeds a threshold, the server determines that an anomaly has occurred.

[0590] Step 4:

[0591] When an anomaly is detected, the server selects the most appropriate response from a pre-configured security plan and formulates a plan. This plan includes various countermeasures, and the server selects the most effective one based on its judgment.

[0592] Step 5:

[0593] The server sends the selected countermeasure as an instruction to the terminal. Based on the received instruction, the terminal controls the home security device and executes the actual security measures. For example, it may lock the smart lock and turn on the lights throughout the house.

[0594] Step 6:

[0595] The terminal sends a notification to the user's device regarding the detection of an anomaly and the implementation of countermeasures. This notification includes the nature of the anomaly, the measures taken, and the current situation in the home. Based on the notification, the user can check detailed information from the application.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] To ensure safety within the home, it is necessary to efficiently analyze data from multiple sensor devices and quickly and accurately detect potential anomalies. Furthermore, it is essential that appropriate responses be taken promptly in the event of an anomaly, and it is desirable that users in remote locations be able to monitor the situation in real time. The challenge lies in realizing such a comprehensive security system.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes means for collecting information from multiple measuring devices, means for analyzing the information using a generative model and detecting anomalies, and means for allowing users to remotely check the situation through a video device when an anomaly is detected. This enables advanced security management within the home and allows for a quick and appropriate response to potential dangers.

[0601] A "measuring device" is a device used to measure environmental and physical conditions and collect that data as information.

[0602] "Information" refers to data acquired from measuring devices and insights gained during the analysis process.

[0603] A "generative model" is an algorithm trained using machine learning or artificial intelligence techniques to identify normal patterns and anomalies.

[0604] "Anomaly" is a term that refers to behavior or a state that deviates from normal operating conditions or expected patterns.

[0605] A "crime prevention plan" refers to a series of measures and operational procedures that are implemented in response to the detection of anomalies.

[0606] A "barrier" is a device used to restrict access, and includes, for example, door locking devices.

[0607] "Light source" refers to lighting fixtures and other light-emitting equipment within a home.

[0608] A "user device" is a device owned by the user that allows them to receive notifications from the system or check their home status.

[0609] A "video device" is equipment used to acquire real-time visual information using cameras or similar devices.

[0610] This invention relates to a security system for ensuring safety within the home. This system collects environmental information using multiple measuring devices and analyzes it based on a generative model to detect unusual behavior and automatically take necessary security measures.

[0611] The server collects information from measuring devices such as WiFi, UWB, temperature, humidity, audio, and cameras, and analyzes this information using a generative AI model. The generative AI model has learned normal operating patterns and audio data, and detects anomalies by comparing them with this data. If an anomaly is detected, the server plans and executes appropriate countermeasures based on a pre-configured security plan.

[0612] Specific countermeasures include the server controlling home security devices within the home, such as locking circuit breakers and operating light sources. Furthermore, if an anomaly is detected, a notification is sent to the user's device, allowing the user to remotely monitor the situation inside the home in real time via video equipment.

[0613] The device receives a notification from the server if it detects an intrusion while the user is away, and quickly reviews the footage. The system also includes an example of a prompt for the AI ​​model that generates messages for anomaly detection: "Identify normal activity patterns in the home and suggest security measures to be taken in the event of an intrusion."

[0614] Through these features, users can monitor the safety of their homes and implement advanced security measures even from a physically distant location. Furthermore, the goal of this system is to provide users with a safe environment in which they can live with peace of mind.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The terminal continuously collects environmental information using various in-home measuring devices (WiFi, UWB, temperature, humidity, audio, camera, etc.). Input data from these measuring devices includes, for example, temperature, humidity, sound intensity, and video data. This information is periodically transmitted to the server. The output is raw data sent to the server.

[0618] Step 2:

[0619] The server receives information transmitted from the terminal and performs data analysis using a generative AI model. The input here is a wide variety of environmental data sent from the terminal. The generative AI model analyzes this data using normal patterns and speech to determine whether or not an anomaly is present. The output is a result indicating whether or not an anomaly was detected.

[0620] Step 3:

[0621] If the server detects an anomaly based on the analysis results, it plans appropriate countermeasures according to a pre-configured security plan. The input is the analysis results of a generated AI model. Data processing includes comparing the analysis results with security plan data. The output is a list of specific countermeasures.

[0622] Step 4:

[0623] The server controls the home management devices in the home based on planned actions. The input to this step is the pre-planned action. For example, the server might lock a door shutoff or operate a light source to turn on the lights. The output is a control signal sent to the home management device, which then performs the actual operation.

[0624] Step 5:

[0625] The server sends a notification to the user's device so that the user can remotely check for the occurrence of an anomaly and the countermeasures taken. This step takes the results of the security plan's implementation as input. The server notifies the user's device of the situation and, if necessary, enables remote video verification. The notification is displayed on the user's device as output.

[0626] This allows users to respond quickly in the event of an anomaly, ensuring the safety of their homes.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] In modern urban environments, public safety is a critical issue. In particular, it is essential to detect and address any abnormal incidents that threaten safety at an early stage. However, current security systems are often limited to individual homes and facilities, lacking integrated safety management across broader areas. Against this backdrop, there is a need for a means of monitoring and managing public safety in a unified and efficient manner across a wide area.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes means for collecting information from multiple sensor devices, means for analyzing the information using a generative model to identify anomalies, and means for integrating information from monitoring equipment in public facilities in the event of an anomaly to enable monitoring of citizen safety. This enables efficient monitoring of safety throughout the city and allows for a rapid response.

[0632] A "sensor device" is a device used to acquire information from the environment and has the function of collecting various data such as temperature, humidity, sound, and images.

[0633] A "generative model" is an algorithm or method used to analyze data acquired from sensors, which learns unusual patterns and enables the detection of anomalies.

[0634] "Means for identifying anomalies" refer to systems that analyze collected data and have the function of detecting behaviors or states that deviate from normal patterns.

[0635] A "disaster prevention plan" is a document that pre-determines specific countermeasures to be taken when an abnormality is detected, and includes things like locking doors and operating lights.

[0636] A "residential area management device" is a device installed in a home or facility to comprehensively manage various sensors and system operations.

[0637] A "personal information terminal" is an electronic device that a user can carry with them and use to report situations and check information.

[0638] "Public facility monitoring equipment" refers to devices installed in public areas within cities to monitor the surrounding environment in real time.

[0639] To implement this invention, it is necessary to construct a system that integrates sensor equipment, a residential management device, and a portable information terminal. The sensor equipment acquires information such as temperature, humidity, sound, and images, and transmits the data to a server. The server analyzes the collected data using a generation AI model and identifies anomalies. If an anomaly is detected, the server controls the residential management device based on the disaster prevention plan, performing actions such as locking doors and operating lights.

[0640] The server also reports detected anomalies to mobile devices, allowing users to stay informed in real time. By integrating information from monitoring equipment in public facilities, this system can monitor public safety throughout the city.

[0641] This system processes vast amounts of information acquired from sensor devices in the cloud as part of the data processing and calculation process, and identifies anomalies based on generative models. This makes it possible to efficiently manage the safety of the entire city.

[0642] As a concrete example, if a suspicious person is detected in a public park late at night, the system can immediately turn on all the lights in the park and notify the nearest security agency of the anomaly. In this case, based on the anomaly analyzed using a generative AI model, a prompt message such as "Suspicious activity has been detected in the park. Please promptly provide details of the incident and recommended countermeasures." can be used.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] The server receives data from multiple sensor devices. This data includes information such as temperature, humidity, sound, and images. The server acquires this data in real time and prepares it for analysis. The input is raw data from the sensor devices, and the output is an integrated dataset.

[0646] Step 2:

[0647] The server analyzes the received data based on a generative AI model. The generative AI model learns normal behavioral patterns and performs comparative analysis to identify anomalies in the data. The input is an integrated dataset, and the output is whether or not anomalies were detected and their details.

[0648] Step 3:

[0649] If an anomaly is detected, the server will formulate a response based on the disaster prevention plan. It will determine specific actions such as locking doors, operating lights, or issuing warning sounds. The input in this step is the anomaly detection information, and the output is the specific countermeasure to be implemented.

[0650] Step 4:

[0651] The terminal executes planned countermeasures through the residential management device. Specifically, this includes actions such as locking smart locks, turning on indoor lights, and sounding alarms. The input is the formulated countermeasures, and the output is the result of the execution of physical and electronic controls.

[0652] Step 5:

[0653] The server notifies the user's mobile device of the detected anomaly and the countermeasures taken. The user can check the details of the situation and the actions taken in real time. The input here is information about the anomaly and the countermeasures taken, and the output is notification data for user reporting.

[0654] Step 6:

[0655] The server integrates information from monitoring equipment at public facilities and continues to monitor the safety of the entire city. If an anomaly spreads at the city level, it considers and implements further appropriate countermeasures. The input for this step is additional data from public facilities, and the output is an assessment of the safety of the entire city.

[0656] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0657] This invention is a security system that combines multiple in-home sensor devices with an emotion engine. This system uses data obtained from in-home sensors to detect anomalies and infers the user's emotional state, thereby providing more personalized security measures and a comfortable environment.

[0658] First, the device collects data in real time through sensor devices installed in the home. This data includes human movement detected by WiFi sensors, conversations and ambient sounds detected by voice sensors, and video information from camera sensors. This data is temporarily stored on the device and sent to the server at regular intervals.

[0659] The server analyzes the received data using a generative model to detect anomalies. This allows for the early detection of potential threats within the home. If an anomaly is detected, the server plans and implements the optimal countermeasures based on a pre-configured security plan.

[0660] Furthermore, this system incorporates an emotion engine. The server analyzes the user's emotional state based on voice and behavioral data obtained from sensors. The emotion engine determines emotional states such as stress, relaxation, and alertness, and generates instructions to control the home management device accordingly.

[0661] For example, if the emotion engine determines that the user is feeling fatigued, the server sends instructions to the home management device to adjust the lighting to a warmer color and play relaxation music. Conversely, if the system determines that the user is under high stress, it will optimize the environment according to the user's emotions, such as displaying images of refreshing scenery on the camera display.

[0662] In this way, the collaboration between the emotion engine and the generative model allows users to ensure daily peace of mind while simultaneously receiving personalized support tailored to their emotional state. As a result, family safety and quality of life are improved.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The device collects data from sensor devices installed in the home. This includes motion detection using WiFi sensors, acquisition of audio data from audio sensors, and recording of video from camera sensors. This data is stored in a buffer at regular intervals.

[0666] Step 2:

[0667] The terminal sends the data in the buffer to the server. The transmission includes data packets containing information from various sensors, organized in chronological order. Data transmission occurs only when stable communication is ensured.

[0668] Step 3:

[0669] The server analyzes the received data. Using a generative model, it compares it to normal behavioral patterns to detect anomalies. An anomaly score is calculated during this process, and if it exceeds a set threshold, an anomaly is detected.

[0670] Step 4:

[0671] When an anomaly is detected, the server consults a pre-prepared security plan and devises a response. This plan includes how to control which home security devices, selecting the quickest and most effective method.

[0672] Step 5:

[0673] The server uses an emotion engine to analyze the user's emotions from voice data and behavioral patterns. The emotion engine determines the user's stress level and relaxation state, and uses the results to generate instructions for environmental control.

[0674] Step 6:

[0675] The device controls the home management system based on instructions from the server. Specific operations include locking smart locks, changing lighting colors, and selecting music, all of which are performed automatically according to the user's emotional state.

[0676] Step 7:

[0677] The terminal notifies the user's device of the status of anomaly detection and response, as well as the results of the user's sentiment analysis. Through this notification, the user can check the current situation in their home and the details of security measures.

[0678] (Example 2)

[0679] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] It is difficult to provide a comfortable environment based on the emotional state of residents while responding quickly to sudden emergencies and security risks within the home. Furthermore, current systems cannot optimize the environment in accordance with emotions, resulting in the challenge of not being able to achieve both resident comfort and safety.

[0681] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0682] In this invention, the server includes means for collecting information from multiple detection devices, means for analyzing the information using a generation algorithm to detect anomalies, and means for inferring emotional states based on the analyzed information. This enables early detection of abnormal situations as well as appropriate environmental control in response to emotions.

[0683] A "detection device" is a device that uses sensor technology to collect information from the environment and has the function of acquiring data such as human movement, sound, and images.

[0684] A "generative algorithm" is a computational method for finding specific patterns or trends by analyzing a large amount of input information, and it is a mechanism for gaining new insights using AI technology.

[0685] An "anomaly" refers to an event or behavior that exceeds the normal range, and includes potential dangers and security risks detected by the system.

[0686] "Emotional state" refers to an individual's psychological or emotional condition and includes states such as stress and relaxation, which are determined through analysis based on voice and behavioral data.

[0687] An "environmental control device" is a device that controls household appliances such as lighting and sound equipment to adjust the conditions of a living space, and has the function of improving user comfort.

[0688] A "user device" is a device that a user can directly operate and use to receive notifications of abnormalities or status changes, and includes mobile terminals and tablets.

[0689] This system aims to improve safety and comfort within the home and is implemented with a configuration that includes multiple detection devices, generation algorithms, environmental control devices, and user devices.

[0690] The device collects environmental information in various formats using detection devices installed in the home, such as WiFi sensors, voice sensors, and camera sensors. The WiFi sensor acquires data on people's movements in the room, the voice sensor records surrounding sounds and conversations, and the camera sensor captures visual information. This data is temporarily stored on the device and sent to the server at regular intervals.

[0691] The server analyzes the received data using a generation algorithm. AI model analysis is used to find useful patterns in large amounts of data and to detect anomalies that threaten safety within the home at an early stage. If an anomaly is detected, necessary countermeasures are devised based on a pre-configured safety plan.

[0692] Furthermore, the server employs an emotion engine for sentiment analysis, inferring the user's emotional state from voice and movement data. Based on this analysis, instructions are sent to the environmental control system, which then appropriately optimizes the home environment through, for example, lighting adjustments and sound effects. Specifically, if the user needs to relax, warm-colored lighting and relaxation music are used, while if they are feeling stressed, refreshing images are played.

[0693] Users have a user device that notifies them of detected anomalies and environmental changes, and can check the situation in their home in real time via their smartphone or tablet. Examples of prompt messages include, "Based on sensor data, please consider countermeasures when a person is experiencing high stress. Please suggest ways to optimize the environment," and "Please give specific examples of the home environment that should be provided when the user is relaxed."

[0694] This system allows users to improve safety within their homes while also obtaining a comfortable living environment that responds to their emotions.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The device collects input data from Wi-Fi sensors, voice sensors, and camera sensors installed in the home. The Wi-Fi sensor detects the movement of residents, the voice sensor records conversations and ambient sounds, and the camera sensor captures visual information. This data is temporarily stored in the device's memory. The output of this step is a set of sensor data recorded in chronological order.

[0698] Step 2:

[0699] The terminal transmits the accumulated sensor data to the server at predetermined intervals. For security reasons, the data is encrypted before being transmitted to the server via the internet. The input to this step is the sensor data stored in the terminal, and the output is the transmission of encrypted data to the server.

[0700] Step 3:

[0701] The server analyzes the received sensor data using a generating AI model. The algorithm compares the data to normal conditions and detects abnormal patterns. For example, if abnormal movement or noise is detected, the server determines it to be an anomaly. The input for this step is the transmitted sensor data, and the output is the anomaly detection result.

[0702] Step 4:

[0703] If an anomaly is detected, the server plans and implements the optimal response according to a pre-configured safety plan. For example, if suspicious activity is detected, it records footage from security cameras and activates an alarm. The input to this step is the anomaly detection result, and the output is the implementation of specific safety measures.

[0704] Step 5:

[0705] The server uses an emotion engine to infer the user's emotional state based on sensor data. It analyzes the tone and behavioral patterns of voice data to determine emotions such as stress and relaxation. The input for this step is sensor data, and the output is an evaluation of the user's emotional state.

[0706] Step 6:

[0707] Based on the emotional state, the server sends specific instructions to the environmental control unit to control the home environment. For example, if relaxation is needed, the lighting is adjusted to warmer colors and relaxation music is played. The input for this step is the result of the emotional analysis, and the output is the optimization of the home environment.

[0708] Step 7:

[0709] Users receive notifications about detected anomalies and changes to the environment. They can then access detailed information and take additional action as needed via a smartphone app or tablet. The input in this step is a notification from the server, and the output is information provided to the user.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] In modern smart cities, ensuring the safety of residents while providing a comfortable living environment is crucial. However, existing security and environmental control systems struggle to respond flexibly based on residents' emotional states, and their ability to quickly and appropriately address anomalies is insufficient. To solve this problem, the introduction of more advanced systems incorporating emotion analysis is required.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes a function to collect data from multiple information gathering means, a function to process that data using a generation algorithm and identify anomalies, and a function to construct countermeasures according to a pre-set safety plan when an anomaly is identified. This enables the rapid implementation of countermeasures when an anomaly occurs and the optimization of the environment based on the emotional state of residents.

[0715] "Information gathering means" refers to sensors and devices used to acquire diverse data.

[0716] A "generative algorithm" is a program that analyzes collected data to identify specific patterns or anomalies.

[0717] The "function for identifying anomalies" is a process for identifying phenomena that differ from normal data or situations.

[0718] A "safety measures plan" is a set of pre-defined action plans for responding to abnormal situations.

[0719] The "function of developing countermeasures" refers to the process of planning and preparing, in a feasible form, the actions to be taken when an anomaly is identified.

[0720] A "control device" is a machine or system used to control equipment in a home or public space.

[0721] A "user device" is a terminal or device used to provide information to a user and receive operating instructions.

[0722] "Emotional analysis" is the process of inferring a user's emotional state from collected data and analyzing the results.

[0723] The "environment adjustment function" refers to the function of appropriately changing the lighting, music, and other elements of a space based on acquired emotional data.

[0724] The system implementing this invention uses a mechanism to collect data from various sensors and information gathering devices installed throughout a city or within individual homes. A server takes in the data collected from these sensors in real time and processes it using a generation algorithm to identify anomalies. If an anomaly is identified, the server quickly develops countermeasures based on a pre-configured safety plan and sends instructions to the management device. As a result, the management device in the city or home performs actions such as controlling entrances and exits or adjusting light sources.

[0725] The server also performs emotion analysis simultaneously, inferring the user's emotional state from the collected data. Based on this analysis, it can send instructions to the management device to optimize the environment in public spaces or homes. For example, for a user analyzed as feeling stressed, the server could play relaxation music or change the lighting to a warmer tone.

[0726] As a concrete example, by monitoring park usage through IoT sensors, if there are many visitors on gloomy weather days, the environment can be improved by making the lights brighter or playing cheerful music. The main hardware used includes IoT sensors from various manufacturers and AWS EC2 instances as servers. On the software side, programs are written in Python, and TensorFlow is used for the generated AI models.

[0727] An example of a prompt to be input into the generating AI model would be: "In order to create an urban environment that citizens find comfortable, please use data collected from sensors to appropriately determine emotional states and work towards creating a safe and happy space."

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server collects data from information gathering devices installed in homes and public spaces. Inputs include raw data obtained from motion sensors, sound sensors, cameras, and other sources. This data is temporarily stored, formatted, and converted into an analyzable format.

[0731] Step 2:

[0732] The server inputs the formatted data into a generation AI model, which then analyzes the data. This analysis identifies the presence or absence of anomalies. The input is pre-processed data, and the output is the presence or absence of anomalies and the type of anomaly. Using the analysis results, if anomalies are found, the system identifies situations requiring further action.

[0733] Step 3:

[0734] When the server detects an anomaly, it develops a countermeasure based on a pre-configured safety plan. The input is the result of the anomaly identification, and the output is a detailed protocol for the countermeasure. The server sends this protocol to the management device, preparing to take physical control.

[0735] Step 4:

[0736] The control device performs actions such as locking doors and adjusting lighting based on instructions from the server. The input is the corresponding protocol received from the server, and the output is the actual control action. This improves security in homes or public spaces.

[0737] Step 5:

[0738] The server performs emotion analysis based on the collected sensor data. The input is formatted sensor data, and the output is the user's emotional state (e.g., relaxed, stressed). The server sends environment optimization instructions to the management device according to the emotional state.

[0739] Step 6:

[0740] The management device adjusts the environment according to the user's emotional state, following environment optimization instructions received from the server. The input is the emotional optimization instructions from the server, and the output is a change in the environment (e.g., music playback, change in lighting color).

[0741] Step 7:

[0742] The server sends notifications to the user's device regarding the situation and countermeasures. The input is the results of anomaly and sentiment analysis and the countermeasures, and the output is the content of the notification to the user. This allows the user to understand information about their environment and safety status in real time.

[0743] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0744] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0746] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0747] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0748] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0749] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0750] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0751] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0752] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0753] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0754] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0755] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0756] 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.

[0757] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0758] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0759] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0760] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0761] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0762] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0763] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0764] The following is further disclosed regarding the embodiments described above.

[0765] (Claim 1)

[0766] A means for collecting data from multiple sensor devices,

[0767] A means for analyzing the data using a generative model and detecting anomalies,

[0768] A means of planning countermeasures according to a pre-configured security plan when an anomaly is detected,

[0769] A means for controlling a home management device based on the countermeasures to lock doors and operate lights,

[0770] A means of notifying the user device of the status,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, further comprising means for monitoring the status of multiple home management devices and linking information between them.

[0774] (Claim 3)

[0775] The system according to claim 1, further comprising means for automatically sending a notification to an emergency contact after detecting an anomaly.

[0776] "Example 1"

[0777] (Claim 1)

[0778] A means of collecting information from multiple measuring devices,

[0779] A means for analyzing the information using a generative model and detecting anomalies,

[0780] A means of planning countermeasures according to a pre-set security plan when an anomaly is detected,

[0781] A means for controlling a household management device based on the said countermeasures to lock the circuit breaker and operate the light source,

[0782] A means of notifying the user's device of the status,

[0783] A means by which users can remotely check the situation through a video device when an anomaly is detected,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, further comprising means for monitoring the status of multiple home management devices and for linking information among them.

[0787] (Claim 3)

[0788] The system according to claim 1, further comprising means for automatically sending a notification to an emergency contact after detecting an anomaly.

[0789] "Application Example 1"

[0790] (Claim 1)

[0791] A means of collecting information from multiple sensor devices,

[0792] A means for analyzing the information using a generative model and identifying anomalies,

[0793] A means of formulating a response in accordance with a pre-established disaster prevention plan when an anomaly is identified,

[0794] A means for controlling the residential management device based on the said response to lock and adjust lighting,

[0795] A means of reporting the situation to a mobile device,

[0796] A means to integrate information from monitoring equipment in public facilities to enable citizen safety monitoring,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising means for monitoring the status of multiple residential management devices and linking information among them.

[0800] (Claim 3)

[0801] The system according to claim 1, further comprising means for automatically sending a report to an emergency contact after an anomaly has been identified.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means of collecting information from multiple detection devices,

[0805] A means for analyzing the information using a generation algorithm and detecting anomalies,

[0806] A means of planning countermeasures according to a pre-configured safety plan when an anomaly is detected,

[0807] A means for operating the environmental control device based on the countermeasures to lock doors and adjust lighting,

[0808] A means of inferring emotional states based on analyzed information,

[0809] A means of optimizing the environment based on the emotion prediction results,

[0810] A means of notifying the user's device of the status,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for observing the status of multiple environmental control devices and linking information among them.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for automatically sending a notification to an emergency contact after detecting an anomaly.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] The function of collecting data from multiple information gathering methods,

[0819] A function that processes the data using a generation algorithm and identifies anomalies,

[0820] When an anomaly is identified, it has a function to construct countermeasures according to a pre-configured safety plan,

[0821] Based on those countermeasures, the control device operates to control entrances and exits and adjust light sources, and

[0822] A function to notify the user's device of the status,

[0823] A function that generates optimization instructions based on sentiment analysis in urban environments and adjusts the environment of public spaces based on those instructions,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising a function for monitoring the status of multiple control devices and integrating information between them.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising a function to automatically transmit information to emergency contacts after an anomaly has been identified. [Explanation of Symbols]

[0829] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information from multiple sensor devices, A means for analyzing the information using a generative model and identifying anomalies, A means of formulating a response in accordance with a pre-established disaster prevention plan when an anomaly is identified, A means for controlling the residential management device based on the said response to lock and adjust lighting, A means of reporting the situation to a mobile device, A means to integrate information from monitoring equipment in public facilities to enable citizen safety monitoring, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the status of multiple residential management devices and linking information among them.

3. The system according to claim 1, further comprising means for automatically sending a report to an emergency contact after an anomaly has been identified.

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